Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, modifying, or reviewing code — implementation tasks, code changes, refactoring, bug fixes, or feature development. Do NOT use for architecture design, documentation, or non-code tasks.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versioncoding-guidelinesExecute the skills CLI command in your project's root directory to begin installation:
Fetches coding-guidelines from tech-leads-club/agent-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate coding-guidelines. Access via /coding-guidelines in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Use skill to generate boilerplate code, refactor legacy code, and write tests faster
Example
Generate React component with TypeScript types, styled-components, and comprehensive test suite in minutes
Reduce development time by 40-60% for repetitive coding tasks
Systematically review code for bugs, security issues, and style violations
Example
Analyze pull requests for common anti-patterns, suggest performance improvements, flag security vulnerabilities
Catch 70%+ of code issues before human review, improve code quality
Trace errors through stack traces and identify root causes faster
Example
Analyze error logs, suggest probable causes, recommend fixes with code examples
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| name | coding-guidelines |
| description | Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, modifying, or reviewing code — implementation tasks, code changes, refactoring, bug fixes, or feature development. Do NOT use for architecture design, documentation, or non-code tasks. |
| metadata | author: ale version: '1.0.0' source: 'Karpathy Guidelines' |
Behavioral guidelines to reduce common LLM coding mistakes. These principles bias toward caution over speed—for trivial tasks, use judgment.
Don't assume. Don't hide confusion. Surface tradeoffs.
Before implementing:
Minimum code that solves the problem. Nothing speculative.
Ask yourself: "Would a senior engineer say this is overcomplicated?" If yes, simplify.
Touch only what you must. Clean up only your own mess.
When editing existing code:
When your changes create orphans:
The test: Every changed line should trace directly to the user's request.
Define success criteria. Loop until verified.
Transform tasks into verifiable goals:
For multi-step tasks, state a brief plan:
1. [Step] → verify: [check]
2. [Step] → verify: [check]
3. [Step] → verify: [check]
Strong success criteria let you loop independently. Weak criteria ("make it work") require constant clarification.
Cut debugging time by 30-50%, especially for unfamiliar codebases
Get explanations, examples, and best practices for unfamiliar frameworks
Example
Understand Next.js app router, learn Rust ownership, grasp Kubernetes concepts with practical examples
Accelerate learning curve by 2-3x, reduce onboarding time for new tech stacks
Prerequisites
Time Estimate
15-30 minutes to install and see first useful output
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use coding skills for boilerplate generation, code reviews, refactoring legacy code, writing tests, learning new frameworks, and debugging non-critical issues. Best for repetitive tasks where errors are easy to catch.
✗ Avoid when
Avoid for production security features (auth, encryption, payment processing), complex business logic requiring deep domain knowledge, performance-critical algorithms, or when learning fundamentals is more valuable than speed.
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Useful defaults in coding-guidelines — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
coding-guidelines has been reliable in day-to-day use. Documentation quality is above average for community skills.
Registry listing for coding-guidelines matched our evaluation — installs cleanly and behaves as described in the markdown.
coding-guidelines is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
I recommend coding-guidelines for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
We added coding-guidelines from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Solid pick for teams standardizing on skills: coding-guidelines is focused, and the summary matches what you get after install.
coding-guidelines reduced setup friction for our internal harness; good balance of opinion and flexibility.
Keeps context tight: coding-guidelines is the kind of skill you can hand to a new teammate without a long onboarding doc.
Registry listing for coding-guidelines matched our evaluation — installs cleanly and behaves as described in the markdown.
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