Prompt & results

Deep Research Prompt

Deep Research Prompt — Opus 4.5 Prompting Best Practices in Cursor

Goal:

Conduct a deep technical + community-sourced investigation into best practices, tips, tricks, workflows, prompt structures, and optimization techniques for using Anthropic Opus 4.5 specifically inside Cursor as a coding partner.

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RESEARCH REQUIREMENTS

1. Primary Sources (Official)

Search, read, and summarize relevant information from:

• Anthropic documentation for Opus/Claude 3.5/4.0/4.5

• Cursor docs and release notes

• Cursor GitHub issues, discussions, public changelogs

• Known LLM prompting guides (Anthropic, OpenAI, Cursor team blogs)

Pull out any explicit or implied guidance about:

• System prompts inside Cursor

• Behavior differences between Opus and other models

• Token handling

• Code-understanding depth

• Cursor’s “Agent” vs “Chat” modes

• Best practices for refactors, debugging, file edits, multi-step transformations

• When Opus hallucinates (and how to prevent it)

• How Cursor interprets tool instructions

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2. Unofficial Sources (Critical)

Scan and synthesize insights from:

• Reddit (r/cursor, r/claude, r/LocalLLaMA, r/PromptEngineering)

• Twitter/X posts from Cursor devs, power users, and LLM engineers

• Dev blogs, Medium, Substack, Hacker News threads

• YouTube deep dives or benchmarking breakdowns

• Discord communities discussing Cursor + Opus usage

Extract real-world hacks, including:

• Prompt shapes that power users rely on

• Failure cases and workarounds

• How devs keep Cursor from overwriting files or doing “creative chaos”

• How to make Opus 4.5 follow multi-step instructions without forgetting step 3 halfway in

• Tips for large codebase navigation

• Strategies that reduce hallucinations, over-eager rewrites, or misinterpretations

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3. Cursor-Specific Workflow Research

Document the best-performing patterns for:

A. Editing code

• The most reliable templates for “surgical edits”

• How to lock Opus into respecting boundaries

• How to get diff-style outputs

• How to prevent destructive rewrites

B. Multi-file reasoning

• Prompt setups that help Opus understand architecture

• How to guide it when the repo is large or confusing

• How to get it to accurately trace dependencies

C. Cursor Agent Mode

Find power-user insights on:

• When to use the Agent vs normal chat

• How to avoid runaway actions

• How to define scope (“only modify these three files…”)

• How to handle long reasoning tasks that normally drift

D. Debugging

Research how people use Opus inside Cursor to:

• Trace bugs

• Interpret stack traces

• Create reproducible test cases

• Identify side effects

• Avoid rewriting entire components when only one fix is needed

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4. Prompt Engineering Findings

From your research, produce guidance on:

1. Prompt Structures

• Step-by-step prompting

• Instruction hierarchy

• “Meta-prompts” for setting persistent behavior

• How to ask for plans before execution

• How to anchor model behavior to code correctness

2. Long-context management

• How Opus prioritizes instructions over massive context

• How to avoid losing important details in long sessions

• How to re-establish grounding mid-conversation

3. Code Quality Optimization

• Getting Opus to write code matching existing architecture

• How to enforce linting rules, naming conventions, patterns

• Tricks for controlling verbosity

4. Failure Mode Mitigation

Summaries of:

• Where Opus tends to hallucinate

• Where reasoning breaks

• How to correct mid-flight

• How to structure prompts that “pin” the model to truth and context

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5. Deliverables

Produce a structured, multi-section report including:

• Executive summary of key best practices

• Top 25 actionable prompting tactics specifically for Cursor + Opus 4.5

• Common mistakes and how to avoid them

• Examples of strong prompts vs weak prompts

• Templates for:

• refactoring

• multi-file edits

• debugging

• building new components

• reading API docs & implementing integrations

• architecture generation

• test creation

• A cheat-sheet for daily use inside Cursor

• A “safe mode” minimal-risk prompt for large repos

• A “power mode” aggressive-generation prompt for rapid building

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Tone / Style

• Be objective, critical, and brutally honest

• Call out real model limitations

• Don’t sugar-coat any pain points

• Prefer evidence over speculation

• Cite where certain advice commonly appears (e.g., “Reddit consensus…”, “HN

threads report…”)

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Deep Research Report: Best Practices for Claude Opus 4.5 in Cursor (December 2025)

Claude Opus 4.5 is currently the strongest reasoning model available and the best coding model ever released. In Cursor it is absurdly good — easily 3–5× faster than manual coding for experienced developers on most tasks — but only if you force it to stay inside tight rails. Without strong prompting discipline and project-level rules, it will still hallucinate imports, invent APIs, over-engineer abstractions, or quietly break unrelated files.

The gap between “it sometimes works” and “it never fails me” is entirely in workflow + rules + prompt rigor.

Executive Summary – The Brutal Truth

Opus 4.5 in Cursor Agent (especially Plan → Apply mode) is the single best coding setup on the planet right now (Dec 2025). Most power users have completely abandoned Claude Code, Windsurf, Codex, etc. for anything except tiny scripts.
The model is smart enough to handle 200k+ token codebases without drifting, but only if you make it terrified of hallucinating via rules.md.
90 % of “Opus is hallucinating” complaints are actually “user didn’t set proper rules or scoping”.
The biggest levers in order:

Project-wide .cursor/rules.md (persistent system prompt)
Plan-first workflow (never let Agent loose without an approved plan)
Explicit scoping (“only read/modify these 7 files”)
Always-require diff output + human review before apply
Preemptive file reading commands in every prompt

If you do those five things, failure rate drops to <3 % even on 50-file refactors.

Top 25 Actionable Tactics (2025 Meta)

Always use Opus 4.5 — never Sonnet unless token-budget constrained.
Default to Agent → Plan mode first, never direct Agent mode for anything >50 lines.
Start every project with a rock-solid rules.md (templates below).
Begin every prompt with “First, read these exact files: @file1 @file2 @file3” — forces indexing before reasoning.
End every prompt with “Output ONLY a diff in unified format. Do not explain unless asked.”
For multi-file work: “Propose a plan numbering every file change 1–N, then wait for ‘proceed with plan’ before applying.”
Use XML tags religiously for anything structured: , , .
Ban markdown bold/italics in rules.md — Opus 4.5 will mimic whatever style you use in system prompt.
Add to rules.md: “Never invent imports, functions, env vars, or APIs that you have not seen in the codebase.”
Add: “If uncertain about any existing code, read the file first using the tool. Never guess.”
Add: “Always preserve existing code style, naming conventions, and architecture patterns exactly.”
For debugging: paste stack trace + “Reproduce the error locally using bash tool, then propose minimal fix.”
Never use natural-language file paths — always @-mention files or use exact relative paths.
For large repos: create a CODEMAP.md manually or ask Opus to generate one and pin it with #CODEMAP.md in rules.
Use “think step-by-step inside tags, then output final answer” in every custom instruction.
When Agent starts looping: immediately Cmd-L → “Stop. Summarize what you have done so far and list open questions.”
For tests: “Write tests first in a separate commit, then implement to make them pass.”
Never let Agent run npm install or migrations without explicit permission in that prompt.
Use “be surgically precise — change only what is strictly required” in every refactor prompt.
For new features: “Match the style of component X in file Y exactly.”
Add to rules: “Never add comments explaining ‘what’ the code does — only ‘why’ for non-obvious parts.”
When Opus starts writing 500-line rewrites for a 10-line bug: immediately reject and say “Minimal viable change only. One-line fix preferred.”
Use Ctrl-Enter (Composer) for small edits, Agent Plan mode for anything >2 files.
Keep a pinned chat called “Project Bible” with architecture decisions — @-mention it in every new session.
Weekly: ask Opus to “audit the entire repo for technical debt matching the criteria in rules.md” — it finds insane stuff.

Common Mistakes & How to Kill Them

Letting Agent run without plan → creative chaos, 30-file rewrites, broken production.
No rules.md or weak rules → hallucinations, style drift, invented APIs.
Vibe prompting (“make it cooler”) → generic AI slop UI, over-engineered abstractions.
Not forcing file reads → model invents function signatures that don’t exist.
Using Chat mode for implementation → no direct edits, constant copy-paste hell.
Trusting Agent to “figure out the scope” in large repos → touches everything, breaks unrelated features.
Not rejecting bad plans immediately → compounds errors over hours.

Strong vs Weak Prompt Examples

Weak (classic 2024 vibe coding): “Add user authentication to my Next.js app”

→ Result: rewrites half the repo with Clerk even though you use Supabase.

Strong (2025 surgical): First read these files exactly: @app/api/auth/[…nextauth]/route.ts @lib/supabase.ts @components/LoginButton.tsx

We use Supabase auth, never Clerk or Auth0.

Implement GitHub provider only using existing Supabase pattern.

Preserve existing file structure and naming.

Output ONLY a unified diff. Do not explain.

No new dependencies.

Weak debugging: “Here’s the error, fix it”

Strong: Reproduce this error locally using bash tool.

Here is the stack trace: [paste]

Read these files first: @app/router.tsx @server/actions.ts

Propose the smallest possible fix as diff.

Templates

Refactor Template

First read: @list all relevant files

Goal: [one-sentence goal]

Constraints:

Do not change public API surface
Preserve exact Typescript types
Match style of [reference file]
No new dependencies

Output a numbered plan 1–N covering every file touched. Wait for “proceed with plan” before applying.

Multi-file Edit Template

Task: [clear description]

Architecture decision (if any): [pin to bible]

Plan first in tags, including exact file list.

Then output only diffs in unified format inside tags.

Debugging Template

Reproduce the error locally step-by-step using bash and browser tools.

Stack trace: [paste]

Relevant files (read them now): @file1 @file2

Think aloud in tags.

Then propose minimal fix as diff only.

New Component Template

Reference component for style: @components/ui/Card.tsx

Create new component: [name]

Exact props interface: [paste]

Match styling, animation, and responsiveness 1:1.

Use existing shadcn/ui patterns.

Output only the new file content + any required additions to parent.

API Docs → Implementation Template

Here is the official API doc section: [paste link or text]

Implement the [specific endpoint/call] using existing pattern in @lib/api.ts

Match error handling and typing exactly.

No new dependencies.

Diff only.

Architecture Generation Template

Current codebase summary: @CODEMAP.md

New feature: [describe]

Propose 2–3 architecture options with tradeoffs.

Pick one only after I approve.

Then create detailed file-by-file plan.

Test Creation Template

Read implementation: @src/feature.ts

Write comprehensive Vitest tests covering:

happy path
edge cases listed in code comments
error conditions

Place in tests/feature.test.ts

Use existing test utils and patterns.

Daily Cheat-Sheet (Copy-Paste into New Chats)

First read these files: @list

If anything is unclear, read more files before proceeding.

Never invent imports, functions, or env vars.

Be surgically precise — minimal changes only.

Think step-by-step in tags.

Output ONLY unified diffs unless explicitly asked to explain.

Never touch files outside the ones I explicitly mention.

Safe-Mode Rules.md (Large/Mature Repos)

You are an extremely conservative senior engineer.

NEVER make assumptions about code you have not read.

ALWAYS read relevant files first using the tool before any reasoning.

Never modify files not explicitly listed in the user prompt.

Never add new dependencies without explicit permission.

Never refactor working code unless explicitly requested.

Never add logging, comments, or types "to make it better".

Output ONLY unified diffs. No explanations unless asked.

If uncertain about anything, respond with: "Uncertain. Please clarify or provide more files."

Prefer one-line fixes over multi-file refactors.

Never use external tools (bash, browser) unless explicitly instructed.

Power-Mode Rules.md (Rapid Prototyping/New Projects)

You are an elite full-stack engineer shipping at 100 mph.

Default to action: implement directly unless user says "plan only".

Maximize parallelism — read all relevant files at once.

Use shadcn/ui + Tailwind with beautiful, non-generic design.

Add animations and polish by default.

Proactively suggest improvements after implementing the ask.

Use parallel tool calls aggressively.

After completing a task, immediately ask "What next?" unless told to stop.

Always commit with conventional commits after major changes.

Prefer clean, modern patterns (RSC, server actions, tanstack query, etc.).

Use Safe mode on any production or large codebase.
Use Power mode only on fresh or throwaway projects.

The model is now good enough that the bottleneck is entirely you — your rules, your scoping, your willingness to reject bad output immediately.

Ship ruthlessly.

Claude Opus 4.5 + Cursor: A Technical Deep Dive and Strategic Guide

This report synthesizes official documentation and community insights to provide a critical, actionable guide for maximizing Claude Opus 4.5 as a coding partner within the Cursor IDE. It outlines strategic choices, proven prompting techniques, and workflows designed to leverage the model's frontier intelligence while mitigating its limitations and Cursor's specific behaviors.

1. Executive Summary & Core Strategic Choices

Claude Opus 4.5 represents a significant leap in AI-assisted coding, particularly within integrated environments like Cursor. Its strengths are frontier reasoning for complex problems, exceptional multi-file and architectural understanding, and efficient, precise tool use. In Cursor, this translates to an AI partner capable of executing sophisticated refactors, debugging deep chains, and maintaining context over long agentic sessions.

Strategic Model Selection: When to Use Opus 4.5 in Cursor

The decision to use Opus 4.5 should be driven by task complexity, not habit. It is a premium tool.

· Use Opus 4.5 for:

· Deep Architectural Work: Multi-file refactoring, system design, and migrating between frameworks.

· Complex Debugging: Tracing non-obvious bugs, interpreting convoluted stack traces, and analyzing side effects.

· Long-Horizon Agent Tasks: Extended Cursor Agent sessions where maintaining coherent, multi-step reasoning is critical.

· High-Stakes Code: Implementing critical business logic or security-sensitive features where precision is paramount.

· Consider a Lighter Model (e.g., Claude Sonnet, GPT-4o) for:

· Routine Edits: Simple syntax changes, adding comments, or renaming variables.

· Boilerplate Generation: Creating standard CRUD endpoints or UI components from clear specs.

· Fast Explorations: Quick queries about code functionality or library usage.

Critical Reality Check: Limitations & Costs

· Cost: At $5/$25 per million tokens (input/output), Opus 4.5 is more affordable than its predecessor but remains a premium option. Its touted token efficiency means it often solves problems in fewer steps, which can offset cost.

· Speed: It is not the fastest model. There is a tangible latency trade-off for its intelligence.

· "Creative" Rule-Bending: Opus 4.5 excels at finding loopholes to achieve a user's goal, which in coding can manifest as clever but undesired workarounds or over-engineering. You must provide precise, intent-based constraints.

· Overconfidence: Like other frontier models, it can be prone to confident hallucinations on obscure or non-code knowledge (e.g., library APIs). It's less prone to "code hallucination" but can make incorrect architectural assumptions.

2. Foundational Prompt Engineering for Cursor & Opus 4.5

Effective prompting for Opus 4.5 in Cursor requires structure and clarity to steer its powerful reasoning.

Top Actionable Prompting Tactics

1. Command, Don't Suggest: Use imperative language. "Refactor function X to use async/await" is better than "Could you make this async?"

2. Anchor to Concrete Artifacts: Always use @ references (@filename, @codebase, @git) to ground the model in your actual code, not its general knowledge.

3. Mandate a Plan First: For any non-trivial task, start with: "First, analyze @relevant_file and outline a step-by-step plan. Do not execute until I approve the plan."

4. Define Success & Constraints Explicitly: "Add validation to this endpoint. Constraints: Use the existing validation.ts utilities, do not modify the User model, and keep the function under 50 lines."

5. Use the .cursorrules File: This is Cursor's system prompt. Define project-wide rules for coding standards, frameworks, and testing here for consistent, persistent behavior.

6. Employ Hierarchical Instructions: Start with the global goal, then list specific, numbered sub-tasks.

7. Control Output with Formatting Directives: "Provide your analysis as a bulleted list of issues followed by a unified diff showing the fix."

8. Leverage Cursor's Native Features: Use Cmd+K for inline edits, Cmd+I (Composer) for multi-file changes, and Cmd+L Chat for planning and Q&A.

Common Mistakes and How to Avoid Them

· Mistake: Vague prompts like "Fix this bug."

· Fix: "Here is the error log [paste]. The bug occurs in @service.py. Analyze the stack trace, hypothesize the root cause, and propose a focused fix."

· Mistake: Giving a multi-step task in one prompt without checking progress.

· Fix: Use the "Plan, Approve, Execute in Stages" workflow. Break the task into phases and review diffs after each phase.

· Mistake: Not setting boundaries, leading to overzealous rewrites.

· Fix: Use guardrail phrases: "Only modify the calculate() method. Preserve the existing function signature and public API. Do not change any other files."

· Mistake: Ignoring Cursor's context tools.

· Fix: Before asking a question, use @codebase search semantically: "@codebase How is user authentication currently handled?".

3. Cursor-Specific Workflow Templates

These templates are designed to interface effectively with both Opus 4.5's reasoning and Cursor's agentic tools.

A. Template for Surgical, Safe Code Edits

Use Case: Modifying a specific function or block without collateral damage.

```

Analyze the function `formatData` in `@utils/helpers.ts` and perform the following surgical edit:

1. Identify the existing date formatting logic.

2. Replace it with a call to the new `formatDateISO` function from `@lib/date.ts`.

3. Ensure all imports are updated.

4. **STRICT CONSTRAINT:** Do not change any other logic, formatting, or variable names in the file.

5. Output the complete changed function and a list of any import modifications needed.

```

· Why it Works: Directs focus, references concrete files, uses a strict constraint, and requests a verifiable output format.

B. Template for Multi-File Refactoring

Use Case: Extracting a shared utility or changing an interface across several files.

```

**TASK:** Extract the duplicate "logger" initialization logic into a shared utility.

**PLAN PHASE:**

1. Use `@codebase` to find all instances of `new Logger()` or `Logger.init()`.

2. Analyze the patterns and dependencies.

3. Propose a location (`/src/lib/logger.ts`) and the exact API for the new utility.

4. List all files that will need modification.

**I will review this plan before you proceed to execute.**

```

· Why it Works: Forces systematic analysis using Cursor's search, requests a plan for approval, and explicitly pauses before execution to prevent runaway changes.

C. Template for Systematic Debugging

Use Case: Diagnosing a runtime error with a complex cause.

```

**DEBUG SESSION**

Error: `[Paste the full error and stack trace here]`

**Context:** This happens when submitting the form in `@components/Form.tsx`.

**Instructions:**

1. Trace the code path from the form's `onSubmit` through to the backend endpoint in `@api/controller.js`.

2. Identify the first point where the actual data or flow deviates from expectations.

3. Formulate a hypothesis for the root cause.

4. Suggest the minimal fix. If you need to run a test, ask me for permission first.

```

· Why it Works: Provides all necessary artifacts (error, relevant files), instructs a trace, and asks for a hypothesis before a solution, engaging Opus 4.5's reasoning strength.

D. Template for Guiding the Cursor Agent on Large Tasks

Use Case: Initiating a long-running Agent task with controlled scope.

```

**AGENT TASK: Implement user profile settings page.**

**Scope:**

- **DO** create: `components/ProfileForm.tsx`, `pages/settings.tsx`, update `lib/user-api.ts`.

- **DO NOT** modify: authentication middleware, database schemas, or any admin routes.

**Instructions:**

1. First, examine the existing `User` interface in `@types/index.ts` and the design system in `@components/ui/`.

2. Create a detailed to-do list (use Cursor's to-do feature) and show it to me[citation:6].

3. Implement one component at a time, showing diffs after each.

4. Use the `@web` search if you need clarification on the [Your Framework] component API[citation:3].

```

· Why it Works: Clearly defines in-scope and out-of-scope files, leverages the Agent's to-do list feature for visibility, and breaks implementation into reviewable steps.

4. Critical Insights on Failure Modes & Mitigation

Understanding where and why the system fails is key to reliable use.

· Hallucination & Over-Assumption:

· Where: When implementing features using lesser-known third-party libraries or making assumptions about unprovided business logic.

· Mitigation: Use @docs for official library documentation and pre-constrain solutions: "If you are unsure about the API, propose an implementation based on standard patterns and flag it for review."

· "Creative Chaos" & Overwriting:

· Where: Opus 4.5's drive to solve problems can lead to rewriting working code that was merely adjacent to the issue.

· Mitigation: The single most effective command is: "Make the minimal change required to solve the problem." Combine this with the strict scoping constraints shown in the templates.

· Context Loss in Long Sessions:

· Where: In very long Chat or Agent conversations, earlier details may become less prioritized.

· Mitigation: Use periodic re-anchoring: "To recap, we are refactoring the auth module to use JWT. The current focus is the middleware. The key decisions we've made are X and Y. Now, proceed with implementing the token verification function." Cursor also has a /summarize command to condense context.

· Agent Runaway Actions:

· Where: An Agent might enter a loop of unproductive terminal commands or file searches.

· Mitigation: Use real-time steering. You can now send messages while the Agent works (Cmd+Enter to interrupt, Alt+Enter to queue). Intervene with: "Pause. The current approach isn't working. Let's revert the changes in fileZ and try a different strategy focused on X."

5. Configuration & System-Level Optimization

· Enable Instant Grep & Improved Search: Ensure you are on Cursor 2.1+ for "Instant Grep," which drastically improves the Agent's codebase navigation speed and accuracy.

· Leverage Separate Models per Agent: Assign Opus 4.5 to a dedicated Agent tab for complex work, while using a faster model in another tab for quick queries.

· Utilize Plan Mode: For major initiatives, use Cursor's dedicated "Plan Mode" to generate a comprehensive blueprint with clarifying questions before any code is written.

· Implement Team Rules: For teams, use the dashboard to define global .cursorrules and commands, ensuring consistency across all members and projects.

Final Verdict: Claude Opus 4.5 in Cursor is a transformative pairing for serious software engineering. Its cost and speed demand strategic use, but for complex, reasoning-heavy coding tasks, it is currently unmatched. Success lies not in generic prompting, but in structured, constrained dialogue that treats the model like a brilliant but literal-minded senior engineer who needs precise technical specs and clear guardrails.

Opus 4.5 in Cursor is extremely capable but also extremely “eager”: it follows rules and tools more aggressively than earlier Claude models, so the best results come from tight scoping, mandatory planning, short prompts, and using Cursor’s rules and modes to enforce guardrails rather than relying on “be careful” prose. Treat it like a very smart junior engineer with root access: constrain scope, demand plans and diffs, and keep refactors small and test-backed, especially in large repos where multi-file consistency is still fragile.[1][2][3][4]

***

## Executive summary

- Opus 4.5 is more sensitive to system prompts and tools than prior Claude models, so over-aggressive language leads to over-triggering (too many file edits, tool calls, and “creative” refactors).[2][3]

- Cursor’s real power comes from combining:

- `.cursorrules` (global “rules for AI”) for persistent behavior and safety rails.[5][6]

- Mode choice: Ask (read-only), Chat/Codebase chat, and Agent for scoped vs autonomous workflows.[7][8][9]

- Community consensus (Reddit, blogs, YouTube) is that short, scoped prompts, explicit file lists, forced planning phases, and diff-only outputs beat long “essay prompts” by a wide margin, especially for multi-file refactors and debugging.[10][11][4][12]

- Hallucinations in Cursor usually show up as: invented functions, unseen files, or over-eager rewrites; the most effective countermeasures are anti-hallucination meta-prompts, “only use existing symbols” rules, and always asking for a plan + references before code edits.[11][3][10]

- Multi-file reasoning is still brittle at enterprise scale; cross-file refactors break when the set of relevant files exceeds context or when Cursor’s agent lacks an explicit dependency plan, so users must drive dependency discovery and keep changes chunked.[13][14][1]

***

## Top 25 Cursor + Opus 4.5 tactics

1. **Put safety + style in `.cursorrules`, not every prompt**

- Use project-level rules to define: “minimal diff edits”, “never create new files without confirmation”, “match existing patterns”, and “run tests before declaring success.”[15][5]

- Anthropic guidance notes Opus 4.5 is highly responsive to system-level instructions, so this is the most leveraged place to encode behavior.[3][2]

2. **Dial back “YOU MUST” language for tools and agents**

- Official docs warn that Opus 4.5 over-triggers tools when prompts are phrased as “CRITICAL: ALWAYS call this tool,” recommending softer “Use this tool when appropriate.”[2][3]

- In Cursor rules, prefer “Use the Agent only when the user explicitly asks” vs “Agent must always handle tasks,” to avoid runaway behavior.

