CU

cursor-debug-bundle

Diagnostic guide to improve Cursor AI suggestion quality and fix incorrect code generation.

Install

mkdir -p .claude/skills/cursor-debug-bundle && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/1176" && unzip -o skill.zip -d .claude/skills/cursor-debug-bundle && rm skill.zip

Installs to .claude/skills/cursor-debug-bundle

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

Debug AI suggestion quality, context issues, and code generation problems
73 charsno explicit “when” trigger
Intermediate

Key capabilities

  • Diagnose AI suggestion quality issues in Cursor
  • Identify root causes for wrong code generation, such as context or model problems
  • Configure project rules to specify tech stack versions
  • Use `@Docs` and `@Files` to provide specific context to the AI
  • Debug context window overflow by starting new chats or reducing `@` references
  • Analyze developer tools logs and enable verbose logging for detailed diagnostics

How it works

The skill provides a diagnostic framework to categorize AI suggestion issues into context, model, or prompt problems. It then offers specific fixes and a systematic workflow for debugging.

Inputs & outputs

You give it
Cursor AI suggestions, generated code, and user instructions
You get back
Identification of AI generation issues and steps to improve AI suggestion quality

When to use cursor-debug-bundle

  • Debugging incorrect API usage in AI suggestions
  • Troubleshooting context window overflow issues
  • Refining project-wide AI rules
  • Identifying hallucinated library versions
  • Improving AI prompt instruction quality

About this skill

Cursor Debug Bundle

Diagnose and fix AI suggestion quality issues in Cursor. Covers why AI generates wrong code, how to improve context, and systematic debugging workflows.

Diagnostic Framework

When AI suggestions are wrong, the cause is almost always one of these:

┌─ Context Problems (80% of issues) ───────────────────────┐
│  1. Missing context: AI doesn't have the relevant code   │
│  2. Wrong context: AI has stale or irrelevant files      │
│  3. Too much context: context window overflow            │
│  4. No project rules: AI doesn't know your conventions   │
└──────────────────────────────────────────────────────────┘
┌─ Model Problems (15% of issues) ─────────────────────────┐
│  5. Wrong model for the task                             │
│  6. Model hallucinating APIs or patterns                 │
└──────────────────────────────────────────────────────────┘
┌─ Prompt Problems (5% of issues) ─────────────────────────┐
│  7. Ambiguous or vague instructions                      │
│  8. Conflicting requirements in prompt                   │
└──────────────────────────────────────────────────────────┘

Debugging by Symptom

AI Uses Wrong API or Library Version

Symptom: Generated code uses React.createClass, old Express syntax, or deprecated patterns.

Root cause: Model training data includes old code. No project rules specifying versions.

Fix:

# .cursor/rules/stack-versions.mdc
---
description: "Tech stack version pinning"
globs: ""
alwaysApply: true
---
# Stack Versions (ALWAYS use these)
- React 19 with Server Components (NOT class components)
- Next.js 15 App Router (NOT Pages Router)
- TypeScript 5.7 strict mode
- Prisma 6 (NOT Sequelize or TypeORM)
- Tailwind CSS 4 (NOT styled-components)
- Node.js 22 (ESM, NOT CommonJS require())

AI Generates Code That Doesn't Match Your Patterns

Symptom: Generated code uses different naming, structure, or patterns than your codebase.

Root cause: AI does not have your existing code as context.

Fix: Reference your existing patterns explicitly:

@src/api/users/route.ts

Create src/api/products/route.ts following the EXACT same patterns:
same error handling, same response format, same validation approach.

AI Hallucinates Non-Existent Functions

Symptom: AI calls functions or uses imports that do not exist in your project or in the library.

Root cause: Model confuses similar libraries or invents plausible-sounding APIs.

Fix:

  1. Add @Docs context: @Docs Prisma gives the AI real API documentation
  2. Verify generated imports: run npm run build immediately after applying
  3. Use @Files to show the actual module interface: @src/lib/database.ts

AI Ignores Your Instructions

Symptom: You ask for one thing, AI does something different.

Root cause: Context window overflow -- your instructions get pushed out by file contents.

Fix:

  1. Start a new chat (Cmd+N) -- conversation history may be using too much context
  2. Use fewer @ references -- each consumes context budget
  3. Put instructions at the end of the prompt (models attend more to recent text)
  4. Use @Files instead of @Codebase to reduce context volume

Tab Completion is Repetitive or Wrong

Symptom: Tab keeps suggesting the same wrong pattern.

Root cause: Tab has limited context compared to Chat/Composer.

Fix:

  1. Open related files in editor tabs (Tab reads open tabs)
  2. Add a comment above your cursor describing what you want
  3. Reject bad suggestions with Esc (trains the model)
  4. If persistently wrong, use Cmd+K inline edit instead

Systematic Debug Workflow

When AI output is consistently wrong:

Step 1: Check context
  - Open Chat, look at context pills at top
  - Are the right files included?
  - Are stale files adding noise?

Step 2: Check rules
  - @Cursor Rules in chat -- what rules are active?
  - Do rules conflict with each other?
  - Are glob patterns matching the right files?

Step 3: Test with minimal context
  - Start new chat
  - Add ONLY the most relevant file: @src/the-file.ts
  - Ask your question with explicit constraints
  - If this works, the issue was context pollution

Step 4: Test with different model
  - Switch from Sonnet to Opus or GPT-5
  - If better model gives better results, the task needs more reasoning power

Step 5: Check indexing
  - Is the codebase indexed? (status bar shows "Indexed")
  - Is the relevant file excluded by .cursorignore?
  - Run Cmd+Shift+P > "Cursor: Resync Index"

Logs and Diagnostics

Opening Developer Tools

Cmd+Shift+P > Developer: Toggle Developer Tools

Check the Console tab for:

  • API request errors (red)
  • Context assembly logs
  • Extension errors

Verbose Logging

Enable verbose output: Cursor Settings > search "log level" > set to "Debug"

Logs location:

  • macOS: ~/Library/Application Support/Cursor/logs/
  • Linux: ~/.config/Cursor/logs/

Reproducing Issues for Bug Reports

1. Note Cursor version: Help > About
2. Note model used (from Chat/Composer header)
3. Copy the exact prompt that produced wrong output
4. Copy the wrong output
5. List active extensions: Cmd+Shift+P > "Extensions: Show Installed"
6. Note if Privacy Mode is on (affects model behavior)
7. Report at forum.cursor.com or github.com/getcursor/cursor/issues

Enterprise Considerations

  • Quality baselines: Track AI suggestion accuracy over time per team/project
  • Model pinning: If a model update degrades quality, temporarily switch to a different model while reporting
  • Rule audits: Periodically review .cursor/rules/ for outdated or conflicting rules
  • Training: Ensure team knows the difference between context, model, and prompt issues

Resources

When not to use it

  • When the issue is not related to AI suggestion quality, context, or code generation problems

Limitations

  • Tab completion has limited context compared to Chat/Composer

How it compares

This skill offers a structured diagnostic framework and specific configuration examples for Cursor AI, contrasting with general AI debugging that lacks tool-specific guidance.

Compared to similar skills

cursor-debug-bundle side by side with the closest alternatives in the catalog.

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