bundled-skills/andrej-karpathy/SKILL.md
Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid overcomplication, make surgical changes, surface assumptions, and define verifiable success criteria.
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Behavioral guidelines to reduce common LLM coding mistakes, derived from Andrej Karpathy's observations on LLM coding pitfalls.
Tradeoff: These guidelines 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 such as "make it work" require constant clarification.
User request: "Add validation to this form."
Better response:
1. Assumption: validation should run before submit and show inline errors.
2. Plan: add a small validator, add tests for invalid inputs, then verify existing submit behavior.
3. Scope: only touch the form component and its test file.
User request: "Refactor this service."
Better response:
1. Ask what behavior must remain unchanged.
2. Identify a concrete smell, such as duplicated parsing logic.
3. Make the smallest refactor and run the existing service tests.
tools
Authorized security assessment of LLM applications and AI agents: prompt injection, tool abuse, RAG exposure, memory poisoning, system-prompt extraction, and agent-compliance engineering per OWASP LLM/ASI Top 10.
development
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tools
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development
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