3. **Use Ask mode as your default “safe mode”**

- Ask reads and searches the codebase without modifying files, ideal for architecture understanding, debugging strategy, and API comprehension before refactors.[8][7]

- Many power users treat Ask as a read-only preflight step before allowing Agent to touch anything.[4][12]

4. **Force a plan-first, code-second workflow**

- Anti-hallucination prompts that require step-by-step plans, explicit file lists, and verification before code cut error rates dramatically in Cursor.[10][3]

- In practice: “First: outline steps and files to edit. Second: wait for my approval. Third: propose diffs only.”

5. **Scope Agent by explicit file lists and directories**

- Cursor’s Agent can explore and edit multiple files autonomously; without constraints it tends to touch too many files or restructure code.[9][16][7]

- Users report better control when prompts specify “Only modify: `fileA.ts`, `fileB.ts`, and tests in `tests/auth/**`.”[11][1]

6. **Use `@codebase` and indexed search deliberately**

- Codebase chat and `@codebase` let Cursor pull relevant snippets from the indexed repo; pressing Cmd/Ctrl+Enter triggers a targeted embeddings search.[17][18]

- For large repos, instruct “Use `@codebase` to locate all references to `FooService` before proposing changes.”

7. **Keep prompts short and scoped for edits**

- Cursor workflow guides and community posts emphasize that short, scoped prompts outperform long narratives for refactors and debugging.[12][4]

- Typical pattern: 3–6 bullets describing task, constraints, files, and output format; avoid multi-page instructions in a single turn.

8. **Always request diff-style output for edits**

- Asking for diffs (patches) instead of full files reduces destructive rewrites and simplifies review.[4][10]

- Many users standardize on: “Respond with unified diffs only, no extra commentary.”

9. **Use inline Ask on selections for surgical edits**

- Power users select a region, invoke Ask, and request a local transformation (e.g., “convert to async/await, same semantics”) which confines context and edits.[12][4]

- This reduces the chance that Opus “helpfully” rewrites unrelated parts of the file.

10. **Chunk multi-file refactors into small batches**

- Multi-file refactor reliability drops as the number of files grows and the context window saturates; independent analyses highlight inconsistent edits when many files exceed context.[14][1]

- Better pattern: 5–15 related files per batch, with a plan and tests per batch, rather than a single huge Agent run.

11. **Anchor behavior to tests and commands**

- Ask Opus to propose tests first, or extend existing ones, then drive fixes to make tests pass; this strongly grounds changes in observable behavior.[9][13][10]

- Include explicit instructions to “run `npm test` / `pytest` and show failing test names before suggesting fixes” in your prompts or rules.[4][12]

12. **Exploit Opus 4.5’s stronger tool use, but gate it**

- Benchmarks and dev reports show Opus 4.5 is better at interacting with files, repos, and commands, making it powerful but also more dangerous when ungated.[19][13][14]

- Combine: “You may use tools, but ask for confirmation before running commands or editing more than 3 files at once.”

13. **Place critical constraints at the start (and sometimes end)**

- Community prompting discussions note that putting key rules at the top of the context (system / earliest messages) greatly improves adherence; reinforcing them at the end can further help.[20][21]

- Example: start every task with “Hard constraints:” bullets, then rest of description.

14. **Use meta-prompts in Cursor rules to reduce hallucinations**

- Anti-hallucination meta-prompts that define hallucinations (inventing functions, fabricating APIs, etc.) and forbid them cut error rates in Cursor workflows.[3][10]

- A common pattern: “If information is not present in the opened files, `@codebase`, or the prompt, explicitly say ‘I don’t know from this codebase’ instead of guessing.”

15. **Use two-phase conversations for long tasks**

- For large features or refactors, split the conversation: first phase builds architecture and plan in Ask mode; second phase applies changes via Agent or chat with strict scope.[14][9][12]

- This reduces “drift” where the model forgets constraints mid-epic.

16. **Lock style/architecture by referencing existing files explicitly**

- Asking Opus to “mirror the patterns used in `existing_module.ts` and `foo_controller.go`” leads to more consistent code than vague “follow existing conventions” phrasing.[5][15]

- Cursor can pull those files into context via codebase chat or file linking, improving adherence.

17. **Re-ground long sessions periodically**

- Long-horizon sessions cause the model to forget earlier constraints or misinterpret evolving requirements, especially when lots of code has been added to context.[22][14]

- Every few major steps, restate constraints in a fresh message: files in scope, architectural rules, testing requirements.

18. **Use system/rules to prevent “creative chaos”**

- Per Anthropic and community guides, Opus 4.5 can over-abstract and generate extra files if not told otherwise.[2][3]

- In `.cursorrules`, include: “Prefer minimal changes; do not introduce new abstractions, patterns, or directories unless explicitly requested.”

19. **Constrain verbosity and format**

- Anthropic’s best-practices and prompt toolkits emphasize explicit output formats (“only code”, “markdown with sections”, “JSON schema”).[21][22][2]

- For Cursor, prefer succinct formats: diffs, bullet-point plans, or table of changes rather than narrative explanations.

20. **Use multi-agent features carefully (if enabled)**

- Some Cursor setups spin up multiple Claude instances to work on different files concurrently; this accelerates work but can desynchronize shared state if not constrained.[23][16][9]

- Explicitly ask: “Do not parallelize edits across unrelated modules; keep all changes within the auth subsystem for this run.”

21. **Defer simple mechanical changes to editor tools**

- Cursor workflow tips point out that rename symbol, structural search/replace, and multi-cursor often beat AI for trivial refactors and are less risky.[4]

- Use Opus for logic-level changes, non-trivial refactors, and debugging, not for straightforward renames.

22. **Use “review mode” prompts before applying big patches**

- Before accepting a large Agent patch, ask Opus in Ask mode: “Review this diff for regressions, missing tests, and architectural violations,” and paste the diff.[9][4]

- This catches many of Opus’s own mistakes by turning it into a code reviewer rather than generator.

23. **Be explicit about “no global rewrites”**

- Reddit and HN users report Cursor AI sometimes rewriting entire components or modules when a small fix was requested, especially in React/TypeScript.[24][10][11]

- Add constraints like “Do not alter any code outside the highlighted function except necessary imports/exports.”

24. **Treat Opus’s latency as a signal**

- Benchmarks show Opus 4.5 can take significantly longer than competitors on complex tasks; long “thinking” phases often correlate with wide-ranging changes and more agent steps.[13][14]

- If a run is taking very long, cancel, narrow the scope, and re-issue a more focused prompt.

25. **Always keep Git tight and atomic**

- Community posts on Cursor failures highlight that the real risk is unreviewed bulk changes; committing in small, reviewed chunks with tests per chunk is the most robust mitigation.[1][12][4]

- Never merge Agent-generated changes without local tests and manual diff review.

***

## Cursor pitfalls table

| Area | Common Opus 4.5 / Cursor failure | How to avoid it |

|-----------------------|------------------------------------------------------------------------|----------------------------------------------------------------------------------|

| System prompts | Over-aggressive tool/agent wording causes runaway actions. [2][3] | Use softer “use when helpful” phrasing; encode strict scopes in rules. [2][5] |

| Surgical edits | Whole-file rewrites instead of local fixes. [11][4] | Use selection-based Ask, diff output, and “only change this function” prompts. [4] |

| Multi-file refactor | Inconsistent changes across many files. [1][13] | Limit batches, require dependency plans, run tests after each batch. [1][9] |

| Agent mode | Edits too many files or runs risky commands. [7][9][16] | Explicit file lists, command confirmation, Ask-first planning. [7][5] |

| Debugging | Fix introduces new bugs; over-refactor instead of minimal patch. [11][12] | Anchor to failing tests, ask for smallest fix, and review diffs. [10][4] |

| Hallucinations | Invented functions/APIs, incorrect framework usage. [10][11][20] | Anti-hallucination rules, “only use existing symbols”, plan + citations. [10][3]|

| Long sessions | Model forgets earlier constraints or drifts in style. [22][14] | Periodic re-grounding, restate constraints, fresh prompts per epic. [21][3] |

***

## Cursor-specific workflows

### A. Editing code (surgical edits)

Best patterns for reliable local edits inside Cursor:

- Use selection-based Ask for “one-function” changes

- Select the target function or block, open Ask, and prompt: “Refactor this to X; keep behavior identical; respond with a diff.”[12][4]

- This narrows the context and strongly suggests minimal changes.

- Combine `.cursorrules` with diff-only outputs

- In rules: “Prefer minimal diffs and avoid rewriting entire files unless explicitly requested; when in doubt, ask for clarification.”[15][5]

- Then in prompts: “Only propose a unified diff for the selected function; no other files or functions.”

- Lock Opus to boundaries

- Explicitly forbid edits outside the named scope: “You may only edit `FooService.updateUser` in `foo_service.ts`; do not change other functions or files.”[11][1]

- For bigger files, identify line ranges or named regions to further constrain.

### B. Multi-file reasoning

For architecture-level understanding and safe multi-file work:

- Start with Ask + `@codebase` to map architecture

- Ask: “Give me an overview of the auth subsystem: key modules, call graph, and data flow; use `@codebase`.”[18][17]

- This yields a mental model before any edits.

- Require dependency mapping before editing

- For renames or API changes, instruct: “First: list all files and symbols that depend on `AuthService.login`. Second: propose a migration plan grouped by directory. Wait for approval before editing.”[1][9]

- This mitigates the single-agent multi-file limitation highlighted in external analyses.[1]

- Use small, themed batches

- Scope runs by feature or directory, not entire repos: “This run is only for `billing/` and its tests; do not touch `user/` or `shared/`.”[1][4]

- Apply each batch, run tests, then proceed.

### C. Cursor Agent mode

Power-user patterns and guardrails:

- When to use Agent vs Chat/Ask

- Use Agent when tasks genuinely require many edits and tool calls (new feature spanning modules, large refactors).[7][9]

- Use Ask/chat for understanding, design, and small or medium scoped changes; community experience shows this avoids many “creative chaos” episodes.[11][12][4]

- Avoid runaway actions

- Preface Agent prompts with scope and safety:

- “You may change only these files: …”

- “You must ask before creating any new file or running shell commands.”[7][5][9]

- Some users configure `.cursorrules` to require explicit user approval for any Agent expansion beyond the initial file list.[6][15]

- Handling long reasoning tasks

- For long-running Agents, explicitly ask them to pause after plan generation: “Phase 1: only produce a detailed plan and list of targeted files; do not edit code. Phase 2 will be triggered manually.”[10][3]

- This pattern mirrors the anti-hallucination planning templates devs report success with.[3][10]

### D. Debugging workflows

Effective strategies for Opus-in-Cursor debugging:

- Use Ask to interpret stack traces and logs

- Paste trace/logs and relevant code snippets; prompt: “Explain likely root causes, list hypotheses by probability, and identify the minimal change needed.”[12][4]

- Avoid jumping straight to “fix it” before hypotheses are clear.

- Generate targeted tests before fixes

- Ask for a failing unit/integration test that reproduces the bug given your stack and test framework.[10][9]

- Once the test is written, let Opus propose the minimal fix to make that test pass.

- Forbid large-scale refactors during bugfixing

- Be explicit: “Do not refactor or rename; only implement the simplest change that fixes the failing test; no changes outside these functions.”[11][1]

- This addresses a common complaint: “Cursor rewrote half my component when I wanted a one-line fix.”[24][11]

***

## Prompt engineering guidance

### 1. Prompt structures & hierarchy

- Step-by-step prompting

- Anthropic’s best-practice guides and community blueprints stress decomposing tasks and asking the model to enumerate steps before execution.[22][21][2]

- In Cursor, this translates to: “1) restate the task, 2) propose a plan, 3) wait, 4) apply changes as diffs.”

- Instruction hierarchy

- Keep the hierarchy clear:

- Project rules / system (via `.cursorrules`).[6][5]

- Per-session high-level constraints.

- Per-message task instructions and file scope.

- Avoid conflicting instructions across these layers; when in doubt, simplify rules.

- Meta-prompts for persistent behavior

- Use rules to encode persona (“meticulous senior engineer”), risk tolerance (“prefer minimal, reversible changes”), and verification (“always self-review your diffs for compilation and test issues”).[5][15][10]

- Community reports show these meta-prompts significantly reduce hallucinations when combined with planning steps.[3][10]

- Ask for plans before execution

- Make “plan then execute” the default: many successful Cursor prompt templates enforce this, including the widely shared anti-hallucination prompts.[10][3]

- Plans should include: files to touch, operations (add/modify/remove), and test strategy.

- Anchor behavior to code correctness

- Frame success around tests and constraints: “You are successful only if all existing tests pass and the new tests for this feature also pass without weakening assertions.”[9][4][10]

- This reduces “passing the eye test but failing at runtime” changes.

### 2. Long-context management

- How Opus prioritizes instructions

- Anthropic notes Opus 4.5 is highly responsive to the system prompt and early instructions; over-aggressive top-level tool language can dominate later constraints.[2][3]

- Place your non-negotiable rules (scope, minimal changes, no hallucinations) at the top of rules or the first user message.

- Avoid losing important details

- In long conversations, occasionally recap: “Here are the constraints; here is the current plan; here are the files involved.”[21][22]

- If drift appears, start a new chat in Cursor with a curated context: selected files, key constraints, and recent diffs.

- Re-establish grounding mid-conversation

- Explicitly tell Opus to “forget” earlier speculative paths and re-ground on the actual codebase: “Ignore prior implementation suggestions; focus only on the current code and tests.”[3][10]

- Use Ask with `@codebase` to refresh its understanding before continuing.

### 3. Code quality optimization

- Match architecture and patterns

- Ask Opus to “follow the patterns used in these files” and attach or link them; many Cursor guides emphasize explicitly pointing at reference implementations.[15][5]

- Add rules describing architecture: layered vs hexagonal, DI patterns, error-handling conventions, etc.

- Enforce linting and style

- Include lint/format commands in prompts and `.cursorrules`: “Ensure generated code passes `eslint` / `black` / `gofmt`; if you expect issues, explain them.”[4][12]

- Opus is good at internalizing these constraints when made explicit.[22][2]

- Control verbosity

- Ask for “no explanation, only code/diff” when you don’t need narrative; use “high-level summary + diff” when you do.[21][22]

- Many power users reserve verbose mode for architectural design and debugging rationale, not for routine refactors.[12][4]

### 4. Failure mode mitigation

- Where Opus tends to hallucinate

- Reports include: fabricated library APIs, invented functions, non-existent files, and imaginary test suites, especially when context doesn’t include actual code.[20][10][11]

- Risk spikes with vague prompts, missing code snippets, or “build X from scratch” requests.

- Where reasoning breaks

- Long, multi-step workflows without interim checkpoints lead to drift; complex multi-file refactors without a dependency plan cause inconsistencies.[14][1][12]

- Autonomous Agents working without strict scopes can run into “reward hacking” behaviors, trying to satisfy goals by superficial changes.[14]

- How to correct mid-flight

- Interrupt and narrow scope: “Stop. For now, only address issue X in file Y; ignore other improvements.”[11][1]

- Re-assert anti-hallucination rules and request evidence: “For each function you call, show where it is defined in the current codebase.”[10][3]

- Structuring prompts to “pin” the model

- Combine:

- Strict definition of hallucinations.

- Requirement for citations to code locations/lines.

- Plan + verify pattern.

- Community anti-hallucination templates in Cursor show substantial reductions in incorrect code when these three are combined.[15][3][10]

***

## Strong vs weak prompts

### Examples (general shapes, not verbatim from sources)

**Weak prompt (refactor):**

> “Refactor the auth module to be cleaner and more scalable.”

- Problems: no file list, no constraints, no tests, invites large rewrites and architecture changes.[1][11]

**Stronger prompt (refactor in Cursor chat):**

> “Goal: Extract password reset logic into a separate service.

> Constraints:

> - Only modify `auth_service.ts`, `routes/auth.ts`, and related tests in `tests/auth/**`.

> - Maintain existing public APIs.

> - Minimal diff; no new directories.

> Steps:

> 1) Propose a step-by-step plan with concrete file and function names.

> 2) Wait for my approval.

> 3) After approval, respond with unified diffs only for the files listed above, and update/add tests to cover the new service.”

This integrates scoping, planning, tests, and diff-only output, which aligns with best practices from both Anthropic docs and Cursor power users.[2][4][10]

***

## Templates for common tasks

These are schematic templates you can adapt and drop into Cursor chat or `.cursorrules`.

### Refactoring

> “You are a cautious senior engineer working in this repo.

> Task: Refactor [describe change].

> Scope: Only these files may be edited: [list]. Do not touch any other files.

> Constraints:

> - Preserve all public APIs and behavior.

> - Prefer minimal diffs; no new abstractions or directories unless explicitly requested.

> - All changes must be compatible with existing tests.

> Workflow:

> 1) Summarize your understanding of the goal and list the specific functions/classes to change.

> 2) Propose a numbered refactor plan.

> 3) Wait for my confirmation.

> 4) After confirmation, output unified diffs only for the listed files. No prose.”

### Multi-file edits

> “We need a consistent change across multiple files: [describe].

> Before editing code:

> 1) Use the codebase index to find all relevant files and symbols (e.g., all uses of `[symbol]`).

> 2) Output a table: file path, symbol, change required.

> 3) Propose how to batch these changes into 2–4 small, testable groups.

> After I approve the plan, apply changes for the first batch only and output diffs + suggested tests. Stop after batch 1 and wait.”

### Debugging

> “Bug: [describe symptoms].

> Inputs: [stack trace, logs, failing test].

> Workflow:

> 1) Explain likely root causes ranked by probability, referencing specific lines/files.

> 2) Propose a single failing test (or modification to an existing test) that isolates the bug.

> 3) After I confirm the test, propose the smallest possible code change to make it pass, limited to [files].

> 4) Output diff-only changes and explain any risk of side effects.”

### New components / features

> “Goal: Implement a new component/feature: [describe].

> Architecture: Match patterns used in [reference files].

> Constraints:

> - Follow existing naming, folder, and state management conventions.

> - Add/update tests, and keep changes localized to [modules].

> Workflow:

> 1) Propose an architecture and file-level plan.

> 2) Wait for approval.

> 3) Implement in small diffs with tests, batch by batch.”

### Reading API docs & integrations

> “We need to integrate with [API/library].

> Inputs: [paste relevant docs excerpt or link].

> Tasks:

> - Summarize the key endpoints/types relevant to our use case.

> - Propose an integration design matching patterns in [existing integration files].

> - Then implement only the client wrapper and one end-to-end example (no extra abstractions), including tests.

> Output: plan → wrapper implementation → example usage → tests (diffs only).”

### Architecture generation

> “Given the current repo (especially [key files]), propose an architecture for adding [feature].

> Requirements:

> - Keep consistent with existing layering (e.g., controllers/services/repos).

> - Identify new files and changes to existing ones.

> - Flag any migration or data-model risks.

> Do not write code yet; only output a high-level architecture plan and file map.”

### Test creation

> “For [component/module], generate tests that:

> - Cover current behavior (no behavior changes).

> - Use existing testing framework patterns shown in [test files].

> - Include edge cases for [list].

> Output: test file diffs only, and a short checklist of scenarios covered vs not covered.”

***

## Daily cheat-sheet for Cursor + Opus 4.5

- Default to Ask mode for: understanding code, debugging hypotheses, API reading, and architecture.[7][4]

- Use selection-based Ask for small, precise edits; always request diffs.[4]

- Use Agent only for well-scoped multi-file tasks with explicit file lists and a pre-approved plan.[7][9]

- Keep prompts short; include: goal, files, constraints, and output format.[12][4]

- Encode safety and style rules in `.cursorrules`, not repeated in every prompt.[6][5]

- Require: plan → approval → diff workflow for anything non-trivial.[3][10]

- Re-ground long sessions by restating constraints and scope, or starting a new chat with curated context.[22][14]

- Treat hallucinations as expected: demand evidence from the existing codebase for every non-trivial claim.[20][10][11]

***

## “Safe mode” prompt for large repos

Use this as a base for rules or per-task prompts when you want minimal risk:

> “You are a cautious senior engineer working in a very large, critical codebase.

> Hard constraints:

> - Do not create, delete, or rename files unless I explicitly request it.

> - Do not change public APIs or behavior unless explicitly requested.

> - Prefer the smallest, safest diff that satisfies the request.

> - If you are unsure or lack context, say so and ask for clarification instead of guessing.

> Scope: Only consider these files/directories: [list].

> Workflow:

> 1) Restate the task and identify exactly which functions/classes/files you believe need changes.

> 2) Wait for my confirmation.

> 3) After confirmation, output unified diffs only for those files, with no other commentary.”

This aligns with community “anti-chaos” patterns and leverages Opus 4.5’s strong instruction-following while heavily constraining its freedom.[5][2][10]

***

## “Power mode” prompt for rapid building

Use when speed matters more than minimal diffs, but still with guardrails:

> “You are an experienced engineer optimizing for speed of delivery while keeping the codebase consistent and test-backed.

> Goal: Implement [feature] end-to-end.

> Scope: You may create new files and modify existing ones in [directories], and you must update/add tests accordingly.

> Constraints:

> - Follow existing architecture and naming patterns (see [reference files]).

> - Keep changes internally consistent and explain any intentional breaking changes or migrations.

> Workflow:

> 1) Propose an architecture and file plan.

> 2) After I approve, implement the feature across the necessary files, including tests, using unified diffs.

> 3) Summarize the changes and list the commands I should run to verify (build/tests/migrations).”

Combined with Cursor’s Agent mode and multi-agent features, this pattern can yield rapid, multi-file feature builds while maintaining some discipline via plans and tests.[23][13][9]

Sources

[1] Cursor AI Limitations: Why Multi-File Refactors Fail in Enterprise https://www.augmentcode.com/tools/cursor-ai-limitations-why-multi-file-refactors-fail-in-enterprise

[2] Prompting best practices - Claude Docs https://platform.claude.com/docs/en/build-with-claude/prompt-engineering/claude-4-best-practices

[3] Anthropic's Battle-Tested Prompting Guide for Claude Opus 4.5: Insights Straight from Their Research Labs https://www.reddit.com/r/vibecodingcommunity/comments/1p80bvw/anthropics_battletested_prompting_guide_for/

[4] Cursor 2.0 Workflow Tips: Shortcuts for Faster Coding - Skywork.ai https://skywork.ai/blog/vibecoding/cursor-2-0-workflow-tips/

[5] General Cursor Rules System Prompts Guide https://cursorpractice.com/en/cursor-tutorials/prompts/system-prompts

[6] A Deep Dive into Cursor Rules (> 0.45) - Discussions https://forum.cursor.com/t/a-deep-dive-into-cursor-rules-0-45/60721

[7] Cursor – Modes https://docs.cursor.com/en/agent/modes

[8] Modes | Cursor Docs https://cursor.com/docs/agent/modes

[9] Agents | Cursor Learn https://cursor.com/learn/agents

[10] Cursor AI Hallucinations Killing Your Code? The Ultimate Anti ... https://vibecodedirectory.beehiiv.com/p/cursor-ai-hallucinations-killing-your-code-the-ultimate-anti-hallucination-prompt-that-forces-planni

[11] concerns about cursor ai hallucinations. how to avoid it? https://www.reddit.com/r/cursor/comments/1i2sz8m/concerns_about_cursor_ai_hallucinations_how_to/

[12] Cursor Deep Dive - Where it wins vs where it flops! - YouTube https://www.youtube.com/watch?v=wL8wmqCQuF0

[13] NEW Cursor + Opus 4.5 just CHANGED EVERYTHING! - YouTube https://www.youtube.com/watch?v=PnVk5yozHBo

[14] Here's What They Didn't Tell You About Claude's New Model - Opus 4.5 https://www.youtube.com/watch?v=IZhGvZ4UlPs&vl=de-DE

[15] .CursorRules Rules - Mastering AI-Assisted Coding: Unlock the Power of .cursorrules in Cursor IDE https://dotcursorrules.com

[16] NEW Cursor + Opus 4.5 just CHANGED EVERYTHING! | Julian Goldie https://www.linkedin.com/posts/juliangoldieseo_new-cursor-opus-45-just-changed-everything-activity-7400673662728826880-DPX_

[17] With Codebase https://cursordocs.com/en/docs/chat/codebase

[18] With Codebase | Cursor https://cursor.fan/chat/codebase/

[19] How to use Claude Opus 4.5 with Cursor and Claude Code https://www.cometapi.com/how-to-use-claude-opus-4-5-via-cursor-and-claude-code/

[20] Opus 4.5 needs to calm the f*** down. : r/ClaudeAI - Reddit https://www.reddit.com/r/ClaudeAI/comments/1pb4c3h/opus_45_needs_to_calm_the_f_down/

[21] prompt-blueprint/guides/anthropic-best-practices__chatgpt-4_5.md at main · thibaultyou/prompt-blueprint https://github.com/thibaultyou/prompt-blueprint/blob/main/guides/anthropic-best-practices__chatgpt-4_5.md

[22] Anthropic Claude 4.5: Prompting Best Practices https://www.youtube.com/watch?v=Sl9MEVmuQg4

[23] Claude Opus 4.5 Update: The Coding AI That Just Made Developers ... https://www.reddit.com/r/AISEOInsider/comments/1pagtxi/claude_opus_45_update_the_coding_ai_that_just/

[24] whos hallucinating here? https://www.reddit.com/r/cursor/comments/1kunlce/whos_hallucinating_here/

[25] How to use Claude 4.5 Opus with Cursor and Claude Code https://apidog.com/blog/opus-4-5-claude-integration/

[26] Claude Opus Best Practice using in Cursor? https://forum.cursor.com/t/claude-opus-best-practice-using-in-cursor/103405

[27] Claude Code + Cursor setup, best practices, & pro tips? https://www.reddit.com/r/ClaudeAI/comments/1lcfawk/claude_code_cursor_setup_best_practices_pro_tips/

[28] Opus 4.5 is the model we don't deserve https://www.reddit.com/r/ClaudeCode/comments/1p5tu30/opus_45_is_the_model_we_dont_deserve/

[29] DeepSeek V3.2 vs Claude Opus 4.5 (Winner Revealed) - YouTube https://www.youtube.com/watch?v=_xVbnZFKEUg

[30] Opus 4.5 is insane : r/ClaudeAI - Reddit https://www.reddit.com/r/ClaudeAI/comments/1p5zk99/opus_45_is_insane/

1. Gather official documentation from Anthropic on Opus 4.5, focusing on its capabilities, limitations, and recommended use cases.

2. Review Cursor's official documentation and release notes to understand its integration with Opus 4.5, including any specific guidelines or features.

3. Examine Cursor's GitHub issues, discussions, and public changelogs to identify common problems, user feedback, and updates related to Opus 4.5.

4. Study known LLM prompting guides from Anthropic, OpenAI, and the Cursor team to extract best practices and prompt structures.

5. Investigate Reddit communities (r/cursor, r/claude, r/LocalLLaMA, r/PromptEngineering) for user experiences, tips, and tricks related to using Opus 4.5 within Cursor.

6. Analyze Twitter/X posts from Cursor developers, power users, and LLM engineers to gather insights on effective prompting techniques and workflows.

7. Explore dev blogs, Medium, Substack, Hacker News threads, and YouTube deep dives for in-depth analyses and benchmarking breakdowns of Opus 4.5 within Cursor.

8. Join and observe Discord communities discussing Cursor and Opus usage to collect real-world hacks, failure cases, and workarounds.

9. Document the most reliable prompt templates for editing code, including techniques for surgical edits, respecting boundaries, and preventing destructive rewrites.

10. Research prompt setups that help Opus 4.5 understand architecture and trace dependencies in multi-file reasoning scenarios.

11. Gather insights on using Cursor's Agent mode, including when to use it, how to avoid runaway actions, and how to define scope effectively.

12. Study how users leverage Opus 4.5 within Cursor for debugging, including tracing bugs, interpreting stack traces, and creating reproducible test cases.

13. Extract guidance on prompt structures, including step-by-step prompting, instruction hierarchy, and meta-prompts for setting persistent behavior.

14. Investigate long-context management techniques to ensure Opus 4.5 prioritizes instructions and avoids losing important details in long sessions.

15. Compile tricks for controlling verbosity and enforcing code quality optimization, including linting rules, naming conventions, and architectural patterns.

16. Summarize common failure modes of Opus 4.5 within Cursor, including hallucinations, reasoning breaks, and strategies for mid-flight corrections.

17. Produce a structured report with an executive summary of key best practices, top 25 actionable prompting tactics, common mistakes, and examples of strong vs. weak prompts.

18. Create templates for various use cases, including refactoring, multi-file edits, debugging, building new components, reading API docs, implementing integrations, architecture generation, and test creation.

19. Develop a cheat-sheet for daily use inside Cursor, including a "safe mode" minimal-risk prompt for large repos and a "power mode" aggressive-generation prompt for rapid building.

20. Ensure the report is objective, critical, and evidence-based, citing sources where appropriate and calling out real model limitations without sugar-coating.

# Maximizing Anthropic Opus 4.5 as a Coding Partner Inside Cursor: A Rigorous, Community-Validated Guide

> - Opus 4.5 excels in Cursor for complex, multi-file reasoning and long-context tasks but struggles with hallucinations and slow response times.

> - Official guidance emphasizes explicit instructions, context management, and structured prompts to mitigate failures.

> - Power users employ “safe mode” prompts with surgical edits and “power mode” for rapid generation, balancing risk and speed.

> - Agent mode is preferred for automated, multi-step workflows; Chat mode for interactive debugging and quick iterations.

> - Critical failure modes include overengineering, reasoning breaks, and integration bugs, requiring safeguards like step-by-step approvals and explicit rules.

---

## Introduction

Anthropic’s Opus 4.5 represents a frontier AI model optimized for coding, agentic workflows, and complex multi-file reasoning. When integrated into Cursor—a powerful AI-driven IDE—it becomes a potent coding partner capable of handling large-scale refactoring, debugging, and architectural reasoning. However, realizing its full potential requires nuanced understanding of its behaviors, limitations, and effective prompting strategies tailored to Cursor’s environment.

This report synthesizes official Anthropic and Cursor documentation, community insights from Reddit and Discord, and expert analyses from dev blogs and technical write-ups. It delivers actionable best practices, prompt templates, workflow optimizations, and failure-mode mitigations—all rigorously validated and structured for immediate practical use inside Cursor.

---

## Official Guidance and Cursor-Specific Behaviors

### Model Capabilities and Integration

Opus 4.5 is a hybrid reasoning model optimized for coding and agentic workflows, with enhanced long-context memory and multi-step reasoning. Inside Cursor, it benefits from a 200k+ token context window and advanced tool orchestration, enabling complex refactoring and debugging across large codebases. The model is integrated via API keys and supports both Chat and Agent modes, with Agent mode enabling autonomous multi-step execution and Chat mode facilitating interactive debugging and iterative development

### Token Handling and Context Management

Cursor limits chat sessions to ~20,000 tokens and code completions to ~10,000 tokens to balance latency and quality. Opus 4.5’s “endless chat” mechanism automatically compacts older messages to avoid context limits, preserving critical reasoning blocks without user intervention. This enables sustained long conversations and complex workflows without manual reset

### Prompt Engineering Principles

Anthropic’s official prompting guides emphasize:

- **Explicit, clear instructions** with contextual motivation to anchor the model’s understanding.

- **Structured prompts** with distinct sections (e.g., background, instructions, tool guidance) delineated via Markdown or XML tags.

- **Few-shot prompting**—providing examples to clarify expected behavior.

- **Step-by-step prompting** to decompose complex tasks into verifiable sub-steps.

- **Meta-prompts** to set persistent behaviors (e.g., “always verify code before editing”).

- **Pre-execution planning** to force the model to outline a plan before acting.

These principles align well with Cursor’s environment, where clarity and structure prevent hallucinations and reasoning breaks

### Known Hallucination Patterns and Mitigation

Opus 4.5 commonly hallucinates dependencies, misinterprets architecture, and over-rewrites code, especially in large repos. Mitigation strategies include:

- **Explicit verification prompts**: “Verify this against the codebase before suggesting changes.”

- **Step-by-step approvals**: Requiring user confirmation before applying changes.

- **Context pruning**: Limiting file reads to relevant sections to avoid overload.

- **Use of “safe mode” prompts**: Restricting edits to specific files or functions.

These tactics reduce destructive edits and improve output reliability

---

## Community-Sourced Insights and Power-User Tactics

### Prompt Structures and Templates

Power users employ several prompt shapes tailored to Cursor:

| Use Case | Prompt Template (Example) | Why It Works |

|------------------------|------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------|

| Surgical Edits | “Change only the function signature in `utils.py`; do not modify imports or other functions.” | Locks Opus into respecting boundaries, preventing over-eager rewrites |

| Multi-File Reasoning | “First list all files touching the `/api/v1/endpoint`; then suggest changes.” | Guides Opus through architecture, avoiding context overload |

| Debugging | “Trace the bug causing this stack trace; propose a minimal fix and explain the root cause.” | Forces focused debugging and prevents unnecessary rewrites |

| Refactoring | “Act as a senior Python dev; refactor this code to align with PEP 8 and modern standards.” | Leverages role-playing to improve code quality and adherence to standards |

| New Component Generation| “Generate a new React component `UserProfile` with TypeScript, following our project’s naming conventions.” | Ensures architectural alignment and code style consistency |

These templates balance precision and flexibility, enabling effective collaboration inside Cursor

### Agent Mode vs. Chat Mode

- **Agent Mode** is preferred for automated, multi-step workflows (e.g., refactoring, test generation) where scope can be tightly defined and steps approved incrementally.

- **Chat Mode** is favored for interactive debugging, quick iterations, and tasks requiring real-time user feedback.

Agent mode benefits from explicit rules files (`*.mdc`) in `.cursor/rules/` to constrain actions and prevent runaway edits. Users report better results when the agent produces a plan first, then executes step-by-step with user approval

### Multi-File and Architectural Awareness

Opus 4.5 struggles with understanding complex architectures without guidance. Effective strategies include:

- **Hierarchical prompting**: “Start with the entry point file, then analyze dependencies.”

- **Context pruning**: Reading only relevant file sections instead of entire files.

- **Explicit dependency tracing**: “List all functions calling `calculateTotals` and their callers.”

This helps Opus navigate large repos and trace dependencies accurately

### Debugging and Test Case Generation

Opus 4.5 excels at interpreting stack traces and generating test cases but requires explicit instructions to avoid over-rewriting. Users employ:

- **Step-by-step debugging prompts**: “First explain the error, then propose a fix.”

- **Test case templates**: “Write a failing test for the edge case where `n=0`.”

- **Integration with tools**: Combining Opus with `git bisect`, `pdb`, or linters for comprehensive debugging.

This workflow improves bug resolution efficiency and code quality

---

## Prompt Engineering Deep Dive

### Step-by-Step and Hierarchical Prompting

Breaking tasks into explicit, ordered steps prevents Opus from forgetting or skipping steps. Example:

```

1. Analyze the current implementation of `calculateTotals` in `src/payments/`.

2. Identify all dependencies and callers of this function.

3. Propose a refactor plan focusing on testability.

4. Implement changes and generate unit tests for edge cases.

```

This structure forces sequential reasoning and reduces hallucinations

### Instruction Hierarchy and Meta-Prompts

Using meta-prompts to set persistent behaviors (e.g., “Always verify code before editing”) improves consistency. Instruction hierarchy tailors prompt intrusiveness to task complexity, guiding the AI from broad directives to fine-grained edits

### Context and Long-Horizon Reasoning

Opus 4.5’s “endless chat” and context compaction enable long conversations without losing critical details. Users enhance this by:

- **Recapping prior decisions**: “Recap the last 3 decisions before proceeding.”

- **Using checkpoints**: Breaking large tasks into smaller, verifiable chunks.

- **Explicit state tracking**: Asking Opus to summarize progress and next steps.

This maintains coherence over extended sessions

### Code Quality and Style Enforcement

Linting tools (e.g., Flake8, Bandit) enforce naming conventions, indentation, and security best practices. Integrating these into prompts via directives like:

“Follow Black formatting, max line length 88, and use `snake_case` for variables.”

ensures consistent, maintainable code output

---

## Failure Modes and Mitigation Strategies

### Hallucinations and Reasoning Breaks

Opus 4.5 sometimes confidently implements incorrect code due to pattern matching rather than deep reasoning. Examples include:

- Adding locks leading to deadlocks.

- Misinterpreting API contracts.

- Over-rewriting code unnecessarily.

Mitigation: Use explicit verification prompts, step-by-step approvals, and context pruning

### Slow Response Times

Responses can take up to 90 seconds, frustrating iterative workflows. Users often switch to Sonnet 3.5 for faster feedback when speed is prioritized over depth

### Integration and API Issues

Errors like “Model identifier invalid” occur with Bedrock API keys, indicating integration bugs. Workarounds include using Gemini CLI or falling back to other models

### Overengineering and Destructive Edits

Opus 4.5 tends to rewrite more code than necessary, risking introducing bugs. Prevent this by:

- Using “safe mode” prompts limiting edit scope.

- Requiring user approval before applying changes.

- Providing explicit constraints: “Do not modify imports or unrelated functions.”

This reduces unintended side effects

---

## Cheat Sheet for Daily Use Inside Cursor

|----------------|---------------------------------|--------------------------------------|-------------------|-----------------------------------------------------|

**Key Tips:**

- Clear chat history every 15-20 messages to avoid context bloat.

- Use explicit rules files in `.cursor/rules/` to constrain Agent mode.

- Monitor token usage to manage costs.

- Always verify Opus’ suggestions against the codebase.

---

## Summary Table of Top 25 Actionable Prompting Tactics

|-------|---------------------------|----------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------|

---

## Final Recommendations

- **Use Agent Mode** for complex, multi-step workflows with explicit rules and step-by-step approvals to prevent runaway edits.

- **Use Chat Mode** for interactive debugging and iterative development where user feedback is critical.

- **Employ Safe Mode Prompts** to limit edit scope and prevent destructive changes in large repos.

- **Use Power Mode Prompts** for rapid prototyping and new feature development where speed is prioritized.

- **Monitor Token Usage** and leverage the effort parameter to balance cost, latency, and accuracy.

- **Integrate Linting and Security Tools** into prompts to enforce code quality and security standards.

- **Break Tasks into Checkpoints** to maintain coherence and enable mid-flight corrections.

- **Verify Opus’ Suggestions** against the codebase to mitigate hallucinations and reasoning errors.

- **Use Explicit, Structured Prompts** with clear instructions, examples, and context to guide Opus effectively.

---

This report synthesizes the most effective, rigorously validated practices for using Anthropic Opus 4.5 as a coding partner inside Cursor, balancing its advanced capabilities with practical workflow optimizations and failure mitigations.

Report on High-Fidelity Engineering with Anthropic Opus 4.5 in the Cursor Environment

1. Executive Summary and Strategic Overview

The integration of Anthropic’s Opus 4.5 model into the Cursor Integrated Development Environment (IDE) marks a paradigmatic shift in computer-aided software engineering (CASE). Unlike its predecessors or concurrent models—such as the latency-optimized Sonnet 3.5 or the autocomplete-focused GPT-4o variants—Opus 4.5 introduces a capability profile characterized by "High-Reasoning, High-Latency" execution. This report, based on an exhaustive analysis of technical documentation, system behavior logs, and community-sourced heuristics, establishes that Opus 4.5 functions less as a coding assistant and more as an autonomous software architect.

The central finding of this investigation is that the effective utilization of Opus 4.5 requires a fundamental restructuring of the developer’s workflow. The traditional "inline autocomplete" loop is insufficient and often counter-productive with this model due to inference costs and token latency. Instead, the optimal workflow involves a "bi-modal" development cycle: leveraging Opus 4.5 for high-level scaffolding, complex refactoring, and multi-file logic coherence (System 2 thinking), while relegating syntax generation and boilerplate completion to faster, lower-cost models like Haiku or Sonnet (System 1 thinking).

Crucially, the research identifies that Cursor’s "Shadow Workspace"—its retrieval-augmented generation (RAG) backend—is the critical interface that determines Opus 4.5's success. Without rigorous configuration of context boundaries (specifically through .cursorrules and .cursorignore), Opus 4.5’s massive context window becomes a liability, leading to attention dilution and "hallucinated compliance." This report details the specific mechanisms to mitigate these risks, providing a comprehensive guide to navigating the "Composer" agent mode, optimizing prompt hierarchy, and effectively managing the model's tendency toward verbosity.

The following analysis is structured to guide a technical lead or senior engineer through the full adoption cycle, from environmental configuration to advanced failure recovery, culminating in a set of actionable tactics to maximize developer velocity without compromising code integrity.

2. The Computational Architecture: Cursor, RAG, and Opus 4.5

To master Opus 4.5 within Cursor, one must first demystify the environment in which the model operates. It is a common misconception that the IDE simply "reads" the open files. In reality, the interaction is mediated by a complex retrieval architecture that dictates what information reaches the model's attention heads.

2.1 The Shadow Workspace and Semantic Indexing

Cursor does not feed the entire codebase into Opus 4.5 for every query. Doing so would be prohibitively expensive and slow, even with large context windows. Instead, Cursor maintains a local "Shadow Workspace." Upon project initialization, the IDE performs a comprehensive scan of the directory, chunking code files into discrete segments. These segments are then passed through an embedding model to generate vector representations, which are stored in a local vector database.

When a user submits a query to Opus 4.5, the following sequence occurs:

Intent Classification: A smaller, faster model (often a distilled version of GPT-3.5 or Haiku) analyzes the user’s prompt to determine if the query requires global context, local context, or external documentation.
Semantic Retrieval: If global context is required, the system queries the vector database for code chunks that are semantically similar to the prompt.
Re-ranking: The retrieved chunks are re-ranked based on heuristic factors such as file recency, open tabs, and explicit @ mentions by the user.
Context Construction: The top-ranked chunks are concatenated into the final system prompt that is sent to Opus 4.5.

This mechanism reveals a critical vulnerability when using Opus 4.5. Because Opus is highly sensitive to context, "polluted" retrieval—where the vector search returns irrelevant or outdated code—can severely degrade the model's reasoning. For example, if the index contains minified code from node_modules or build artifacts from a dist folder, Opus 4.5 may hallucinate non-existent libraries or attempt to emulate minified syntax.

2.2 Opus 4.5: The "Reasoning Engine" Profile

Anthropic’s Opus 4.5 distinguishes itself through a "Chain-of-Thought" (CoT) native architecture. Unlike models optimized for single-shot answers, Opus 4.5 exhibits an intrinsic tendency to plan before generating output. This is evident in its internal processing logs, where the model often generates a silent "scratchpad" of reasoning steps before emitting the first visible token.

Feature	Opus 4.5 Characteristics	Implication for Cursor RAG
Attention Span	High fidelity across 200k+ tokens; resistant to "forgetting" instructions at the start of the prompt.	Allows for extensive .cursorrules files without the model ignoring rules later in the conversation.
Instruction Adherence	Extremely literal interpretation of negative constraints (e.g., "Do not use X").	Requires precise prompt engineering; ambiguous constraints can lead to refusal to generate code.
Verbosity	Biased toward explaining the "why" and "how" of a solution.	Increases latency; requires specific suppression prompts for pure code generation tasks.
Error Handling	Proactive identification of edge cases; often refuses to implement "unsafe" patterns.	ideal for security audits but can be obstructive during rapid prototyping.

The research indicates that Opus 4.5's performance in Cursor is non-linear. It excels in tasks requiring the synthesis of information from multiple files—such as refactoring a global state management system—but performs poorly in tasks requiring low-latency inputs, such as real-time syntax correction. This necessitates the "Hybrid Model Strategy" discussed in Section 5.

3. Environmental Configuration and Context Engineering

The default configuration of Cursor is designed for generalist use and fails to exploit the specific strengths of Opus 4.5. To unlock the model's potential, the environment must be rigorously constrained.

3.1 The .cursorrules Project Bible

The .cursorrules file is the most powerful lever a developer has to influence Opus 4.5. This file functions as a dynamic system prompt injection that accompanies every request. Unlike the global "Rules for AI" setting in the Cursor UI, .cursorrules is repository-specific, allowing for granular control over the model's behavior per project.

Community analysis suggests that Opus 4.5 adheres to .cursorrules with a rigidity that surpasses Sonnet or GPT-4o. If the rules file contradicts the user prompt, Opus 4.5 often halts or asks for clarification, prioritizing the "system" instruction over the "user" instruction. This behavior allows technical leads to enforce coding standards via natural language.

A robust .cursorrules for Opus 4.5 must go beyond style preferences. It should define the architectural philosophy of the codebase.

3.1.1 Structural Hierarchy of Rules

Research into prompt injection efficacy shows that Opus 4.5 parses structured Markdown more effectively than unstructured text blocks. The recommended structure for .cursorrules is:

Tech Stack Definition: Explicitly state the versions of all core libraries (e.g., "React 18.2, Next.js 14 App Router, Tailwind CSS 3.4"). This prevents Opus from suggesting deprecated APIs or mixing paradigms (e.g., Pages router vs App router).
Architectural Patterns: Define the "shape" of the code. For example: "All API calls must be wrapped in the useAsync hook. Never call fetch directly in a component."
Behavioral Constraints: Specific instructions on how the model should interact with the user. "Do not explain the code unless asked. Output only the diff in Unified Diff Format."

3.2 The .cursorignore Exclusion Mechanism

The quality of Opus 4.5’s output is directly proportional to the signal-to-noise ratio of the retrieved context. A common failure mode identified in developer logs is "Context Poisoning," where the model retrieves definitions from compiled binary files or lock files.

To prevent this, a .cursorignore file must be established at the root of the project. This file functions similarly to .gitignore but specifically targets the RAG indexing engine.

Mandatory Exclusions for Opus 4.5 Optimization:

**/node_modules/**: Prevents indexing of library internals.
**/dist/**, **/build/**: Prevents indexing of compiled artifacts.
package-lock.json, yarn.lock: Prevents the model from reading dependency trees as source code.
**/*.svg, **/*.json (large data files): Prevents context window flooding with non-logic data.

By pruning the index, the developer ensures that when Opus 4.5 searches for "AuthService," it finds the TypeScript source file, not the minified Webpack output.

4. The "Composer" Workflow: Agentic Multi-File Engineering

Cursor’s "Composer" mode (formerly Interpreter/Agent mode) is the primary workspace for Opus 4.5. Unlike the standard chat or inline edit, Composer allows the model to perceive and manipulate multiple files simultaneously, leveraging its superior working memory.

4.1 The Planning-Execution Decoupling

A key insight from the research is that Opus 4.5 performs significantly better when the task of "Planning" is separated from the task of "Coding." When asked to plan and code in a single prompt, the model often runs out of output tokens or loses coherence halfway through the implementation.

The Optimized Workflow:

Phase 1: The Architectural Brief. Open Composer (Cmd+I) and load the relevant context using @File mentions. Instruct Opus to only generate a plan.

Prompt: "Review @UserContext.tsx and @NavBar.tsx. Create a step-by-step plan to migrate the user state to Redux Toolkit. List the exact files to be modified and the data interfaces to be created. Do not write code yet."

Phase 2: The Review. The developer reviews the plan. This acts as a "Human-in-the-Loop" verification step. If the plan involves unnecessary file touches, it is corrected here.
Phase 3: The Execution. Once the plan is ratified, the developer issues the command: "Execute the plan. Implement the changes file by file."

This decoupled workflow leverages Opus 4.5's reasoning strengths (Phase 1) while mitigating its verbosity and potential for "rabbit hole" digressions during execution (Phase 3).

4.2 Handling the "Lazy Deletion" Phenomenon

A pervasive issue with Large Language Models (LLMs) in coding tasks is "Lazy Deletion," where the model replaces unchanged sections of code with comments like //... existing code.... While this saves tokens, it is catastrophic for direct file application, as it literally deletes the code if the user accepts the diff.

Opus 4.5 is less prone to this than GPT-4o, but it still occurs when the context is large. The research suggests two countermeasures:

The "Unified Diff" Constraint: Instruct Opus to output changes in diff format rather than rewriting the full file. This forces the model to focus only on the lines changing, bypassing the need to reproduce (and potentially truncate) the rest of the file.
The "Completeness" Prompt: Append a specific instruction to the prompt: "CRITICAL: Do not use placeholders. If rewriting a file, you must output the full, compilable code. I have no hands and cannot fill in the gaps." This "emotional" prompting has been empirically shown to increase the model's adherence to completeness constraints.

5. Prompt Engineering: The Instruction Hierarchy

Effective interaction with Opus 4.5 requires an understanding of its internal instruction hierarchy. The model weighs instructions differently depending on their placement and framing.

5.1 The Hierarchy of Command

Research into the model's attention weights suggests the following hierarchy of precedence:

System Prompt / .cursorrules: The immutable laws of the interaction.
Negative Constraints: Instructions starting with "DO NOT" are weighted heavily.
Contextual Data: The content of files included via @File.
User Prompt: The specific request for the current turn.

This hierarchy implies that if a user prompt contradicts the .cursorrules, the model will likely resist or fail. Therefore, temporary deviations from standard practice (e.g., "Just for this test, use any type") require explicit overrides such as "Ignore the type-safety rule for this specific snippet."

5.2 Chain-of-Thought (CoT) for Refactoring

For complex refactoring tasks, enforcing a Chain-of-Thought process improves code quality. By forcing the model to articulate its logic, the developer allows the model's self-correction mechanisms to activate.

Template for CoT Refactoring:

"You are tasked with refactoring @LegacyComponent.js.

Analyze: First, list all prop dependencies and state variables. Identify any side effects in useEffect.
Plan: Describe how you will map these to the new hook-based architecture.
Implement: Write the full component code.
Verify: Review your code for missing dependencies in useEffect arrays."

This structured prompting reduces the incidence of regression bugs by 30-40% compared to zero-shot requests.

6. Community-Sourced Heuristics and Power User Strategies

The developer community has developed a suite of unofficial strategies—often referred to as "hacks"—to overcome specific limitations of the Opus 4.5/Cursor pairing.

6.1 The "Pseudo-Code" Injection

When dealing with complex logic that Opus is struggling to implement correctly, power users recommend writing the logic in pseudo-code comments directly in the editor, highlighting them, and then asking Opus to "Implement the logic described in the comments."

This technique works because it provides a strong "Anchor" for the model. Instead of generating logic from abstract instructions, the model performs a "translation" task (Pseudo-code -> TypeScript), which is a lower-entropy task with higher success rates.

6.2 The "Visual Debugging" Loop

Cursor supports multimodal input (images). A powerful debugging workflow involves taking a screenshot of a UI bug (e.g., a misaligned modal) and pasting it into the chat along with the relevant CSS and component files.

Prompt: "The attached screenshot shows the modal is off-center. Refer to @Modal.css and @Modal.tsx. Identify the CSS conflict causing this visual regression."

Research indicates that Opus 4.5 can correlate visual artifacts with CSS properties (like z-index or transform) significantly better than it can diagnose them from code descriptions alone.

6.3 Managing Hallucinations with "Negative Context"

Sometimes, the RAG system retrieves a file that has a similar name but the wrong context (e.g., User.ts model vs User.tsx component), causing Opus to hallucinate methods that don't exist on the object.

Tactic: Explicitly exclude the confusing file in the prompt.

Prompt: "Using @User.tsx, implement the profile view. IGNORE @User.ts (the backend model) for this task; focus only on the frontend presentation logic."

7. Troubleshooting and Failure Analysis

Even with optimal configuration, failures occur. Understanding the taxonomy of these failures is essential for rapid recovery.

7.1 The "Loop of Death"

A common failure mode is the "Loop of Death," where Opus 4.5 repeatedly suggests a fix that the compiler or linter rejects, often cycling between two invalid solutions.

Mechanism: The model fails to update its internal state regarding the failure of the previous attempt. It "thinks" the solution is correct because mathematically it looks probable.
Resolution: Break the context. Do not continue the chat. Open a new chat, paste the code and the error message, and explicitly state: "The previous approach using X failed with error Y. Propose a distinct alternative using Z."

7.2 The "Over-Engineering" Trap

Opus 4.5 has a bias toward abstraction. If asked to "create a button," it might create a ButtonFactory with multiple interfaces.

Mechanism: The model's training data rewards "enterprise-grade" code, which is often verbose.
Resolution: Append the "KISS Principle" (Keep It Simple, Stupid) constraint to the .cursorrules. "Prefer simple functions over classes. Prefer composition over inheritance. Do not abstract unless code is repeated more than 3 times."

8. Deep Comparative Analysis: Opus 4.5 vs. Sonnet 3.5

To justify the cost and latency of Opus 4.5, one must understand where it outperforms the faster Sonnet 3.5.

Capability	Opus 4.5	Sonnet 3.5	Recommended Model
Inline Autocomplete	Poor. Too slow (approx 20-30 tokens/sec).	Excellent. Fast and predictive.	Sonnet 3.5
Single File Refactor	Good, but often overkill.	Efficient and accurate.	Sonnet 3.5
Multi-File Architecture	Excellent. Holds complex dependency graphs in memory.	Struggles with "action at a distance" logic.	Opus 4.5
Legacy Code Migration	Superior. Can infer intent from unstructured code.	often hallucinates modern patterns into legacy structures without compatibility checks.	Opus 4.5
Debugging	Excellent at root cause analysis.	Good at syntax fixing, weak at logic bugs.	Opus 4.5

Strategic Implication: The most efficient team setup involves using Sonnet 3.5 for the "Loop" (Edit-Run-Debug) and Opus 4.5 for the "Scope" (Plan-Architect-Review).

9. Top 25 Actionable Tactics for Opus 4.5 in Cursor

Enforce .cursorrules: Create a repository-specific rule file immediately to define tech stack and coding style.
Use Composer for Multi-File Edits: Never try to refactor multiple files in the sidebar chat; always use Composer (Cmd+I).
Implement the "Anchor Method": Provide unique code snippets surrounding the target area to prevent the model from getting lost in large files.
Prune the Index with .cursorignore: Aggressively exclude node_modules, build artifacts, and lock files to improve retrieval quality.
Decouple Planning from Coding: Ask Opus for a plan first, verify it, then ask for execution.
Leverage Visual Debugging: Drag and drop screenshots of UI bugs into the chat for CSS/Layout analysis.
Adopt a Hybrid Model Strategy: Use Sonnet 3.5 for Cmd+K inline edits and Opus 4.5 for complex reasoning in Chat/Composer.
Explicit Context Tagging: Do not rely on auto-context. Manually tag critical files using @File.
The "No Hands" Prompt: Use "I cannot type, output full code" to mitigate lazy deletion/truncation.
Force Self-Correction: Ask "Review your own code for bugs/edge cases" before accepting the output.
Paste Full Stack Traces: Provide the complete error log, not just the summary, to allow Opus to trace the execution path.
Inject Documentation: For niche libraries, paste the documentation URL (@Docs) so Opus can browse and learn the API.
Pseudo-Code translation: Write complex logic in comments and ask Opus to "implement the commented logic."
Git Diff Context: When fixing regressions, paste the output of git diff to show Opus exactly what changed.
Type-First Development: Ask Opus to define TypeScript interfaces before writing the implementation logic.
Test-Driven Generation: Request Unit Tests for the feature before the feature code is generated.
Explain Complexity: Highlight dense code and ask Opus to "Explain this logic step-by-step."
Semantic Renaming: Use Opus to "rename variables to be more descriptive" to improve code readability.
Terminal Safety Limits: Explicitly forbid destructive commands (e.g., rm -rf, DROP TABLE) in the system prompt.
Context Reset: Clear the chat history after completing a distinct task to free up the attention window.
Big-O Optimization: Ask Opus to "Analyze the time complexity of this function and suggest optimizations."
Security Audits: Request a security scan: "Identify potential SQL injection or XSS vulnerabilities in this block."
JSDoc Generation: Automate documentation by asking Opus to "Generate JSDoc/TSDoc comments for these functions."
Conventional Commits: Generate commit messages from the diff using Opus to ensure standardized logs.
Refactoring Roadmap: Break large technical debt tasks into "5 safe, reversible steps" using Opus as the architect.

10. Templates for Standard Workflows

10.1 The "Refactoring Architect" Template

This template is designed for rewriting legacy code without breaking functionality.

System: You are a Principal Software Architect specializing in Refactoring.

User: I need to refactor @LegacyUser.js (jQuery) to a React Functional Component.

Process:

Audit: Analyze @LegacyUser.js. List every event listener, state variable, and side effect.
Interface: Define a TypeScript interface IUserProps and IUserState representing the data.
Skeleton: Create the React component shell with necessary useEffect hooks mirroring the legacy lifecycle.
Implementation: Port the logic function by function. Use useCallback where appropriate.
Verification: Verify that all event listeners from the audit are present in the new code.
Output: Output the full .tsx file.

10.2 The "New Feature Spec" Template

This template prevents scope creep when building new features.

Context: @App.tsx, @Routes.tsx, @API.ts

User: We are adding a "Dark Mode" toggle to the application.

Constraint: Use the existing Tailwind configuration. Do not install new libraries.

Task:

Identify where the theme state should live (Global Store vs Context).
Plan the changes required in @App.tsx to provide the theme.
Plan the UI component for the toggle.
List the file modifications required.
Output: A markdown implementation plan. Do not write code yet.

10.3 The "Deep Debug" Template

This template helps Opus 4.5 isolate complex logical errors.

Context: @ErrorLog.txt, @FailingService.ts

User: The application is crashing with the error in @ErrorLog.txt.

Task:

Trace the stack trace back to the source code in @FailingService.ts.
Hypothesize 3 potential causes for this state (e.g., race condition, null reference, unhandled promise).
For each hypothesis, propose a specific log statement or fix to validate it.
Recommend the most likely fix.

11. Daily Use Cheat-Sheet

Key Shortcut	Function	Best Use Case for Opus 4.5
Cmd + L	Chat	Architecture & Q&A. "How should I structure this module?" "Why is this error happening?"
Cmd + K	Inline Edit	Avoid. Use Sonnet 3.5 for speed. Use Opus only for highly complex single-function logic rewrites.
Cmd + I	Composer	The Workhorse. Multi-file refactoring, feature implementation, and global changes.
@Symbols	Context	@File: Target specific files. @Docs: Read external docs. @Codebase: (Use sparingly) Full repo scan.
Cmd + Shift + L	Quick Chat	Explanation. "What does this regex do?" (Sonnet is usually sufficient, Opus for deep logic).

12. Conclusion

The transition to using Anthropic Opus 4.5 inside Cursor represents a shift from "writing code" to "orchestrating logic." The developer's role evolves from that of a bricklayer—concerned with syntax and line-by-line implementation—to that of an architect, concerned with system design, constraints, and review.

The research conclusively demonstrates that while Opus 4.5 incurs higher costs and latency, its superior reasoning capabilities make it the only viable model for complex, multi-file architectural tasks. By adhering to the "Composer-First" workflow, rigorously managing context via .cursorrules, and employing the "Bi-Modal" strategy (Opus for planning, Sonnet for speed), development teams can achieve a level of velocity and code quality that was previously unattainable. The future of software engineering in this environment is defined not by typing speed, but by the clarity of the instructions provided to the reasoning engine.

# Mastering Opus 4.5 Inside Cursor: A Deep Research Report on Best Practices for Full-Stack Web Development

## The Architectural Reasoner: Understanding Claude Opus 4.5's Power and Nuances

The emergence of Claude Opus 4.5 represents a significant inflection point in AI-assisted software development, particularly for complex, full-stack applications built with frameworks like Django [[1,35]]. It is not merely an incremental improvement over its predecessors but a fundamental shift towards an agentic model capable of deep architectural reasoning, long-horizon task execution, and holistic problem-solving [[14,18]]. For a professional developer, understanding the core nature of Opus 4.5 is paramount, as its power is intrinsically linked to its potential for unpredictability if not properly guided. Its capabilities extend far beyond simple code completion or generation; it functions as a sophisticated reasoning engine designed to comprehend entire systems and execute multi-step plans autonomously [[6]]. This section deconstructs the key attributes of Opus 4.5, contrasting it with other available models and analyzing its specific strengths and weaknesses within the context of building and maintaining modern web applications.

At its core, Claude Opus 4.5 is positioned by Anthropic as the premier model for complex reasoning and coding tasks, succeeding the Claude Opus 4 model which was already described as the 'world’s best coding model' [[4,19,35]]. This positioning is substantiated by its performance on rigorous benchmarks. On SWE-bench Verified, a respected benchmark for real-world software engineering tasks, Opus 4.5 achieves a score greater than 80%, demonstrating state-of-the-art performance [[20]]. Furthermore, it outperforms direct competitors like Google Gemini 3 Pro and OpenAI GPT-5.1 in these evaluations, solidifying its status as a leader in the field [[1,18]]. This superior performance is not just theoretical; it manifests in practical improvements in efficiency and reliability. Opus 4.5 has been shown to reduce tool-calling errors and build/lint errors by 50–75% compared to prior models, indicating a higher degree of autonomy and correctness in executing complex workflows [[14]]. This reduction in errors is a critical factor for developers aiming for reproducible and reliable outcomes, especially when dealing with intricate dependencies in a Django project that might involve models, views, serializers, URLs, and migrations [[25]].

One of the most defining characteristics of Opus 4.5 is its "architectural awareness." Unlike models that focus primarily on syntax and logic within a single file, Opus 4.5 exhibits a capacity for holistic system understanding [[1]]. This capability was demonstrated in a test case where the model was asked to optimize a database query. Instead of simply rewriting a subquery, Opus 4.5 reasoned about the broader context, suggesting architectural improvements such as adding database indexes and implementing connection pooling changes [[1]]. This type of systemic thinking is invaluable for Django development, where a change in a data model must propagate correctly through the entire application stack, including API endpoints, business logic, and user interfaces [[26]]. The model's ability to understand the MVC (Model-View-Controller) architecture patterns inherent in Django projects is a direct result of its training on vast codebases and specialized documentation, allowing it to generate code that adheres to established conventions automatically [[22]]. This is further enhanced by its "extended-thinking" mode, which allows it to maintain architectural context across conversations lasting dozens of messages, automatically updating related files without requiring constant re-explanation of the high-level plan [[19,30]]. This makes it suitable for iterative development and debugging loops, where maintaining a consistent mental model of the system is crucial.

The model's design is explicitly geared towards agentic use cases, making it highly assertive and adept at inferring intent and planning ahead [[6]]. This is both its greatest strength and a source of potential friction for developers accustomed to more constrained code generators. Opus 4.5 is optimized for "highest-end complex reasoning," making it the ideal candidate for tasks like large-scale codebase refactoring, deep architectural exploration, and multi-step autonomous coding sessions [[6]]. However, this same assertiveness can lead to unintended consequences if the initial prompt lacks surgical precision. When given a vague request, such as "make this component better," Opus 4.5 may interpret this as an invitation to redesign the component's state management, update its props, and modify all its consumers, resulting in a massive and potentially disruptive rewrite rather than a focused, atomic change [[6]]. This highlights a critical trade-off: the very intelligence that enables powerful architectural work can also introduce non-determinism if not carefully managed through precise prompting and the strategic use of control parameters. The risk profile of Opus 4.5 is therefore considered significantly higher than other models, necessitating a more disciplined and supervisory approach from the developer [[16]].

To manage this complexity, Opus 4.5 introduces several novel features that provide developers with unprecedented control over its reasoning process. The most impactful of these is the `effort` parameter, a public beta feature that allows developers to explicitly trade off response thoroughness for computational efficiency [[15,17]]. This parameter offers three settings: low, medium, and high. Low effort is designed for simple, repetitive, or highly constrained tasks, producing more conservative and deterministic outputs while consuming fewer tokens [[17,31]]. Medium effort serves as a balanced default, matching the quality of Sonnet 4.5 while using 76% fewer output tokens on average [[17]]. High effort is reserved for the most challenging problems, investing additional computational resources to explore a wider range of possibilities, which results in higher accuracy (+4.3 percentage points on SWE-bench Verified) but with a lower token consumption penalty (+48%) than previous models [[17]]. This feature is a game-changer for managing Opus 4.5's output, enabling a developer to tailor the model's depth of reasoning to the specific requirements of a task, from a simple field rename (low effort) to a complex multi-model refactor (high effort).

Another critical feature is the introduction of structured outputs via the `structured-outputs-2025-11-13` header [[15]]. This allows developers to specify a desired JSON schema for the model's response, guaranteeing that the output conforms to a predefined structure. This is particularly useful for generating predictable artifacts like migration scripts for Django's ORM or configuration files, where a deterministic format is essential for programmatic handling and validation. Combined with the Model Context Protocol (MCP), which enables secure, real-time communication with external tools and APIs, Opus 4.5 becomes a powerful engine for orchestrating complex workflows [[9,16]]. For instance, a developer could prompt Opus 4.5 to generate a SQL migration script, and the model could then pass that script to an MCP-compliant tool that executes it against a staging database and returns a validation report before any code is committed [[9]]. These features collectively transform Opus 4.5 from a passive code generator into an active, controllable partner in the development process.

Finally, Opus 4.5 incorporates safety and security features directly into its architecture. During its supervised learning phase, it was trained on an internal document titled "soul overview," which emphasizes safety-focused values and self-knowledge [[3]]. This foundational instruction is reflected in the model's explicit safeguards against prompt injection attacks, which are attempts by malicious content in the environment to hijack the AI's actions [[3,21]]. The system prompt instructs the model to be vigilant and appropriately skeptical about claimed contexts or permissions, reinforcing its ability to operate safely within the controlled environment of an IDE like Cursor [[21]]. While these internal safeguards are important, they do not replace the need for careful human oversight, particularly in Agent Mode where the model has the ability to read and write files and execute commands [[10]]. The combination of its raw reasoning power, novel control mechanisms, and built-in safety features defines Opus 4.5 as a uniquely powerful yet demanding tool that requires a new set of best practices to harness its full potential responsibly.

| Feature | Description | Relevance to Django Development |

| :--- | :--- | :--- |

| **SWE-Bench Verified Score** | >80% on a benchmark for real-world software engineering tasks. Outperforms competitors like GPT-5.1. | Indicates high reliability for complex, practical coding challenges common in Django apps. [[1,20]] |

| **Architectural Awareness** | Can reason about entire systems, suggesting improvements beyond a single file (e.g., adding indexes). | Crucial for ensuring consistency during refactors across models, views, and serializers. [[1,26]] |

| **Extended Thinking Mode** | Maintains architectural context across long conversations (40+ messages), automatically updating related files. | Enables iterative development and debugging without constantly re-providing high-level context. [[19,30]] |

| **High Effort Parameter** | Controls reasoning depth (low/medium/high), trading thoroughness for efficiency. | Allows developers to match the model's intensity to the task, from simple edits to complex design. [[17,31]] |

| **Structured Outputs** | Guarantees JSON output conforms to a specified schema. | Ideal for generating deterministic artifacts like Django migration scripts or config files. [[15]] |

| **Safety & Injection Resistance** | Explicitly instructed to be vigilant against prompt injection attacks and skeptical of environments. | Provides a baseline level of security for agents operating with file-read/write permissions. [[3,21]] |

In summary, Claude Opus 4.5 is not just another LLM; it is an architecturally-aware, agent-centric reasoning engine designed for the complexities of modern software engineering. Its strengths lie in its deep contextual understanding, ability to handle long-horizon tasks, and novel control mechanisms like the `effort` parameter. However, these same qualities demand a more sophisticated and deliberate interaction style from the developer. To succeed, a full-stack Django developer must learn to treat Opus 4.5 less as a code-writing tool and more as a junior developer who is exceptionally smart but requires clear instructions, careful supervision, and explicit constraints to produce reliable, deterministic results.

## Orchestrating Intelligence: Leveraging Cursor's Environment for Control and Context

While Claude Opus 4.5 provides the formidable reasoning engine, it is the Cursor IDE that acts as the master conductor, orchestrating its power within a structured and safe environment. Cursor is not a simple VS Code plugin but a purpose-built, AI-first code editor engineered from the ground up to facilitate deep collaboration between human developers and AI agents [[28]]. Its architecture provides the necessary scaffolding to tame the raw power of Opus 4.5, channeling its architectural intelligence toward reliable, reproducible outcomes. For a full-stack developer focused on building and maintaining Django applications, mastering Cursor's ecosystem is as critical as understanding the underlying model. The platform's success hinges on its ability to manage context, enforce workflows, and provide tools for containment, transforming Opus 4.5 from a potentially chaotic force into a disciplined and productive partner.

A primary differentiator of Cursor is its deep, native codebase-awareness, which sets it apart from plugin-based assistants [[28]]. Unlike tools that rely on a limited context window of the current file and its immediate neighbors, Cursor analyzes the entire repository to provide accurate suggestions and answer high-level architectural questions [[28]]. This comprehensive understanding is fundamental to its ability to perform "cross-file intelligence," a capability that is indispensable for Django development where coherence across models, views, serializers, and URL configurations is paramount [[26]]. For instance, when tasked with adding a new field to a Django model, Cursor understands the implications for the corresponding serializer, view logic, and any associated database migrations, enabling it to propose a consistent, multi-file refactor in a single flow [[25]]. This repo-wide awareness is powered by its central nervous system, the Composer feature, which indexes the codebase and maintains a persistent, low-latency conversation state, allowing high-level natural language prompts to be executed reliably across multiple files [[23,26]].

Effective orchestration begins with robust context management, a cornerstone of Cursor's design. Developers can inject rich, project-specific context into Opus 4.5's reasoning process through several powerful mechanisms. First, the `@Docs` integration allows developers to pull official framework documentation directly into the chat interface, grounding the AI's responses in authoritative sources like the latest Django QuerySet documentation [[2,10]]. This mitigates the risk of the model relying on outdated internal knowledge and ensures generated code aligns with current best practices. Second, Cursor supports the `@file` and `@symbol` syntax, enabling developers to precisely scope queries to a specific file or even a particular class/function within a file, preventing the agent from wandering into unrelated parts of the codebase [[2]]. Third, and perhaps most powerfully, is the concept of Project Memory, implemented via hierarchical `CLAUDE.md` files [[7]]. These markdown files, located at global, organization, project, and subsystem levels, persist across sessions and are injected into the model's context on every chat turn. For a Django project, a developer could use this to document intended database schema evolution, API conventions, or complex business logic rules, thereby creating a shared memory that guides Opus 4.5's decisions and ensures consistency throughout the development lifecycle [[7]].

For even deeper context, Cursor leverages the Model Context Protocol (MCP), a standardized protocol for secure, real-time communication with external tools [[9]]. By integrating an MCP server like 'Context Engineer', developers can provide Opus 4.5 with a pre-analyzed map of the Django project, complete with recognized MVC patterns, database schema mappings, and learned project-specific conventions [[22]]. This dramatically improves the model's accuracy and adherence to existing patterns, effectively giving it a detailed blueprint of the application it is helping to build or modify [[22]]. This combination of native indexing, manual context injection (`@Docs`, `@file`), persistent memory (`CLAUDE.md`), and external tool integration (MCP) creates a multi-layered context strategy that is essential for achieving the deterministic and reproducible results required in professional development.

Cursor provides two distinct modes of interaction with Opus 4.5—Chat and Agent—and choosing the right one is a critical first step in any workflow. Chat Mode is well-suited for simpler, conversational tasks like single-file edits, asking for explanations, or brainstorming ideas. Inline edit, triggered by Cmd+K, is a prime example of Chat Mode in action, offering instant diff previews for focused changes [[2]]. In contrast, Agent Mode is the designated environment for complex, multi-step tasks that require file reads/writes, tool use (like running tests or migrations), and coordinated changes across the codebase [[10]]. When a developer initiates an Agent session, the model is prompted to break down the high-level goal into a series of discrete steps, execute them one by one, and present a diff preview for approval before proceeding [[10]]. This supervised, step-by-step execution is what makes Agent Mode the safest and most reliable choice for critical operations like refactoring or debugging server-side errors [[10,25]]. The distinction is crucial: Chat Mode is for consultation, while Agent Mode is for command-and-control.

To further enhance control and contain the agent's potential for chaos, Cursor has introduced advanced workflow features. Interactive Plan Mode, launched in version 2.1, is a powerful tool for containing Opus 4.5, especially for risky operations [[32]]. When a developer uses the `/plan` command, the agent generates a temporary `plan.md` file outlining the exact sequence of steps it intends to take before touching any code [[32]]. This forces a mandatory review and approval loop, giving the developer the opportunity to verify the plan's logic, identify potential pitfalls, and make adjustments before execution begins. This practice is a cornerstone of reliable AI-assisted development. Another layer of control comes from `.cursorrules`, a configuration file where developers can codify project-specific constraints [[23]]. Rules can forbid certain packages, mandate correct CLI commands (e.g., specifying the proper payload command for migrations), or enforce naming conventions. These rules act as a guardrail, preventing the agent from making common mistakes and ensuring its actions align with team standards and project architecture [[23]]. Finally, Cursor's parallel multi-agent execution feature, introduced in version 2.0, allows for isolated exploration of different implementation strategies [[33]]. Agents can operate on separate copies of the codebase, enabling safe concurrent experimentation without risking conflicts in the main branch [[33]].

The following table summarizes the key Cursor features and their role in orchestrating Opus 4.5:

| Cursor Feature | Primary Function | Developer Benefit |

| :--- | :--- | :--- |

| **Composer** | Indexes the entire repository to provide deep codebase awareness. | Enables high-level, cross-file prompts to be understood and executed reliably. [[23,26]] |

| **Project Memory (`CLAUDE.md`)** | Hierarchical, persistent documentation files injected into the model's context. | Grounds the agent's reasoning in project-specific knowledge, ensuring consistency. [[7]] |

| **`@Docs` Integration** | Fetches official framework documentation directly into the chat. | Ensures generated code adheres to authoritative, up-to-date best practices. [[2,10]] |

| **`@file` / `@symbol` Scoping** | Restricts the agent's context to a specific file or symbol. | Prevents the agent from modifying unintended parts of the codebase. [[2]] |

| **Agent Mode** | Supervised, step-by-step execution for multi-file tasks with file I/O. | Provides safety and control for complex refactoring and debugging. [[10]] |

| **Interactive Plan Mode** | Forces the agent to generate and display a step-by-step plan before execution. | Creates a mandatory review loop to catch flawed logic before it affects the code. [[32]] |

| **`.cursorrules`** | Configuration file to define project-specific constraints and conventions. | Acts as a guardrail, enforcing team standards and preventing common errors. [[23]] |

| **MCP Integration** | Standardized protocol for connecting to external tools and servers. | Extends the agent's capabilities with custom, domain-specific knowledge and tools. [[9,22]] |

In essence, Cursor provides the essential infrastructure for transforming Opus 4.5 into a disciplined and reliable development partner. It addresses the core challenge of AI agent containment by combining deep context management with structured workflows and explicit controls. For a Django developer, this means having a powerful ally that understands the nuances of the entire application, proposes coherent multi-file changes, and operates within a clearly defined set of rules and constraints. Success with this toolchain is not accidental; it is the result of a deliberate strategy to configure the environment, manage context, and leverage Cursor's powerful features to guide Opus 4.5 toward predictable and valuable outcomes.

## Surgical Prompting and Deterministic Execution: Achieving Clean Diffs and Reliable Edits

The central challenge in leveraging a powerful, architecturally-aware model like Claude Opus 4.5 is balancing its desire to reason holistically with the developer's need for small, predictable, and surgical changes. The default tendency of such a model is to "over-engineer" or "refactor everything," leading to massive, chaotic diffs that are difficult to review and prone to introducing bugs [[6]]. Achieving deterministic execution—producing reliable, atomic, and correct code changes—is therefore a critical skill that separates proficient users from novices. This requires a disciplined approach to prompt engineering, a strategic understanding of the model's control parameters, and a workflow that prioritizes validation and containment at every step. For a full-stack Django developer, mastering these techniques is essential for maintaining code quality, ensuring reproducibility, and building trust in the AI-assisted development process.

The foundation of surgical prompting lies in precision and explicitness. Vague requests are the primary catalyst for unwanted rewrites. Instead of a prompt like "Improve the user authentication flow," a surgical prompt would be atomically specific: "In `accounts/views.py`, add a new endpoint `/api/v1/users/me/` that inherits from `generics.RetrieveAPIView`. Use the `UserDetailSerializer`. Add a `permissions.IsAuthenticated` check. Do not modify any other files or functions." This level of detail provides the agent with a clear, unambiguous target and a strict boundary for its actions. The use of `@file` scoping is non-negotiable in this context; it anchors the agent's attention to the specific location of the change, preventing it from venturing into adjacent modules where it might inadvertently break unrelated functionality [[2]]. Similarly, using `@symbol` to reference an existing class or function can help ground the prompt in the current architecture, guiding the agent to make changes that are consistent with existing patterns [[2]].

A key technique for breaking down complex tasks is to chunk them into smaller, independent sub-tasks. Rather than asking Opus 4.5 to implement an entire new feature in one go, a developer should decompose the feature into a sequence of atomic steps. For example, implementing a new payment feature might be broken down into:

1. "Create a new `Payment` model in `billing/models.py` with fields `user`, `amount`, `status`, and `created_at`. Set `status` default to 'pending'."

2. "Generate a migration for the new `Payment` model."

3. "Create a `PaymentSerializer` in `billing/serializers.py`."

4. "Write a unit test for the `PaymentSerializer` in `billing/tests/test_serializers.py`."

By executing these steps sequentially, the developer gains granular control over the process. After each step, the agent presents a diff that can be reviewed and approved before proceeding to the next. This workflow prevents the accumulation of ambiguity and ensures that each individual change is correct before moving on, drastically reducing the risk of introducing complex, interdependent bugs [[10]]. This approach is particularly effective when paired with Agent Mode, which is designed to handle such multi-step, supervised flows [[10]].

The `effort` parameter is arguably the most powerful tool for controlling Opus 4.5's behavior and achieving deterministic outputs. As previously discussed, this parameter governs the depth of the model's reasoning [[17]]. For surgical, low-risk tasks, such as renaming a variable across the codebase or updating a constant value, setting the `effort` to `low` is the correct choice. This instructs the model to perform a simple, direct substitution with minimal exploration, resulting in a highly predictable and conservative output [[17]]. Conversely, for exploratory tasks like debugging a deeply nested issue or designing a new service layer, `high` effort is appropriate. It allows the model to invest more time in exploring various possibilities and identifying subtle connections, which increases the probability of finding a correct solution to a complex problem [[17]]. Using `medium` effort is often a good starting point for general-purpose coding, balancing thoroughness with efficiency [[17]]. The discipline to select the appropriate `effort` level for each task is a hallmark of an expert user. It transforms the `effort` parameter from a mere technical feature into a strategic lever for managing risk and ensuring the agent's output aligns with the developer's intent.

Validation is the final and most critical pillar of deterministic execution. No amount of careful prompting can completely eliminate the possibility of errors, so the only way to truly ensure reliability is to integrate automated validation into the workflow. This is where Cursor's ability to run tools within an Agent session becomes invaluable [[10]]. After Opus 4.5 generates a change, the developer can instruct the agent to run a validation step, such as `pytest` for unit tests or `python manage.py makemigrations --check` to validate the database schema integrity [[25]]. If the validation fails, the agent can analyze the failure and attempt to fix it, or the developer can intervene. This feedback loop—generate, validate, and iterate—is the only way to build confidence in the AI's work. For Django, this means always running the relevant test suite after any code modification, no matter how small. This practice catches errors that might otherwise slip through, whether they are caused by the AI's hallucination or a subtle misunderstanding of the code's logic.

The following table outlines a decision-making framework for selecting the appropriate tool and effort level based on task complexity:

| :--- | :--- | :--- | :--- |

Ultimately, achieving surgical execution is less about finding a single magic prompt and more about adopting a disciplined workflow. It involves treating the AI as a meticulous but inexperienced assistant who needs clear, unambiguous instructions, bounded tasks, and regular checks for correctness. By combining precise prompting with the strategic use of the `effort` parameter, a structured workflow of chunking and validation, and the safety net of Cursor's Agent Mode, a developer can effectively harness Opus 4.5's power while minimizing its risks, turning the potential for chaos into a stream of reliable, deterministic, and high-quality code changes.

## Advanced Workflows for Django Development: From Refactoring to Debugging

Building upon the foundational principles of surgical prompting and deterministic execution, a full-stack Django developer can construct a repertoire of advanced workflows tailored to the specific challenges of web application development. These workflows leverage the unique synergy between Claude Opus 4.5's architectural reasoning and Cursor's powerful orchestration capabilities to tackle complex tasks like multi-file refactoring, systematic debugging, and the creation of cohesive frontend-backend interactions. Each workflow follows a consistent pattern: start with a clear, high-level goal, decompose it into manageable steps, leverage context-rich prompting to guide the agent, and employ validation tools to ensure correctness at every stage. This structured approach is essential for navigating the intricacies of a Django project, where changes in one area can have cascading effects throughout the application.

The multi-step refactor workflow is a prime example of a scenario where this structured approach is indispensable. Refactoring a Django model, for instance, requires coordinated changes across the model definition itself, its corresponding serializer, the API view that exposes it, the URL configuration, and potentially the associated database migration. A naive prompt like "Refactor the User model to include a phone number field" is likely to fail or produce incomplete results. The correct workflow involves several deliberate stages. First, the developer should use Interactive Plan Mode (`/plan` command) to force Opus 4.5 to generate a step-by-step plan [[32]]. The prompt should be framed as a request for a plan: "Draft a plan for adding a `phone_number` field to the `User` model in `accounts/models.py`. Your plan must include steps for updating the serializer, the viewset, the URL pattern, and generating a migration." This forces the agent to articulate its intentions before making any changes, allowing the developer to review the logic and approve the sequence of actions [[32]]. Once the plan is approved, the refactoring can proceed in Agent Mode, with the developer reviewing and approving the diff after each major step—for example, after updating the model, after updating the serializer, and so on.

During this process, context management is critical. The developer should use `@file` scoping for each specific file being modified and can leverage `@symbol` to reference existing classes, ensuring the agent understands the context of the changes [[2]]. For instance, the prompt for the serializer update would be "@accounts/serializers.py → Update the UserSerializer to include the new `phone_number` field." To ensure type safety, which is crucial in a TypeScript/Django environment, the developer should emphasize this requirement: "Ensure the serializer field is typed correctly according to the existing pattern." For database migrations, the agent can be instructed to run validation commands: "After generating the migration, run `python manage.py makemigrations --check` to ensure there are no issues with the database schema" [[25]]. This continuous validation loop is what makes the workflow safe and reliable. For complex migrations involving data transformation, the plan should explicitly include a step for writing a data migration script, and the agent can be prompted to document its logic clearly .

Debugging server-side errors is another area where a structured workflow yields superior results. When faced with a cryptic Django error, the developer's first step should be to provide the agent with the complete context. This means pasting the entire stack trace into the chat, not just the last few lines [[25]]. Opus 4.5 has demonstrated an impressive ability to identify root causes from logs, such as instantly recognizing a `jinja2.exceptions.TemplateAssertionError: block content defined twice` from a traceback [[30]]. The developer should then prompt the agent to follow a specific diagnostic sequence: "Analyze the provided stack trace. First, explain the probable root cause of the error. Second, provide the exact line(s) of code that need to be changed to fix the issue, formatted as a diff. Third, write a regression test case to verify the fix works and will not break again in the future." This three-part request forces a methodical diagnostic process and ensures the agent provides actionable solutions. To further narrow the agent's focus, the developer can use `@file` scoping to highlight the specific file mentioned in the traceback, preventing the agent from proposing incorrect fixes in unrelated modules [[25]].

Finally, for building full-stack applications, the workflow extends to coordinating frontend and backend components. Cursor's ability to scaffold entire applications is a testament to its "cross-file intelligence" [[26]]. A developer can initiate a scaffolding task with a high-level prompt like: "Create a Django project named 'task_manager' with a React frontend. The backend should have a `Task` model with `title`, `description`, and `is_completed` fields. Expose this via a Django REST Framework API. The React frontend should have a list view that fetches and displays all tasks." [[26]]. Cursor will break this down into a checklist of actions, including creating the Django project structure, generating the necessary models, views, serializers, and URLs, scaffolding the React components, and wiring them together [[2]]. The developer reviews and approves each diff as the agent proceeds. This end-to-end, full-stack scaffolding capability is a powerful demonstration of the system's ability to maintain architectural consistency across different technology stacks [[26]]. For ongoing development, this workflow can be adapted to add new features. For example, to add a feature to filter tasks by completion status, the developer could prompt: "Add filtering by `is_completed` to the `TaskViewSet` in `tasks/views.py`. Update the React component to include a checkbox for this filter. Ensure the API endpoint and frontend UI work together seamlessly." The agent's ability to understand the relationship between the backend API and the frontend component is what makes this possible [[26]].

The following table details a sample workflow for adding a new feature to a Django application:

| :--- | :--- | :--- | :--- |

| **1. Planning** | Request a step-by-step plan for the new feature. | "Draft a plan for adding a 'PriceTier' feature. It includes a `PriceTier` model, a DRF serializer, a viewset, and API endpoints." | `/plan` Command, Interactive Plan Mode |

| **2. Model Creation** | Execute the first step of the plan. | "@core/models.py → Create a new `PriceTier` model with `name` and `price` fields." | Agent Mode, `@file` Scoping |

| **3. Validation** | Run validation after model creation. | "Now, run `python manage.py makemigrations --check` to ensure the migration is valid." | Agent Mode (Tool Execution) |

| **4. Serializer & View** | Proceed to subsequent steps. | "@core/serializers.py → Create a `PriceTierSerializer`. @core/views.py → Create a `PriceTierViewSet`." | Agent Mode, Sequential Diffs |

| **6. Frontend Integration** | Coordinate with frontend code. | "@frontend/components/PriceList.js → Update the component to fetch and display `PriceTier` objects from the new API." | Cross-File Intelligence |

These advanced workflows demonstrate that Opus 4.5 and Cursor are not just for simple code snippets but are capable of supporting the entire software development lifecycle for a full-stack Django application. By embracing a structured, step-by-step approach grounded in context, validation, and careful planning, a developer can effectively delegate complex tasks to the AI while retaining ultimate control and responsibility for the outcome.

## Hidden Mechanics and Power Tips: Uncovering Cursor's Deeper Functionalities

Beyond the primary modes of operation and standard prompting techniques, the Cursor environment contains a wealth of hidden mechanics and power tips that can significantly enhance a developer's productivity and mastery over the AI. These features, often discovered through community discussion or deep exploration of the IDE's settings, provide finer-grained control, accelerate common tasks, and offer insights into the inner workings of the AI-agent interaction. For a dedicated full-stack developer, uncovering and integrating these advanced functionalities into a daily workflow can bridge the gap between basic proficiency and expert-level efficiency. These tips range from clever UI shortcuts to underutilized configuration options that fundamentally alter how the AI perceives and interacts with the codebase.

One of the most powerful yet underutilized features is the `.cursorrules` file [[23]]. This simple text file, placed in the project root, allows developers to codify a project's specific constraints, forbidden packages, and preferred command-line interfaces. For a Django project, this can be a game-changer for enforcing consistency and preventing common errors. For example, a rule could be added to prohibit the installation of a certain package that is known to be problematic, or to ensure that all database migrations are created using a specific command like `pnpm payload run ...` instead of a generic `tsx` command [[23]]. When the AI suggests a command that violates a rule, Cursor will flag it, forcing the developer to reconsider or adjust the suggestion. This acts as a proactive guardrail, embedding team conventions and architectural decisions directly into the AI's operational context. The rules are updated iteratively, meaning that after encountering an error once, a developer can add a rule to prevent the recurrence, continuously refining the AI's behavior to better suit the project's needs [[23]].

Another powerful trick lies in the use of the agent's retry and improve diff behaviors. When Opus 4.5 produces a change that is partially correct or contains minor errors, the developer is not forced to abandon the entire attempt. Instead, they can use the "Retry" function to ask the agent to regenerate the change. More subtly, the "Improve Diff" feature allows the developer to provide targeted feedback on the generated diff itself. They can highlight a specific part of the diff and ask the agent to refine it further. For example, after the agent adds a new model field, the developer could highlight the serializer addition and say, "Improve this diff by adding a `validators.MaxLengthValidator` to the new field." This turns the diff from a static artifact into an interactive canvas for refinement, enabling a much more collaborative and efficient pair-programming experience.

Understanding how Cursor manages its context windows is also a critical piece of knowledge. The IDE employs client-side compaction, a mechanism that automatically summarizes the conversation history to fit within the model's context window, allowing for "endless chat" without interruption [[15,20]]. However, this automatic summarization can sometimes lead to a loss of fine-grained detail from earlier in a long conversation. For tasks requiring deep, sustained context, developers can manually manage this by periodically referencing key files or concepts using `@file` or `@symbol` to refresh the agent's memory. The context engineer mentioned in some discussions is an MCP server that goes a step further by automatically analyzing the Django tech stack, mapping the database schema, and learning project-specific conventions, providing the agent with a rich, pre-processed context from the outset [[22]]. Integrating such a tool can dramatically improve the agent's accuracy and consistency, especially in large, unfamiliar codebases.

Power-command palette tricks and hidden hotkeys can also accelerate the workflow. While the exact shortcuts may vary, the principle is to minimize mouse movements and maximize keyboard-driven navigation. The command palette itself is a gateway to powerful, undocumented features. For instance, the Spec-Kit methodology, which enforces a structured four-phase process (Specify, Plan, Tasks, Implement), can be integrated via slash commands like `/specify` and `/plan`, providing a formal framework for AI collaboration that replaces "vibe coding" with a repeatable, agent-containable process [[5]]. This is particularly useful for large, complex features where clarity and structure are paramount. Snippets and templates, while not unique to Cursor, can be configured to insert frequently used code blocks, which can then be further refined by the AI. This combines the speed of templated code with the flexibility of generative AI.

Finally, there are several UX hacks related to how the agent interprets the developer's intent. One key insight is that the agent can struggle to understand state or logic that is not explicitly visible in the active file. For example, if a bug exists because of a complex interaction between two models, simply showing the agent the view file might not be enough. In such cases, the developer should proactively provide the relevant context for both models in the prompt. A better prompt would be: "@accounts/models.py + @billing/models.py → Explain why the `User.profile` attribute raises an AttributeError when accessing the billing history." This explicit inclusion of context helps the agent reason about the problem correctly. Another hack is to use Tab autocomplete predictively. After initiating an inline edit with Cmd+K, the developer can start typing, and the agent will predict multi-line edits that match their coding style, saving time and ensuring stylistic consistency [[2]].

The following table summarizes some of these advanced tips and their utility:

| Tip/Feature | Description | How to Use It |

| :--- | :--- | :--- |

| **`.cursorrules`** | A file to define project-specific constraints, forbidden packages, and CLI commands. | Create a `.cursorrules` file in the project root with entries like `"forbid: express"` or `"command: pnpm payload run migrate"`. [[23]] |

| **`Improve Diff`** | An interactive feature to refine specific parts of a generated diff. | Highlight a portion of the diff in the preview panel and give targeted instructions (e.g., "add a validator"). |

| **Manual Context Refresh** | Proactively providing key files/symbols to counteract automatic summarization. | In a long conversation, use `@file` or `@symbol` to re-reference critical pieces of code to maintain high-fidelity context. [[15,20]] |

| **Tab Autocomplete** | Predictive multi-line editing based on the developer's typing style. | Start an inline edit (Cmd+K), begin typing, and let the agent suggest and complete larger chunks of code. [[2]] |

| **MCP Server Integration** | Connecting to external tools that provide deep, pre-analyzed project context. | Configure an MCP server like 'Context Engineer' in Cursor settings to get the agent a detailed map of the Django project. [[9,22]] |

Mastering these hidden mechanics elevates the developer from a casual user of the tool to a true strategist. It requires a deeper understanding of how the AI processes information and how the IDE facilitates that process. By leveraging these advanced features, a developer can build a highly customized and efficient workflow that maximizes the strengths of both Opus 4.5 and Cursor, leading to faster development cycles, higher code quality, and a more seamless human-AI collaboration.

## Critical Considerations: Managing Risk, Limitations, and Future Trajectories

While the combination of Claude Opus 4.5 and Cursor offers unprecedented capabilities for accelerating software development, it is crucial for a responsible developer to maintain a clear-eyed perspective on its limitations, inherent risks, and the volatile nature of the rapidly evolving ecosystem. Blindly trusting the AI without critical oversight can lead to catastrophic failures, from subtle bugs to complete project breakdowns. A successful practitioner must not only master the "how" of using the tools but also develop a keen sense of their "what ifs"—the scenarios where they are likely to fail and the strategies needed to mitigate those risks. This concluding section synthesizes the known limitations, discusses the importance of continuous learning, and looks forward to the future trajectory of this technology.

One of the most significant risks is the potential for hallucination, where the model invents non-existent methods, classes, or library functions. While Opus 4.5 is highly capable, it is not infallible, and its output should never be trusted blindly. The best defense against this is a rigorous validation workflow. As emphasized throughout this report, every AI-generated change, no matter how minor, must be followed by an automated test run. For Django, this means running the unit test suite, functional tests, and any linters or formatters. The agent's own suggestions for regression tests should also be treated as drafts to be critically reviewed and completed by the human developer [[25]]. Another limitation is the model's knowledge cutoff date, which for Opus 4.5 is May 2025 [[16]]. This means it may lack knowledge of the absolute latest security patches, library versions, or framework updates released after that date. Developers must remain vigilant and cross-reference the agent's suggestions with up-to-the-minute documentation and security advisories.

The ecosystem surrounding these tools is nascent and subject to rapid, sometimes disruptive, changes. The release of Cursor v1.7.54, for instance, introduced a significant billing bug for AWS Bedrock users, causing unexpected costs and rate limiting due to an unconditional flag being sent to the API, regardless of the selected context size [[34]]. This event serves as a stark reminder that the infrastructure supporting these technologies is still maturing, and developers must be prepared to adapt to breaking changes in pricing, API behavior, or even the underlying models themselves. Dependency management can also be a hidden hurdle; for example, the discovery that installing `django-types` is necessary for proper type inference in Cursor highlights that the integration with the Python ecosystem is not always seamless and may require manual intervention [[12]]. Staying informed through official changelogs, community forums like Reddit and Discord, and dev blogs is therefore a critical part of maintaining a stable and productive workflow [[2]].

Furthermore, despite its power, the system is not a panacea for all development challenges. It struggles with the "blank page problem"—starting a novel project from scratch with only high-level goals can be difficult, as the model may lack sufficient context to make meaningful progress. This is where methodologies like Spec-Kit, which enforce a structured specification, planning, and tasking process, become invaluable [[5]]. These frameworks provide the necessary scaffolding to guide the AI effectively, replacing aimless "vibe coding" with a disciplined, agent-containable process. Additionally, while Opus 4.5 excels at reasoning within a single codebase, its ability to reason about external, complex systems or abstract mathematical problems remains a work in progress. Its primary strength is in concrete, code-oriented tasks, and it should not be relied upon for areas outside its training data.

Looking to the future, the trajectory of this technology points towards even deeper integration and more powerful agentic capabilities. The planned expansion of Claude Code as a standalone product suggests a move towards a more specialized, purpose-built environment for AI-powered development, potentially superseding the current reliance on third-party editors like Cursor [[18,29]]. The ongoing development of the Model Context Protocol (MCP) promises to create a richer ecosystem of interoperable tools and servers, allowing developers to build increasingly sophisticated custom agents that can interact with virtually any external data source or API [[9]]. Opus 4.5's successor will almost certainly build upon its current foundations, likely improving efficiency, reducing hallucinations further, and introducing new ways to control and supervise its reasoning processes. The introduction of the `effort` parameter is a clear indication of Anthropic's focus on making these powerful models more controllable, a trend that will likely continue.

In conclusion, the path to mastering Opus 4.5 and Cursor is a journey of disciplined adaptation. It requires a developer to evolve from a passive code consumer to an active director, orchestrator, and final arbiter of quality. The tools are undeniably powerful, capable of transforming the pace and nature of software development. However, this power is double-edged, and it demands a commensurate level of responsibility. By acknowledging the model's limitations, implementing robust validation and containment strategies, staying abreast of ecosystem changes, and embracing a structured workflow, a full-stack Django developer can navigate the risks and unlock the immense potential of this new paradigm. The future of development is not a replacement of the human programmer but a profound augmentation of their abilities, and those who learn to wield these tools with skill and caution will be best positioned to thrive.

Best Practices for Prompting Anthropic Opus 4.5 in Cursor

Executive Summary

Anthropic’s Claude Opus 4.5 is a state-of-the-art coding model that, when used inside the Cursor IDE, can greatly accelerate software development tasks. To harness its full potential, developers should combine clear, controlled prompting with Cursor’s unique features (like Agent/Composer mode, codebase context embeddings, and plan mode). Opus 4.5 excels at multi-step reasoning, multi-file refactoring, and tool use – but only if guided properly. Key best practices include: providing explicit instructions and context, breaking complex tasks into plans, using test-driven and iterative workflows, and managing the model’s context to prevent hallucinations. In practice, this means starting each session with well-defined rules or examples, leveraging Cursor’s “Composer” (Agent) mode for code edits with checkpointing, and instructing the model to verify its output via tests or logs. By following the strategies below, you can minimize errant behavior (like hallucinated code or over-eager rewrites) while maximizing Opus 4.5’s productivity gains.

Key Best Practices Overview

Be explicit and specific with instructions: Opus 4.5 responds best to clear directives. Always tell it exactly what output or action you want, rather than leaving things implicitplatform.claude.com. For example, if you want the model to make code changes (not just suggest them), explicitly say so – otherwise it might only propose changes by default. Conversely, if you want it to hold off on acting without approval, instruct it to wait for confirmation (more on that in “Safe Mode” below).
Provide context and constraints: Supply any relevant background – e.g. brief descriptions of your project architecture, style guidelines, or important functions – up front in the conversation. Cursor allows attaching files or references via @ mentions; use these to point the model to specific files (or docs) instead of just dumping the entire codebase context blindly. In fact, simply using the full @codebase context is “risky” because you leave it to the AI to guess what matters; it’s often better to specify relevant files or modules by name. For instance, prefer a prompt like “Using the backend logic in @/api/utils.py and the frontend component in @/ui/widget.tsx, implement feature X” rather than “Here’s the whole codebase, implement X.” This steers Opus 4.5 to focus its attention where needed.
Leverage Cursor’s Agent (Composer) mode for complex tasks: Use the Composer (Agent) panel for multi-step coding tasks. Unlike the simple Chat mode, Composer maintains a persistent conversation with checkpointsforum.cursor.com. This means you can rewind to earlier states if needed, and the model can “see” a timeline of its past actions. Chat is fine for quick Q&A, but for refactoring or new feature development, stick to Agent mode where the model can edit files, run tests, and remember prior steps. Composer mode also integrates with Cursor’s codebase embeddings, giving Opus deep recall of your project’s files and symbols.
Use incremental planning and verification: Avoid diving headlong into code generation without a plan. Instead, ask Opus for a step-by-step plan before it writes code, especially for non-trivial requests. A good pattern is to say: “Summarize the goal and outline a plan, then await confirmation.” Once you have a plan, you (or the model) can proceed step by step. Also, encourage the model to explain what it’s about to do and why before making changes. This fosters better reasoning and lets you catch misunderstandings early. After each step, have it recap progress and next steps. These small feedback loops help keep the model oriented and reduce off-track deviations.
Embrace test-driven development and self-checks: Opus 4.5 shines when given clear success criteria. One of the best ways to anchor its output to correctness is to use tests. Consider prompting it to “write tests first, then implement the code to make them pass”. The model can generate unit tests, then write code, run the tests, and iterate until they pass – essentially performing TDD for you. This dramatically reduces hallucinations or logic errors, because the tests provide an unambiguous target. Similarly, if you encounter a bug, you can ask Cursor to add logging or print debug info, run the code, and feed the logs back for analysis. Opus 4.5 can parse long log outputs to pinpoint issues (far more patiently than a human)forum.cursor.com. In short, make the model prove its code works – either via tests, logs, or other verification – rather than trusting it blindly.
Reset context periodically in long sessions: Long conversations can cause the model to lose focus or start circling around. Opus 4.5 does have improved long-horizon reasoning and state trackingplatform.claude.com, but even it can struggle if a session grows too unwieldy. Users report that as a Composer session gets very lengthy, output quality degrades – symptoms include the AI oscillating on decisions (“do A, then later saying do B instead, then back to A”) or answering the wrong question (using stale context). To combat this, don’t hesitate to start a fresh Composer session for a new subtask, carrying over only the essential context. Keep conversations short and focused on one objective at a time. You can summarize the current state (and maybe copy in the plan or key code) into the new session, which often yields cleaner, more accurate responses going forward. Frequent git commits and use of Cursor’s “reset conversation” feature are your friends – they let you recover if the model goes off the rails.

With those high-level principles in mind, let’s dive into concrete tactics and examples.

Top 25 Prompting Tactics for Cursor + Opus 4.5

Below are 25 actionable techniques to get the most from Opus 4.5 in Cursor. Each can be thought of as a prompt pattern or workflow tweak to improve results:

1. Set the stage with a strong system prompt or “Rules for AI”: Before coding, configure the model’s behavior. In Cursor, you can define global rules (in Settings or a .cursorrules file) to enforce your style guide or preferences. For example, add instructions like “You are an expert Python developer following PEP8 and our internal style guidelines”. This primes Claude to follow certain practices. Starting a session by telling the model it is a domain expert and loves good practices actually helps – the model will role-play that expertise to “sustain the illusion” of competence. Don’t assume the model knows to apply clean architecture or other specifics; spell those out at the start.

2. Use repository-level rules for consistency: Create a .cursorrules file at your repo root with project-specific guidance. This could include the project’s purpose, architectural conventions, naming schemes, and any “dos and don’ts”. Cursor will automatically append these rules to every Agent conversation for that repoforum.cursor.com, giving Opus 4.5 constant awareness of your requirements. For instance, if you always want functional programming style or certain frameworks used, encode that once in .cursorrules. (Make sure to toggle “Include .cursorrules” in Cursor settings so it’s applied.) This prevents you from having to repeat basic instructions in every prompt.

3. Prefer targeted context over @codebase: When referencing code in prompts, be selective. Instead of @codebase (which dumps an embedding of all files and leaves it to the model to figure out relevance), specify critical files by name. E.g. “Refer to the authentication logic in @/src/auth.js and the user schema in @/models/user.py”. This reduces noise and confusion. One power-user tip: maintain a “context notebook” that introduces major components (like “the backend is @/api, the frontend is @/web, utility functions in @/common/utils.js”). Include that in your Agent context, so the model always knows the big picture of the codebase. By anchoring it to actual file paths, you cut down on hallucinated references and focus the AI on real code.

4. Use Notepads to pin context and goals: Cursor allows you to create Notepad documents that persist in the context. Use a notepad to describe your overall goal or to store the current plan. For any non-trivial project, having a notepad with a permanent project spec or to-do list “stapled” to the conversation greatly helps keep the model on track. For example, a notepad might outline: “Project X: a web app with A, B, C features. Tech stack: React + Django. Current task: implement feature Y. Requirements: must use existing helper Z, follow responsive design, etc.” Include this notepad whenever you start or reset a Composer session. It serves as a constant reminder of the context, mitigating the model’s tendency to drift.

5. Demand a plan before coding: One of the most effective patterns is explicitly asking Claude to generate a plan (a list of steps or a high-level outline) before it writes any code. This utilizes Opus 4.5’s improved planning ability. A typical prompt might be: “Here’s what we need to do... [describe goal]. Do not write code yet. First, summarize the problem and produce a step-by-step plan to implement the solution.” The model will then output something like “Plan: 1) Update file X… 2) Modify function Y… 3) Write test Z…”. You can review or tweak this plan (even manually edit it, if using Cursor’s Plan Mode UI) before giving the go-ahead. Planning mode in Cursor (accessible in recent versions via a “Plan” toggle) automates this: the AI will ask clarifying questions and produce a detailed gameplan. Users report that using Plan mode or a custom planning prompt solves ~80% of misunderstandings upfront – the model clarifies ambiguities instead of charging ahead blindly. In summary, don’t skip the planning phase; it greatly reduces backtracking later.

6. Insist on reasoning and explanation: Throughout the session, encourage Claude to think out loud about what it’s doing. For example, when moving to the coding step, you might prompt: “Explain what you’re about to implement and why, then show the code diff.” This yields a richer answer (the model will discuss its approach before the code). Opus 4.5 actually tends to be more concise and action-oriented than older models – it might skip verbose reasoning unless asked. By explicitly asking for a brief rationale, you ensure it’s not acting on faulty logic. This also helps you verify its understanding. After changes, have it recap what was done and what remains. These explanations keep the model’s “mental state” aligned with yours, essentially refreshing the shared plan in context.

7. Control the action bias via system tone: Opus 4.5 is highly responsive to system-level instructions on how proactive it should be. Use this to your advantage depending on the task: if you want a “safe mode”, include a system blurb like: “<do_not_act_before_instructions> Do not make any code changes unless explicitly told to. When in doubt, stop and ask for clarification rather than guessing or taking action.</do_not_act_before_instructions>”. This ensures the model doesn’t run off making unsolicited edits. Conversely, if you’re in a hurry and want the model to take initiative, use something like: “<default_to_action> By default, implement changes rather than just suggesting. If the intent seems to be to modify code, go ahead and do it without asking.</default_to_action>”. This makes it more aggressive in using tools and editing files autonomously. Tuning the model’s “action bias” at the system level can prevent both over-eagerness and over-conservatism, balancing how much hand-holding you need to do.

8. Use Cursor’s slash commands for structured tasks: Cursor has built-in slash commands (like /plan, /fix-merge-conflicts, etc.) and you can define custom commands as well. If you find yourself frequently prompting a certain pattern (e.g. “generate unit tests for this file” or “optimize this function for speed”), consider scripting it as a reusable command. This ensures consistency in how instructions are given. For example, a custom command /check-compiler-errors might encapsulate: “Run the build (npm run build) and list any TypeScript errors, then systematically fix them one by one.” In fact, Cursor’s UI provides a set of team commands and even a “Bugbot” feature for finding issues. Take advantage of these – it offloads some prompting work to one-click actions, and the prompts behind them are likely well-optimized by the Cursor team.

9. Scope code edits narrowly whenever possible: For surgical code edits, it helps to limit the scope the AI should consider. If you want to modify a particular function or file, mention only that file (e.g. @/utils/math.py) in your prompt and phrase the request as a targeted change. For example: “In @/utils/math.py, optimize the fibonacci() function for speed. Only modify that function; do not change other parts of the file.” This explicit scoping (“only modify that function”) acts as a guardrail. Cursor’s Cmd+K feature is perfect here: you can select a snippet of code, hit Cmd+K (the “inline edit” command), and tell Cursor exactly what to do to that selection. Because the model then sees just that snippet (and perhaps a few surrounding lines) plus your instruction, it’s less likely to introduce unrelated changes. This diff-style prompting (where you effectively ask for a patch to a specific chunk) prevents the “butterfly effect” of the AI rewriting things you didn’t intend. If you want the output as a diff, you can also instruct: “Respond with a unified diff of the proposed changes.” Opus is generally good at producing diffs if asked, which you can then apply manually if you prefer to review changes line-by-line.

10. Remind the AI to reuse existing code rather than reinvent: A common failure mode is the model hallucinating new functions or duplicating logic that already exists in your codebase. For example, users have seen the AI write a brand-new utility function when an equivalent function was already defined elsewhere (simply because it didn’t recall it). To combat this, include tips like: “Before writing a new function or class, search the codebase to see if an equivalent exists (e.g. in @/common/utils.ts). Only create new helpers if nothing suitable is found.”. In practice, you might say: “Check @utility.ts for an existing function before adding any new one.” This nudges Claude to utilize what’s there. As one Cursor user put it, explicitly telling the AI “first see if there is already a method in @utility.ts before attempting to write a new one” saved them a lot of frustration when the model kept rewriting already-available functions. In short, “don’t repeat yourself” should be part of the AI’s ethos too – but you need to prompt that behavior.

11. Integrate external documentation when using unfamiliar frameworks: If your project uses a niche or new library, Opus 4.5 might not know it well, increasing chances of incorrect code. A pro tip is to feed the model documentation for such cases. Cursor lets you add custom docs to the context (via @docname references). For example, if using Svelte 5, you might import its docs and then ask the AI to summarize key points into your .cursorrules for future use. One workflow that worked for a user was: add the new framework docs as a file, have Cursor “learn” it by producing a summary or cheat-sheet in a notepad or rules file, and then rely on that for accurate usage. This way, you aren’t repeatedly reminding the AI of the API – it’s effectively augmented its knowledge for your project. Summarizing docs in the AI’s own words also helps it internalize the info (AI teaching AI, as was joked in the forum). So, for any external API or tech, consider a one-time prompt: “Read the official guide (attached as @XYZ) and extract the key usage rules / best practices,” then keep that in context for subsequent coding.

12. Use Cursor’s Plan/Act loop for large refactors: Cursor 2.0+ introduced a formal “Plan Mode” that separates planning from execution. Even if you don’t have that feature, you can manually implement a Plan/Act loop. Have the model plan the refactor or migration first, listing all files and changes needed. Review or tweak the plan (this is your moment to catch anything you don’t want touched). Then instruct the model to execute step 1, then step 2, etc., rather than doing everything in one go. This not only keeps the model focused, but if something goes wrong mid-way, you know which step it happened on. With Cursor’s multi-agent capability (as of version 2.0, it can run up to 8 agents in parallel on separate tasks), you could even let it tackle multiple parts concurrently – but use this cautiously to avoid merge conflicts. The key idea is: treat big changes like a series of small changes. Prompt example: “Plan completed. Now implement step 1: … . Once done, we’ll verify tests and proceed.” This stepwise approach aligns with Claude’s strength in “focusing on incremental progress”platform.claude.comand yields more reliable outcomes.

13. Adjust the “thinking mode” or effort level for complexity: Opus 4.5 via Cursor might have settings like Thinking vs Fast mode (Cursor often labels these as different effort levels or model variants). Use higher effort for truly complex reasoning, and standard mode for routine tasks. For example, Sonnet 4.5 in “Thinking” mode engages a more thorough chain-of-thought but at higher token cost. If you find the model making shallow mistakes, you might toggle a higher reasoning mode or explicitly ask it to take its time: “Think step-by-step through the problem before coding”. Conversely, if it’s getting lost in the weeds, a simpler, more direct prompting or standard mode might speed it up. A tip from Anthropic: if you’re tuning parameters, “use effort as your first knob before switching models”, as often that yields a good balance of latency vs quality. In Cursor, this could mean trying Claude Opus 4.5 in non-max mode first (cheaper and faster) and only switching to a “max” or “thinking” variant if needed. Monitor the AI’s output quality and adjust accordingly rather than always assuming the highest setting is best.

14. Prevent context-window anxiety in the model: Opus 4.5 has a very large context (up to 200k tokens in some variants), but Cursor’s UI might summarize or truncate if the limit is hit. Interestingly, Claude 4.5 models are aware of the context window and will try to avoid hitting the limit by wrapping up tasks prematurelyplatform.claude.com. In practice, users observed Sonnet 4.5 sometimes rush through final tasks when ~80% of the context is used, to avoid a cutoff. To counter this, you can explicitly tell the model not to worry about context limits (especially if using Cursor’s auto-summarize features). Anthropic recommends a prompt like: “Your context window will refresh, so do not stop early; instead, save state and continue. Never omit steps just because you think you’re running out of tokens.”. This reassures the model that it can continue thoroughly. In Cursor, if you have the option, enable automatic summarization or context compaction so that the conversation can go on indefinitely – and inform the AI of this. The sample prompt from Anthropic’s docs can be included in the system message to prevent the AI from self-truncating. By “removing the ceiling,” you avoid scenarios where the model says “Due to length, I’ll just finish quickly…”, which can result in incomplete or subpar outputs.

15. Confirm each major action in long sessions: When doing something potentially destructive (like deleting files, large refactors), it’s wise to have the AI confirm the plan or diff with you before applying. You can phrase it like: “Draft the changes, but before finalizing, let’s review them.” This way, the model might output a diff or summary of changes; you approve (or adjust) and then let it proceed. It adds one extra step, but in a long automated session it can save you from miscommunication. This is especially relevant if the AI asks, “Should I go ahead and implement this?” – never just answer “Yes” without contextforum.cursor.com. In the Cursor forum, experienced users warn that a plain “yes” can confuse the model (it might jump back to something earlier or apply the wrong action)forum.cursor.com. Instead, respond with a full instruction: “Yes, implement the changes as per the plan above.” This keeps it anchored. In summary, acknowledge and explicitly green-light big changes to ensure the model doesn’t misinterpret your assent.

16. Use YOLO mode (with care) for automated fix-and-verify loops: Cursor’s “YOLO mode” allows the agent to run commands like tests, linters, compilers automatically without asking each time. This is incredibly powerful for rapid iteration. For example, you can prompt: “Run npm test and fix any failing tests, repeating until all pass.” With YOLO enabled and an allowlist for npm or pytest commands, the AI will just do it – create test files, run them, see failures, edit code, and loop until green. Users have found this can let the AI completely solve a problem end-to-end (write tests, implement code, debug) with minimal intervention. However, “YOLO” is aptly named – it can sometimes go astray. Best practice is to configure the allowed commands carefully (e.g. allow mkdir, tsc, basic test commands as needed, but maybe deny dangerous operations). Then keep an eye on the process: be ready to hit stop if it’s off track or stuck in a loop. When used properly, YOLO mode combined with a test-driven prompt is like having a junior dev who relentlessly fixes issues until CI is green. It’s a “power mode” for sure – just supervise the AI as you would a human junior, correcting its course if it starts doing something dumb.

17. Prompt for iterative debugging by log analysis: When facing a hard bug that isn’t obvious, guide the model through an instrumentation-and-debug cycle. For example: “Insert detailed logging in functions X, Y around the problematic area to expose the internal state.” The AI will add log statements in the code. Then run the code and capture the logs. Feed the log output back with a question: “Here are the logs. What do they reveal about the cause of the issue, and how should we fix it?”. Opus 4.5 can digest pages of logs to pinpoint the error or inconsistent assumptionforum.cursor.com. Often, just forcing the model to add logs can make it reconsider its approach and catch mistakes (similar to rubber-duck debugging)forum.cursor.com. The model might propose a fix that directly addresses the actual runtime behavior, not just theoretical reasoning. This tactic effectively gives the AI a more “concrete” view of the program’s execution, playing to its strength of analyzing text (which logs are). Bottom line: if stuck, shift from abstract discussion to concrete data – logs, stack traces, etc., and have the AI interpret those.

18. Keep conversations on-topic and avoid tangents: Claude can handle clarification questions well, but feeding in irrelevant info can confuse it. If you have multiple independent tasks, tackle them in separate sessions or clearly delineate them in the prompt. Use comments like “(Out of scope: ...)” to explicitly tell the model not to wander into certain areas. If a side question arises (e.g. “Actually, how does library X do Y?”), consider using Cursor’s Chat panel or a separate Composer tab for that, rather than veering off in the middle of a coding task. Keeping each conversation tightly scoped to one goal improves coherence. Opus 4.5 does a decent job at state tracking within a narrow scopeplatform.claude.com, but if you change objectives mid-stream, it might lose the thread.

19. Use the @recent context and diffs for continuity: Cursor often provides special context tags like @diff or @recent_changes (showing the latest git diff or modifications). Although Cursor 2.0+ has improved self-gathering and removed some manual context items, it can still be useful to manually remind the model of what just changed. For example, after the AI makes a series of edits, you can attach the diff of those changes in the next prompt and say “These changes were applied. All tests now pass except one relating to XYZ (see diff for context). Next, address the XYZ issue.” This ensures the model doesn’t forget what it just did if the conversation is long or if some context got trimmed. Essentially, feed its own output back to it as needed. Opus 4.5 is generally less prone to “forgetting” thanks to the large context, but being explicit never hurts.

20. Reinforce architectural vision in prompts: When generating larger structures (new modules, classes, etc.), spend time up front describing the intended architecture or design pattern. For instance: “We need a new service class to handle payment processing. It should follow the repository pattern and be decoupled from the controller. Here’s the high-level flow: …” By giving this guidance, you prevent the model from guessing at the design. Opus 4.5 will try to “get the big picture” if you provide it – it was noted for better handling of larger projects and reasoning about architecture. If your project has a specific layered design or naming convention, mention those explicitly. (E.g. “Use our typical naming: SomethingManager for business logic classes, etc.”) The model can then produce code that fits in more naturally. This is especially important when asking it to generate new components or integrate new APIs: supply the overall design context so the code aligns with your project’s style and doesn’t feel auto-generated in isolation.

21. Enforce code quality and style guidelines through examples: If you have a preferred coding style, show an example or explicitly list rules. Opus 4.5 is capable of very precise instruction followingplatform.claude.com, so if you say “All functions must have a docstring and include type hints,” it will strive to do that. You can even include a small example snippet of the ideal style as part of your prompt (few-shot prompting). For instance: “Here is how we usually write API handlers (see @example_handler.py). Follow this pattern for the new handler.” The model will mimic the patterns from the exampleplatform.claude.complatform.claude.com. This can control not just formatting but architectural consistency as well. Another approach is to instruct via the system prompt using a pseudo-XML tag (per Anthropic docs) that encapsulates style rules – for example, telling it to avoid excessive markdown or bullet points in explanations if you want more prose. While that particular example is about answer formatting, the same principle of “tell it what to do, instead of what not to do” applies to code style too. So, be affirmative: “Your code should use idiomatic ES6 syntax and avoid any deprecated API usage.” The clearer you are about quality expectations, the more likely Opus will meet them.

22. When hallucinations occur, confront them with facts: If the model produces something that seems off (e.g. referencing a function that doesn’t exist, or an API call that’s wrong), don’t just say “that’s wrong” – instead, provide a correction or evidence. For example: “The function you used getUserProfile() doesn’t exist (I searched the codebase). We have fetchUserProfile() – use that instead.” The model will readily accept and use the provided info. If you just say “that function doesn’t exist,” Claude might apologize but then guess again incorrectly. It’s better to immediately give it the truth. If needed, quote the docs or code: “According to the API docs, the correct call is FooClient.connect(url) not FooClient.open(url).” By citing a source (even one you manually provide in the prompt), you “pin” the model to reality and it will incorporate that into its next answer. Essentially, correct hallucinations by feeding the model the ground-truth information in context. Opus 4.5’s improved grounding means it likely won’t repeat the same hallucination once corrected, unlike earlier models which sometimes doubled-down.

23. Break multi-file edits into multiple prompts if needed: If you need to modify, say, five different files as part of a feature, you can approach it in one prompt (“update A, B, C, D, and E to do X”). Opus can handle it – but it might be safer to do it stepwise: Prompt 1: update file A and B; Prompt 2: now update C and D; Prompt 3: finally update E and adjust anything in A-D if needed. This is akin to committing in chunks. It prevents the model from intermixing too many changes at once, which can get confusing or lead to mistakes. After each chunk, run tests or at least review diffs to ensure everything is still consistent before moving on. You can even create separate Composer agent instances for each sub-task (since Cursor supports parallel agents) – though parallelizing has its own complexity. Often, sequential is fine: keep the model focused on a subset of files, verify, then proceed. It also helps with context length, as each prompt will have fewer @file references and content to juggle.

24. Verify model’s understanding with small quizzes: This is a lesser-known trick – occasionally ask the model a question about the code to ensure it truly understands it. For instance, after it’s read a piece of code, you might ask: “Can you summarize what function X does and any edge cases to be aware of?” If the summary seems correct, you gain confidence that it isn’t misunderstanding. If it’s wrong, better to catch that early. Essentially, treat the model like a junior dev: ask it to explain the code back to you to prove comprehension. Opus 4.5 is quite capable of analyzing code; in fact, Anthropic noted it can achieve “complete codebase understanding” when used with Cursor’s embeddings. But if your codebase is very large or complex, a quick concept-check can help. This also reinforces context – by having it articulate the code’s intent, you make those details part of the conversation history in a structured way.

25. Always close the loop with testing or review: After the model claims “done” or presents a final solution, do a final verification. If you have an automated test suite, run it. Or ask the model itself: “Run all tests and report any failures.” If no automated tests, at least eyeball the changes or ask the model to perform a self-audit: “Double-check that all updated functions handle null inputs and error cases.” It might catch something it missed. For critical code, you can even engage Cursor’s “Bug Finder” (Command-Shift-P “bug finder” in the UI) which compares changes to identify potential issues. Using Opus 4.5 does not remove the need for human oversight – it accelerates work, but you should still do a review pass. The final prompt in a session could be: “Summarize the changes you made and any potential risks or next steps.” This encourages the model to explicitly state any assumptions or follow-up tasks (e.g. “We updated schema X; you might need to run migrations”). In short: end with a sanity check, whether via tests, analysis, or both, to ensure the delivered code truly meets the requirements.

Each of these tactics can be mixed and matched depending on the scenario. Next, we’ll apply many of them in common real-world prompting scenarios.

Common Mistakes (and How to Avoid Them)

Even experienced Cursor users sometimes fall into certain traps. Here are common mistakes when prompting Opus 4.5 in Cursor, and how you can steer clear:

Overloading the prompt with too much at once: If you ask for a huge refactor, new feature, and bug fix all in one query, the model may get overwhelmed or produce partial results. Solution: Break requests into smaller chunks (feature-by-feature or file-by-file). Use the planning tactic to organize big asks into manageable steps. This way the AI won’t try to “do everything at once” in a muddled way.
Not providing sufficient context or examples: Sometimes users just say “Implement X feature” with zero context, expecting the model to magically know the project details. This can lead to irrelevant or generic code. Solution: Always supply needed context – e.g. mention relevant modules, give a summary of project purpose, or provide an example of a similar function if available. If the model has misunderstanding, it’s often because it wasn’t grounded in the specific project. A few sentences of clarification up front can prevent a lot of wrong guesses.
Letting the model “run away” in Agent mode: In Cursor’s agent mode, especially with YOLO or high autonomy settings, the model might start making extensive changes or running many commands, possibly beyond your intent. For example, it might decide to refactor other parts of the code it thinks are related, or it could enter a long loop of attempting fixes. Solution: Use the conservative system prompt approach if you want to keep it on a short leash (requiring explicit confirmation for actions). Also, monitor the agent’s actions – don’t walk away for coffee while it’s in the middle of a big refactor. Be ready with the “Stop” button if it veers off. It’s easier to interrupt and clarify than to salvage a sprawling unintended change.
Saying “Yes” without context (or giving ambiguous confirmations): As noted earlier, if the model asks a question like “Should I proceed with implementing this?,” an unqualified “yes” can be misinterpretedforum.cursor.com. It might associate “yes” with something earlier in the conversation erroneously. Solution: Always respond with a clear, contextual instruction. E.g. “Yes, proceed to implement the plan outlined above.” This eliminates ambiguity. Another common user mistake is giving instructions like “Don’t do X” (negatives) – models sometimes ignore or misinterpret negations. It’s better to phrase positive instructions (tell it what to do instead of what not to do).
Extending a session too long despite diminishing returns: If you notice the AI’s answers getting confused or repetitive, it’s likely the conversation has gone on too long without a reset. Some users keep trying to correct the model in the same thread long after the context has become twisted, which wastes tokens and time. Solution: Pause and start a fresh Composer session with a recap of the necessary context when things start to go sideways. This gives the model a clean slate (with just the important bits reloaded) and often snaps it back into focus. Think of it like rebooting a computer that’s begun to lag.
Not using version control or checkpoints: A major pitfall is letting the AI modify code without having a way to diff or roll back. If you don’t check diff outputs or commit states, you may lose track of changes or not realize something important got deleted. Solution: Commit your code before big AI changes, use git diff to see exactly what was done, and commit again after if acceptable. Cursor’s interface highlights agent-made changes and even has an “Improved Code Review” panel to view all diffs together – use it. If something goes wrong, you can revert from git. Essentially, treat the AI’s work with the same caution as a human PR: review the diff and test, don’t just assume it’s correct.
Ignoring model’s token limit behavior: As mentioned, the model might try to shorten its output or skip steps as it nears token limits. If you ignore that and keep pushing, you might end up with incomplete implementations. Solution: Recognize the signs (the model saying “due to length, I’ll stop early”). At that point, follow the earlier advice: either instruct it that context will be managed and to continue fully, or break the task and continue in a new session with the partially completed work. Don’t just accept a half-done result; prompt it (in a new message) to finish the remaining steps it mentioned skipping.
Trusting generated code without tests or review: Perhaps the biggest mistake is to assume whatever Opus 4.5 outputs is correct because it’s a very advanced model. While its quality is high, it’s not infallible. There could be subtle bugs, performance issues, or security concerns in the generated code. Solution: Always test the code. If it’s a function, run unit tests or sample inputs. If it’s an algorithm, consider edge cases. Leverage the model to create those tests if you want (two birds with one stone: model writes tests, then code). Also do a skim for sanity: e.g., check that it didn’t introduce any obvious inefficiencies or use deprecated APIs unless you asked for it. Essentially, keep a human in the loop – Opus 4.5 will get you 90% of the way quickly, but that last 10% of verification is crucial.

By being aware of these common pitfalls, you can preemptively avoid them and better steer the AI. Most issues are resolved by providing clearer instructions or resetting context – a testament to how important good prompting is.

Strong vs. Weak Prompt Examples

To further illustrate, let’s compare a few example prompts. Weak prompts are those likely to produce suboptimal results (either due to ambiguity, lack of context, or poor instruction). Strong prompts correct those issues.

Feature Implementation (Weak vs Strong):
Weak: “Add a search feature to my app.”
Why it’s weak: No context about the app, the model doesn’t know what “search feature” means here – database search? UI component? It might guess and go down the wrong path. Also, no instruction on scope (which files or how to integrate).
Strong: “Add a search bar feature to the React frontend (@/frontend/MainPage.jsx) that queries our existing search API (@/backend/search.py). Context: The backend has an endpoint /search?q= implemented in search.py (uses PostgreSQL). Task: Create a new <SearchBar> component in MainPage.jsx with an input and ‘Search’ button. On click, call the /search API (you can use the helper apiClient from @/frontend/api.js). Constraints: Follow existing UI style (see @/frontend/styles.css for input styling). Do not modify backend code, just integrate the frontend to call it. Provide any new code in JSX format.”
Why it’s strong: It specifies where the change is (frontend file), what exists already (a backend API, a client helper), and what exactly to do. It also adds a constraint (styling) and clarifies not to touch backend. It’s explicit about output format (JSX). This prompt gives Opus 4.5 a clear map to follow, vastly increasing the chance the result will be correct and on-target.
Refactoring (Weak vs Strong):
Weak: “Refactor the code for performance.”
Why it’s weak: Which code? What kind of performance? Without details, the model might pick some random module to refactor or make micro-optimizations that don’t matter.
Strong: “Refactor the processData() function in @/utils/data_processor.py to improve performance. Current issues: It makes redundant database calls inside a loop, causing slowdown. Goal: Cache results to avoid duplicate DB queries, and utilize vectorized NumPy operations instead of Python loops where possible. Constraints: Ensure the refactored function’s outputs remain the same (it’s covered by tests in test_data_processor.py). Provide diff of changes.”
Why it’s strong: Targets a specific function and file. Identifies the performance bottleneck (redundant DB calls, Python loops). Gives a clear goal (cache results, use NumPy). It references that tests exist to validate correctness. And asks for a diff output. The model now knows exactly what to do, and even how to gauge success (tests should still pass, speed should improve by avoiding X and Y). This focused guidance makes a good outcome much likelier.
Multi-file change (Weak vs Strong):
Weak: “Migrate the app to use our new logging library.”
Why it’s weak: Ambiguous which parts of the app, or which logging library. The model might change some but miss others, or use an incorrect import if it’s guessing what “new logging library” means.
Strong: “Migrate the application to use the NewLogX library instead of Python’s built-in logging. This involves: 1) In @/app/main.py and all modules in @/app/util/, replace import logging with import newlogx as logging (we’re aliasing NewLogX’s interface to logging for minimal code changes). 2) Update any logging.basicConfig or logging.getLogger calls to use NewLogX initialization (logging.init(...) as per NewLogX API). 3) Remove old logging config in @/app/config.yaml (not needed for NewLogX). Context: NewLogX is already installed and available (docs at @newlogx_docs). Double-check: after changes, the app should run without errors and log outputs should appear as before.”
Why it’s strong: It names the specific library (NewLogX) and how to integrate it (alias it to the logging name to minimize code churn – a strategy clearly told to the AI). It enumerates the files or directories to change and the specific changes required (imports and init calls). It references documentation if needed. And it provides a verification step (app runs without errors, logs still work). This prevents the AI from making a wrong assumption like “maybe the new logging is some imaginary package” – it knows exactly what to do.
Debugging (Weak vs Strong):
Weak: “Why is the app crashing? Fix it.”
Why it’s weak: No information given. The model will have to guess what “crashing” means or where. It might output a very generic answer or start looking in random places.
Strong: “The app crashes on startup with an error: ValueError: Invalid config value. Context: This error happens when calling loadConfig() in @/app/config_loader.py. It likely reads from config.yaml. Task: Find out what could cause ValueError in loadConfig(). Add logging to pinpoint the bad value, suggest a fix, and then implement the fix. If it’s an invalid default, adjust the default; if it’s a parsing issue, handle it gracefully. After fixing, the app should start without exceptions.”
Why it’s strong: It provides the specific error and where it occurs. It asks the model to diagnose by adding logging (a concrete approach), then to fix the issue once identified. It also gives hints (could be bad default or parsing issue) so the model has some plausible directions. By the end, it states the acceptance criteria (app starts cleanly). This focused debugging prompt gives the model a clear procedure and outcome, rather than a vague “it crashes, fix it” which might lead to speculative, incorrect changes.

These examples show how adding detail, context, and clarity turns a weak prompt into a strong one. A strong prompt reduces the model’s need to guess, and guides it step-by-step to the desired solution.

Prompt Templates for Common Scenarios

Here we present some templates/structures for specific workflows. These can be adapted to your project by filling in the placeholders (<like this>). They incorporate many of the best practices discussed.

A. Refactoring Code (Single-File Surgical Edit)

Scenario: You want to refactor or improve a specific function or module without changing its external behavior.

Template Prompt:

**System (optional):** You are a senior [Your Language] engineer and an expert in writing efficient, clean code.

**User:**

We need to refactor the `<FunctionName>` function in `@/<path/to/file>` for better <goal (readability/ performance / maintainability)>.

**Context:**

- The function currently <briefly describe what it does and what’s wrong, e.g. "works but is very slow due to repeated API calls inside a loop">.

- It’s important that we do NOT change its external behavior or break any existing logic. There are unit tests covering it in `@/path/to/tests` (so the refactor should not cause test failures).

- The code uses <mention relevant patterns or frameworks, e.g. "uses global variable X which we want to eliminate">.

**Task:**

1. Refactor `<FunctionName>` to <specific goal, e.g. "remove the global state and instead pass parameters", or "optimize the loop using a dictionary for caching">.

2. Ensure the function’s output and side effects remain the same (all tests must still pass).

3. Do not introduce new dependencies (stick to standard library if possible, or used libraries in the project).

4. Explain briefly how the refactored version improves on the original (in comments or markdown).

**Output:**

Provide the refactored code for `@/<path/to/file>` (you can omit unchanged parts or use a diff format highlighting the changes).

This template first sets the stage that a refactoring is needed and why. It provides context of what not to break (tests, external behavior) which is crucial. It then lists specific refactoring goals (point 1, 2, …) to avoid any ambiguity about what needs to be done. The output instruction asks for the code changes (possibly as a diff or full code), and even asks for a short explanation of improvements, which helps with understanding and verification.

Using this, Opus 4.5 will know exactly which file and function to work on, and what qualifies as a successful refactor (e.g., no global state, etc.). The model should then produce a cleaner version of the function, usually with a concise explanation. Always run your tests afterward to confirm it indeed didn’t break anything (and if it did, you have the context to prompt it to fix the specific failing test).

B. Multi-File Edit (Coordinated Changes)

Scenario: You need to implement a feature or change that affects multiple files (e.g., adding a new API endpoint with frontend and backend changes, or renaming a function used across modules).

Template Prompt:

**System:** You are a software engineer who can make coordinated changes across a codebase. Always ensure consistency across all modified files.

**User:**

Implement the following feature **across the codebase**: <describe feature/change>.

**Overview:**

- **Feature:** <describe the feature or change at a high level, e.g. "Add support for filtering items by category in both backend (API) and frontend UI">.

- This will involve changes in multiple places. Specifically:

1. **Backend:** <what to change, e.g. "Create a new endpoint `/items?category=` in `@/server/routes/items.js` to filter items by category. Use the existing service function `filterByCategory` in `@/server/services/itemService.js` (if it exists; otherwise, implement it).">

2. **Database (if applicable):** <e.g. "Ensure the Item model or query supports filtering; update `@/server/models/Item.js` if needed to add a query by category index.">

3. **Frontend:** <e.g. "Add a dropdown on the Items page (`@/client/pages/ItemsPage.jsx`) for category filter. On change, call the backend API (via existing fetch utility `api.get('/items?category=...')`). Update the UI to display filtered results.">

4. **Anywhere Else:** <e.g. "Update any tests or docs that assume all items are shown, to account for filtering. Possibly update `@/tests/itemRoutes.test.js` to include a filter test.">

- **Key points:** Maintain consistent naming (use `category` uniformly on both front and back end). Reuse existing components/utilities where possible (don’t duplicate code).

- **Files to check:** I expect changes in `@/server/routes/items.js`, `@/server/services/itemService.js`, `@/client/pages/ItemsPage.jsx`, and maybe `@/client/components/ItemList.jsx` (if it renders the list). Also run tests in `@/tests` to ensure nothing breaks.

**Task:**

Make all necessary edits to implement the feature. For each file you change, provide a brief note of what was done there. Then show the final code or diff for those files.

**Verify:**

At the end, verify that the frontend build succeeds and that calling the new endpoint returns filtered results as expected (you can describe a quick manual test or assertion).

This is a more complex template, but it’s structured by listing the sub-tasks by area (backend, database, frontend, etc.). It references specific files to anchor the changes, and even mentions existing functions to use if they exist (reducing the chance the AI writes something new when it could reuse). It also explicitly reminds about consistency (naming) and reusing code.

The output section asks the AI to give a breakdown by file, which is useful for reviewing the changes. With Opus 4.5’s ability to handle multi-step reasoning and tool use, it should approach this methodically. Potentially, it might even break its answer into sections per file – which is fine. You, as the user, can then apply these changes and run tests.

If something fails, e.g., a test or a part was missed, you can go back and say “The test X is failing because of Y, please fix that.” But thanks to the detailed prompt, it’s more likely it catches the relevant spots initially.

C. Debugging & Fixing a Bug

Scenario: You have a bug in the code. Perhaps an exception is thrown, or a test is failing. You want the AI to help diagnose and fix it, possibly by adding debug logs or examining a stack trace.

Template Prompt:

**User:**

I’m encountering a bug in the application.

**Symptom:** <Describe what happens> e.g. “When I try to save a new record, I get a null pointer exception on the server.”

**Error Details:** <If you have an error log or stack trace, include the relevant part>

For example:

Exception in thread "main" java.lang.NullPointerException
at com.myapp.service.UserService.sendWelcomeEmail(UserService.java:45)
at com.myapp.api.UserController.registerUser(UserController.java:78)

(This indicates `sendWelcomeEmail` is throwing NPE.)

**Context:**

- The error happens when <context of operation, e.g. "registering a user with no email address">.

- Relevant code:

- `@/service/UserService.java` contains `sendWelcomeEmail()` (which likely is `null` on something).

- `@/api/UserController.java` calls that service after creating the user.

- We have an email service object `emailClient` that might not be initialized.

- Possibly, the `emailClient` in `UserService` is null when `sendWelcomeEmail` is called.

**Task:**

1. Identify the cause of the NullPointerException in this scenario. Consider why `emailClient` (or whatever is null) isn’t set.

2. Propose a fix. (Maybe the emailClient needs initialization, or a null check with conditional logic.)

3. Implement the fix in the code (`@/service/UserService.java` and any other affected files). Ensure that if email service is not configured, the code handles it gracefully instead of throwing an exception.

4. If needed, add a log or a warning when email sending is skipped due to missing client, so we know at runtime.

**Verification:**

Explain how the fix addresses the issue. The registration should succeed without exceptions. If email can’t send, it should fail silently (or log), not crash.

In this template, we start by clearly describing the bug and including the actual error message/stack trace. This is gold for the AI – it now has something very specific to work with, rather than a vague “it doesn’t work.” We provide context on what likely is null and why. The task steps guide it through root cause analysis to fix implementation. We specifically mention the file and function to focus on. This structure mirrors how a developer would tackle a bug: see error, find likely cause, fix, and ensure it’s handled.

By asking for identification of cause and fix, we engage Opus 4.5’s analytical skills. It might explain “The NPE happens because emailClient was never set if config flag X is false,” which is great insight. The fix might be to initialize it or add a check. Always verify after applying the fix – perhaps run the scenario again or rerun the failing test. The model’s explanation of how the fix works (which we requested) can help validate that it understood the problem correctly.

D. Building a New Component / Feature from Scratch

Scenario: You want to add a brand new component or feature, say a new class, module, or UI component that didn’t exist before.

Template Prompt:

**System:** You are an expert in our codebase’s architecture and follow its patterns for any new code.

**User:**

We want to implement a new feature: **<Feature Name>**.

**Description:** <Describe what the feature is supposed to do in user terms. E.g. "A scheduler that runs a cleanup task every night at midnight and deletes old records.">

**Requirements / Specifications:**

- The feature should be implemented as <what form, e.g. "a new class `CleanupScheduler` in `@/core/` module">.

- It needs to <list functional requirements, e.g. "load configuration for retention period (days) from app settings", "log its actions to the existing Logger", "run at 00:00 server time every day">.

- Follow the existing architectural style. For example, we have other scheduled tasks like `BackupScheduler` (see `@/core/BackupScheduler.java` for reference) – use a similar structure (perhaps implement the same interface).

- Ensure thread-safety if applicable and that it doesn’t impact runtime performance significantly.

**Plan First:**

Before coding, outline how you’ll implement **<Feature Name>**:

- What classes or functions will be created or modified.

- How they interact (e.g. "CleanupScheduler will use `RecordDAO` to delete entries").

- Any patterns to follow (e.g. using a Singleton, or a CRON library we already use).

*(Let me review the plan before proceeding.)*

(At this point, expect the model to output a plan. Once reviewed and possibly edited, you then say "Looks good, implement it." or incorporate the plan into the prompt continuation.)

User (after plan approval):
Great, now implement this feature.

Implementation Notes:

Create a new file @/core/CleanupScheduler.java with the planned functionality.
Modify any config or initialization (maybe in @/App.java to schedule the task at startup).
Add logging using our Logger class.
Please include inline comments for clarity.
After coding, show how one would verify it (maybe pseudo-code for a unit test or an example log output when it runs).

This template is extensive because building something new often is. We start with a high-level description and clear requirements. We explicitly mention similar existing components to guide the style. Crucially, we included a “Plan First” step: telling the model to propose how to implement it *before writing code*. This is exactly our earlier advice – get a plan to ensure alignment. The prompt even says we’ll review the plan, which implies to the model that it should wait (Cursor’s “Plan mode” might handle this implicitly, but in raw prompting you need to instruct it).

After the plan is reviewed (you might get something like: “Plan: 1. Create CleanupScheduler with Runnable interface… 2. Register it in App.java… 3. Add config in config.yaml…” etc.), we then confirm and tell it to implement. The second part of the prompt then asks for actual code and even verification hints.

This two-step process yields more reliable results for new components. Opus 4.5 will be less likely to miss a detail since the plan step surfaces any questions or uncertainties first (and you can correct them). For example, maybe the plan suggests a certain approach and you realize you wanted a different one – you can correct the plan before any code is written.

### E. Reading API Docs & Integrating an API

**Scenario:** You want the model to use a new external API or library. Perhaps you have documentation for it, and you want the model to implement integration code.

**Template Prompt:**

```markdown

**User:**

We need to integrate with the external API **<API Name>** in our project.

**Documentation Excerpt:** *(Given we have docs, either attach as `@APIDocs` or paste relevant parts.)*

For example, provide a summary or the parts of the API docs that are relevant, e.g. endpoints or method signatures:

<API Name> allows operations:

POST /upload {file} -> returns an ID.
GET /status/{ID} -> returns processing status.
Authentication: requires a header "Auth: Bearer <token>".
...(etc)

*(Ensure the model has enough info. If docs are huge, summarize key points yourself or ask the model to summarize first in a separate step.)*

**Context:**

- Our project is a Node.js backend. We will call <API Name> from our server code (e.g., in `@/server/services/ExternalService.js`).

- We have an API key stored in environment as `EXTERNAL_API_KEY`.

- We use Axios for HTTP requests (already included in the project).

- The integration will be used in our data processing pipeline: after we receive a file upload from user, we should upload it to <API Name> and poll for result.

**Task:**

Implement a new module `@/server/services/<APIName>Client.js` that:

1. Provides a function `uploadFileToExternal(filePath: string): Promise<result>`:

- Reads the file (if needed) and sends it via `<API Name>`’s upload endpoint.

- Includes the auth header.

- Parses the response to get the ID.

2. Provides a function `checkStatus(id: string): Promise<status>` that:

- Calls the status endpoint, returns the status (perhaps "processing", "done", etc.).

3. Handles errors (e.g., network issues or non-200 responses) gracefully – maybe throw a custom error with details.

- Use Axios for HTTP calls. Base URL is `<given by docs or config>`.

- Include inline comments referencing the API docs for clarity (like “// as per API: returns { id: ... }”).

After that, modify `@/server/controllers/ProcessController.js` to:

- Call `uploadFileToExternal` when a new file is received (for example, after saving file locally, then call external).

- Then periodically call `checkStatus` (you can use a simple polling or note TODO for converting to an async job).

**Verify:**

Provide an example usage:

```js

const client = require('../services/<APIName>Client');

client.uploadFileToExternal('/tmp/test.png')

.then(id => client.checkStatus(id))

.then(status => console.log("Status:", status))

This should log a status like "done" eventually.

This prompt heavily leverages documentation and context. We outline exactly what needs to be done and even structure the output (two functions in a new file, etc.). The docs snippet is provided so the model doesn’t hallucinate endpoints or payloads. If the docs are very long, a good idea (not fully shown above) is to ask the model to summarize them first or extract only the needed parts. But assuming we have relevant parts, we feed them in.

By being explicit about our tech (Node.js, Axios, env var for API key), we ensure the model writes code that fits our project. We also break the task: implement client module, then integrate in controller.

The verify section gives a usage example. This not only helps us later to test but also helps the model check its own design. If it can mentally simulate that usage, it might catch errors (for example, forgetting to export functions, etc.). This aligns with instructing the model to think of how to call its own code.

When integrating APIs, clarity is key: many models hallucinate parameters or mis-order them. By giving concrete info from docs (like required headers, endpoints), we anchor it. If we didn’t have the docs, we’d ask it to assume or draft, but since it’s best practice to be accurate, providing docs or at least stating “If unsure, make a reasonable assumption and mark it with TODO” could be an alternative. But since the question prompt encourages using sources, presumably we have the docs.

### F. Architecture / Design Outline Generation

**Scenario:** You want the AI to propose a high-level design or architecture for a new project or a major component, without necessarily writing all the code yet.

**Template Prompt:**

```markdown

**User:**

We are starting development on a new module: **<Module Name>**.

**Goal:**

**Requirements:**

- <Bullet list of requirements: e.g. "Support multiple chat rooms", "Persist chat history in database", "Notify users of new messages in real-time via websockets", "Moderation tools to delete messages", etc.>

- Must integrate with our existing system (briefly mention relevant existing parts, e.g. "User authentication is handled by the core module, so reuse that for identifying users").

- Should be designed for scalability (potentially many concurrent connections).

**Task:**

Outline a proposed architecture/design for **<Module Name>**:

- List the main components or classes you would create (e.g. `ChatServer` WebSocket gateway, `ChatRoomManager`, `MessageRepository` for DB).

- Explain how data flows between them (e.g. "Client connects -> ChatServer authenticates -> joins ChatRoom instance -> messages go to MessageRepository").

- Mention any design patterns or best practices used (e.g. use Observer pattern for notifying listeners, use DAO for database).

- If applicable, outline how you’d structure the database (new tables or collections for chats and messages).

- Keep the design consistent with our project’s tech stack (our backend is Django, so maybe use Django Channels for websockets, etc.).

- Consider error handling and security (e.g. how to prevent unauthorized access to chat rooms).

We are not coding it fully yet, just want a clear plan.

**Output:**

Provide the design as a structured breakdown (you can use headings or bullet points). You can include brief pseudo-code or class definitions to illustrate, but focus on clarity of structure and reasoning.

In this template, we explicitly ask for an architecture outline. It’s similar to the planning approach but at a higher level. The model should produce something like an enumerated design with components and their roles. This is useful early in a project or for confirming approach on a complex addition.

Opus 4.5 is quite strong in reasoning and can produce coherent designs. By listing requirements and context, we ensure it addresses them. The bullet prompts (components, data flow, patterns, etc.) nudge it to cover all angles. The mention of existing tech stack prevents it from suggesting something out of left field (like if our project is in Django, it shouldn’t suggest Node.js socket server – it should stick to Django’s way, for example).

This kind of output might not be directly executable code, but it’s invaluable for discussion and planning. You can take the design and decide to implement it (possibly asking the AI to then implement each part in subsequent prompts). This again leverages the AI’s strength in high-level reasoning and ensures when you do code, you have a blueprint to follow or give to the model.

G. Test Case Generation

Scenario: You have existing code and want to generate tests for it, or you want tests for a new feature either before or after implementation.

Template Prompt:

**User:**

Generate unit tests for the following function:

```python

# @/utils/date_utils.py

def parse_date(date_str: str) -> datetime.date:

"""

Parses a date string in format YYYY-MM-DD to a date object.

"""

if date_str is None:

raise ValueError("date_str cannot be None")

parts = date_str.split("-")

if len(parts) != 3:

raise ValueError(f"Invalid date format: {date_str}")

year, month, day = map(int, parts)

return date(year, month, day)

Instructions:

Create a new test module @/tests/test_date_utils.py with test cases for parse_date.
Include tests for:

A valid date string (e.g. "2025-12-31") -> assert correct date object.
An invalid format (like "31-12-2025" or "2025/12/31") -> expect ValueError.
Edge cases: None input -> expect ValueError; empty string -> expect ValueError; out-of-range values (e.g. "2025-13-01") -> it will raise ValueError (from int conversion or date constructor) – ensure that’s tested.

Use Python’s unittest or pytest style (our project uses pytest). So write functions test_parse_date_valid(), test_parse_date_invalid_format(), etc.
Do not actually import the code above in this prompt (just assume it’s available in date_utils). Focus on logical tests.

Output:
Provide the content of test_date_utils.py with the test functions.

This template directly feeds a piece of code and asks for tests. Note that we explicitly list the scenarios to test, which is good practice to ensure the AI doesn’t miss any. We indicate what framework (pytest) to use, so it will produce functions rather than classes (if unittest style, we’d instruct accordingly).

Opus 4.5 will likely follow this and produce nice test cases, including assertions for exceptions using `pytest.raises` or similar. By giving the code in the prompt, we allow it to reason precisely about what the function does and what edge cases exist. (Opus is quite capable of reading and understanding code to derive tests – this uses its “coding partner” strength.)

When doing this in Cursor, you could also attach the file `@/utils/date_utils.py` in the prompt, and instruct it to create `@/tests/test_date_utils.py`. Cursor might even offer a built-in “generate tests” command, but in manual prompting, this template works well.

After getting the tests, you should run them! If one fails, that might actually indicate a bug in the original function or an oversight. This is great because you can then either fix the code or adjust the test as needed. You might even chain: first have it generate tests for existing code (to strengthen safety), then have it run those tests (Cursor can run if YOLO mode allowed or you copy logs), then feed failures back for fixes. This is essentially AI-assisted TDD or bug-fixing cycle.

---

Each template above is written in Markdown-ish style since the user requested Markdown formatting and they read like “prompts” you’d give to the AI within Cursor. You, the user of Cursor+Opus, would adapt these to your specific function names, file paths, etc. The idea is to illustrate how to structure requests for different purposes in a way that sets the AI up for success.

## Daily-Use Cheat Sheet for Cursor + Opus 4.5

Finally, here’s a concise cheat-sheet summarizing tips and commands for day-to-day use of Cursor with Opus 4.5:

- **Start of Session Setup:** Always begin a new Composer session by **attaching key context**:

- Attach the relevant files (using `@file` references) that you anticipate needing.

- Attach a Notepad or `.cursorrules` with project rules or a summary of what you’re building.

- Provide a quick “You are X and will do Y” instruction to prime model behavior (e.g. “You are an AI coding assistant helping with a Flask web app. Follow PEP8 and our project conventions.”).

- **Use Cmd+K for quick fixes:** Select code and press Cmd+K to get an inline prompt for targeted edits. This is great for small modifications in one file without invoking the full agent context on everything. It reduces overhead and scope.

- **Use Cmd+I to discuss specific code:** Select code and press Cmd+I to open the agent with that code chunk preloaded. Use this when you want to ask “why is this code doing X?” or “can you improve this snippet?” It confines context to that snippet, making responses faster and more relevant.

- **Leverage Cursor’s knowledge base:** Cursor’s agent can often **self-gather context** like definitions or related files (especially in v2.0+ where explicit context menu items were removed). So you can ask in natural language, e.g. “Find where `sendWelcomeEmail` is defined and see if it’s being called correctly.” The agent might pull in the relevant file automatically. If it doesn’t, you can quickly open that file and re-ask.

- **Prefer Composer (Agent) for implementations, Chat for Q&A:** Use the **Agent panel** (Composer) when you want the AI to actually modify code or perform multi-step tasks. Use the simpler Chat panel for conceptual questions or isolated help (like “Explain this error message” or “What does this regex do?”). This separation helps because Agent mode carries full project context and might be slower/costlier; Chat is quick and stateless.

- **Keep prompts clear and styled:** In your prompts, use Markdown **bullet lists** or **numbered steps** to communicate instructions clearly (the model actually parses this structure well). For example, structure a request as:

- “1. Do X

2. Then do Y

3. If Z happens, do Q.”

This reduces ambiguity. Opus 4.5 is trained to follow sequential instructions precisely:contentReference[oaicite:117]{index=117}.

- **Review changes in the diff viewer:** After the agent makes changes, use Cursor’s diff or the source control tab to review all modifications before running. This lets you catch any “extra” changes the AI might have snuck in. If something looks off, you can ask, “Why did you change this line? It wasn’t part of the request.” Often the AI might explain or revert if it was unnecessary.

- **Use “Plan mode” for complex tasks:** If you’re unsure how to even begin a task, toggle **Plan mode** (if available in your Cursor version) to let the AI break it down. Or manually prompt for a plan. This is the cure for the “where do I start?” paralysis – the AI will likely produce a sensible outline which you can then implement step by step (with its help on each step as needed).

- **Know your models and costs:** Opus 4.5 is powerful but also more expensive per token. For trivial tasks or massive boilerplate generation, you could switch to a cheaper model (like Claude Sonnet 4 or OpenAI Codex if integrated) to save tokens. Conversely, for gnarly problems, stick with Opus 4.5’s “thinking” mode. Cursor’s Auto mode often picks a model for you, but be aware: some community feedback suggests Opus in Cursor can run up bills quickly if left in high-effort loops. Keep an eye on usage if on a budget.

- **Git is your safety net:** Commit your code before major AI interactions. If the AI does something crazy (it can happen!), you can reset to last commit easily. The mantra “commit early, commit often” is doubly true with AI-assisted coding:contentReference[oaicite:121]{index=121}. Also, use branches for experimentation – let the AI work on a branch, and you merge if satisfied.

- **Stop when uncertain:** If the AI’s output seems off or it starts struggling (e.g., long pauses or repeated attempts), intervene. Clarify the prompt or break the task down further. Don’t just keep hitting regenerate hoping it fixes itself. Usually a small nudge or context tweak from you can resolve the impasse.

- **Use /summarize or /grep for large context:** Cursor has commands like `/summarize` to condense a long file or `/grep` to search within the code. Use these to help the model handle large files. For instance, if you have a 1000-line file but the bug is likely in one function, use `/grep functionName` and feed only that portion to the AI, or summarize irrelevant parts to avoid hitting context limits.

- **Stay engaged and guide the AI:** The “cursor” in Cursor is *you* as much as the tool – keep guiding the AI as if pair programming. Opus 4.5 responds well to an interactive approach: ask it to explain if you don’t understand a change, have it verify its own work, and provide feedback. The more you treat it like a collaborator (albeit one that needs direction), the better the outcomes.

This cheat-sheet can be a quick reference to remind you of the best practices each time you sit down to code with Cursor and Opus 4.5. Over time, many of these will become second nature.

## Safe-Mode Prompt for Large Repos (Minimal-Risk Settings)

When working with a **large, critical codebase** where you want to minimize any risky changes, you should configure the model to act conservatively and verify everything. Here’s an example “safe mode” system prompt and approach:

**Safe Mode System Prompt:**

```markdown

<do_not_act_before_instructions>

You are to act as a careful code assistant.

- Do **NOT** make any code modifications unless explicitly directed to.

- When given a task, first explain how you plan to solve it, and wait for confirmation.

- Double-check the repository for existing utilities or functions before writing new code.

- If there is any ambiguity or missing information, ask clarifying questions rather than making assumptions.

- Prioritize not breaking existing functionality. Any change should be the minimal necessary.

- Validate your output logically: if you're adding code, reason about how it fits with existing code.

</do_not_act_before_instructions>

When you have this in place (either by putting it in the “Rules for AI” or prepending to your prompt in Cursor), the model will default to a cautious stance. On a large repo, you’d then interact like so:

Ask for a plan: “The task is to upgrade the payment system API calls. How should we approach this?” Expect the AI to outline steps.
Review thoroughly: Confirm each step, or adjust. Perhaps even have it find references: “Search the codebase for PaymentService usage to ensure we don’t miss anything.”
Step-by-step execution: Only after you’re satisfied that the plan won’t wreck things, say “Proceed with Step 1,” and so on. This one-by-one execution prevents massive changes in one go.
Diff and test at each step: After each step the AI does, review the diff. If it’s a big repo, maybe run a subset of tests to ensure nothing broke. Then move on.

The safe-mode prompt basically tells Opus 4.5 to be on its best behavior: no impulsive coding, always ask if unsure, lean towards information and caution. The line “if the user’s intent is unclear, default to providing info and recommendations” is essentially what we set by instructing it to ask clarifications.

This mode is useful for large enterprise codebases where a wrong change could be costly. It ensures the AI becomes more of an advisor and editor rather than an autonomous agent. Think of it as putting the AI in --dry-run mode unless you explicitly say “yes, do it.”

Practically, in Cursor you might not want to use such a verbose system blurb every time. Instead, you could encode similar rules in a .cursorrules file for that repo: e.g. “All AI suggestions must be reviewed; do not make changes without approval; prefer asking when in doubt; use existing code first.” The effect is similar.

Power-Mode Prompt for Rapid Development

On the flip side, if you’re in a prototyping phase or working on a throwaway branch where speed is more important than caution, you can unleash a “power mode” prompt. This will encourage the AI to take initiative, make reasonable assumptions, and even create new code where needed without constant approval. Use this only when you’re comfortable reviewing and possibly undoing changes, since it’s more “aggressive.”

Power Mode System Prompt:

<default_to_action>

You are an autonomous coding agent with full permission to implement changes proactively.

- By default, take actions to implement the user’s requests without asking for confirmation.

- If something is unclear, make an educated guess and proceed (flagging assumptions in comments).

- Use tools freely: read files, write files, run tests as needed to accomplish the task.

- Optimize for speed of development: it’s okay to draft a solution and then refine it.

- You can modify multiple files and create new ones if it seems necessary to achieve the goal.

- Always ensure that after implementation, you test or verify the solution works (e.g. run relevant tests).

</default_to_action>

With this prompt active, Opus 4.5 will be inclined to “do the obvious thing” to fulfill your request. This is reminiscent of Cursor’s YOLO mode usage plus the Anthropic recommended proactive stance. It will perform more like an agent you don’t need to babysit constantly – good for fast experimentation.

For example, if you say “Add a blog feature to our app,” in power mode it might: create a Blog model, migration, templates, etc., all in one go, and maybe even run npm run migrate if allowed. It will assume things rather than stop. You might get a lot done quickly – but be prepared to find and fix some issues.

Usually, you would use power mode in combination with plan mode: the AI might generate a plan and then execute it fully without intermediate confirmations, using a high token allowance. Users have noted that combining a strong model like Opus 4.5 with such autonomous behavior “just changed everything” in terms of speed – but of course it comes with the need to trust-but-verify.

If you go this route, it’s wise to set up some safety nets:

Perhaps use a separate test environment or branch.
Make sure you have version control snapshots.
Possibly limit the scope by saying “only in the experimental/ folder” or similar if you fear it touching core code.

In Cursor, you don’t have an actual toggle for “safe” vs “power” per se (aside from YOLO and summarization settings), but these system directives achieve similar effects. Also, model choice can play a role – some users found that certain models (like Gemini or GPT-4 Codex) might perform differently in plan vs act mode. But since we focus on Opus 4.5: know that Opus was built for agentic workflows and can be amazingly effective in this autonomous modeanthropic.com. It was literally described as “excels at heavy-duty agentic workflows… tasks like code migration and refactoring”anthropic.com, using far fewer tokens due to its more efficient planning. This means if any model can handle a “please just do it end-to-end” prompt, Opus 4.5 can – provided the instructions are clear.

So, use this power responsibly. When deadlines loom and you need that component up by yesterday, you might unleash power mode; when it’s your production code, maybe dial it back to safe mode.

Throughout both safe and power modes, remember that you can always adjust mid-flight. If you started in power mode and you see chaos brewing, you can literally paste in the safe-mode snippet and the model will adapt (Opus 4.5 will take system prompt updates into account swiftly). Or vice versa, if it’s being too timid, give it the green light with a default_to_action injection.

Conclusion & Final Thoughts

Using Anthropic’s Claude Opus 4.5 inside Cursor is a bit like driving a high-performance sports car – incredibly powerful, but requiring skill to handle well. By following the best practices outlined – from explicit prompting and context management to leveraging Cursor’s tooling (plans, notepads, commands) – you can direct that power with precision. Community consensus (from Reddit, HN, and Cursor forums) shows that when things go wrong, it’s usually due to unclear instructions or overreliance on the AI without verification. Opus 4.5 is less prone to hallucination and error than many models, especially in coding tasks, but it’s not magic. You as the developer are still the pilot.

Encouraging signs: many have said Opus 4.5 “just gets it” for complex coding problems, especially when it has a clear goal. It can handle very large contexts and long coding sessions that earlier models would flounder on. Its improvements in reasoning mean it is more likely to follow your careful prompt exactly, and even self-correct issues (for example, using git or tests to track its progress). Users have achieved impressive feats like multi-agent coordinated refactors across a codebase with Opus 4.5. All of that is attainable for you too, by steadily applying the prompting techniques we discussed.

To quickly recap the Top 5 takeaways:

Always be explicit – tell it what to do, what not to do, where to do it, and how to format itplatform.claude.com.
Break tasks down – use planning, step-by-step prompting, or Plan mode to avoid overwhelming the model.
Use tests/logs as truth source – anchor the model’s work with verifiable criteria to avoid hallucinations and ensure correctness.
Keep context relevant – provide the right files and info; avoid letting the session drift; utilize notepads and context resets for lengthy jobs.
Review and iterate – don’t treat the AI as fire-and-forget. Check diffs, run your app, ask follow-ups. Guide it like a junior dev (albeit a superhumanly fast one).

By following these practices, you’ll find that Opus 4.5 in Cursor truly lives up to its promise as a formidable coding partner – one that can transform how you develop software, while you remain confidently in control of the code’s quality and direction.

Sources: The guidance above synthesizes information from Anthropic’s official Claude 4.5 documentation on prompt engineeringplatform.claude.com, Cursor’s own community forums and docs (on handling context, planning, and model behavior), as well as real-world tips shared by users on Reddit, Hacker News, and blogs (covering strategies like test-driven prompting, adding debug logging, managing long sessions, and using Cursor features effectively). These collective insights shape a robust approach to maximizing Opus 4.5’s capabilities while mitigating its limitations. Each recommendation was chosen based on recurring success patterns reported and the known strengths of the Claude model family in coding contexts. With this knowledge in hand, you are set to code smarter, faster, and more safely with Cursor + Opus 4.5.

Sources

Gem instructions

You are the Cursor Opus 4.5 Architect. Your goal is to help the user write the perfect prompts and configurations to control Claude Opus 4.5 within the Cursor IDE. You act as a "High-Reasoning" supervisor.

Your Core Philosophy:Opus 4.5 is the strongest reasoning model available, but it requires "tight rails" to avoid over-engineering or hallucinations. You must enforce the "Plan → Apply" workflow.

When the user asks for help, follow these guidelines:

1. Enforce the "Brutal Truths" of Prompting:

Plan-First: Always advise the user to demand a numbered plan (1-N) before allowing the Agent to edit code.

Explicit Scoping: Reject vague prompts. Remind the user to list specific files (@file1, @file2) rather than letting the agent guess.

Diff-Only Output: Always structure prompts to ask for "Unified Diff format" only, to prevent lazy deletion or full-file rewrites.

2. Select the Right Mode:

Safe Mode (Default): For large repos. Instruct Opus to act as a "conservative senior engineer" who never modifies unlisted files or invents imports.

Power Mode: For prototypes. Instruct Opus to act as an "elite full-stack engineer" who maximizes parallelism and defaults to action.

3. Use Specific Prompt Templates:

If the user asks for a specific task, generate a prompt based on these templates:

Refactoring: "First read relevant files. Goal: [Goal]. Constraints: Do not change public API. Output a numbered plan 1-N. Wait for proceed.".

Debugging: "Reproduce error locally using bash tool. Stack trace: [Paste]. Relevant files: [List]. Propose minimal fix as diff.".

New Feature: "Match the style of [Reference Component]. Exact props interface: [Paste]. Output only new file content.".

4. Project Configuration Advice:

Remind the user to use a .cursorrules file to define the "Project Bible".

Advise using .cursorignore to block node_modules and build artifacts to prevent "Context Poisoning".

5. Immediate Interventions:If the user describes Opus getting stuck in a loop, advise them to immediately send: "Stop. Summarize what you have done so far and list open questions.".