
Karpathy Guidelines
- 10k installs
- 200k repo stars
- Updated April 20, 2026
- multica-ai/andrej-karpathy-skills
This is a copy of karpathy-guidelines by forrestchang - installs and ranking accrue to the original listing.
karpathy-guidelines is an MIT-licensed agent skill that embeds Andrej Karpathy's coding principles into LLM behavior for developers who want cleaner, simpler, and more reliable agent-generated code.
About
karpathy-guidelines is an MIT-licensed agent skill from multica-ai/andrej-karpathy-skills that embeds Andrej Karpathy's observations on common LLM coding mistakes directly into agent behavior. It instructs agents to think before coding, avoid overcomplication, make surgical changes, surface assumptions and tradeoffs, and define verifiable success criteria during writing, review, and refactoring. The guidelines bias toward caution over speed—developers apply judgment on trivial tasks. karpathy-guidelines activates whenever agents might assume requirements, hide confusion, or produce bloated diffs. Reach for it when agent output feels over-engineered, changes scope creep beyond the request, or lacks explicit success checks before merge.
- 7 core behavioral guidelines derived from Andrej Karpathy’s LLM coding observations
- Explicit assumption surfacing before any implementation begins
- Enforces surgical changes and forbids speculative features or over-abstraction
- Requires verifiable success criteria and simplicity-first mindset
- Reduces common LLM pitfalls such as overcomplication and hidden tradeoffs
Karpathy Guidelines by the numbers
- 10,007 all-time installs (skills.sh)
- +689 installs in the week ending Aug 5, 2026 (Skillselion tracking)
- Security screen: LOW risk (skills.sh audit)
- Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/multica-ai/andrej-karpathy-skills --skill karpathy-guidelinesAdd your badge
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| Installs | 10k |
|---|---|
| repo stars | ★ 200k |
| Security audit | 3 / 3 scanners passed |
| Last updated | April 20, 2026 |
| Repository | multica-ai/andrej-karpathy-skills ↗ |
How do you reduce common LLM coding mistakes?
Embed Andrej Karpathy’s coding principles directly into agent behavior for cleaner, simpler, and more reliable code output.
Who is it for?
Developers using AI coding agents who want Karpathy-inspired guardrails against overcomplication, hidden assumptions, and unverifiable agent output.
Skip if: Teams needing domain-specific framework lint rules, infrastructure runbooks, or tasks where speed outweighs cautious surgical editing.
When should I use this skill?
The user writes, reviews, or refactors code with an agent and wants to prevent overcomplication, scope creep, or unverified changes.
What you get
Smaller focused diffs, documented assumptions, stated tradeoffs, and explicit verifiable success criteria before code merges.
- Minimal focused code diffs
- Documented assumptions and tradeoffs
Files
Karpathy Guidelines
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.
1. Think Before Coding
Don't assume. Don't hide confusion. Surface tradeoffs.
Before implementing:
- State your assumptions explicitly. If uncertain, ask.
- If multiple interpretations exist, present them - don't pick silently.
- If a simpler approach exists, say so. Push back when warranted.
- If something is unclear, stop. Name what's confusing. Ask.
2. Simplicity First
Minimum code that solves the problem. Nothing speculative.
- No features beyond what was asked.
- No abstractions for single-use code.
- No "flexibility" or "configurability" that wasn't requested.
- No error handling for impossible scenarios.
- If you write 200 lines and it could be 50, rewrite it.
Ask yourself: "Would a senior engineer say this is overcomplicated?" If yes, simplify.
3. Surgical Changes
Touch only what you must. Clean up only your own mess.
When editing existing code:
- Don't "improve" adjacent code, comments, or formatting.
- Don't refactor things that aren't broken.
- Match existing style, even if you'd do it differently.
- If you notice unrelated dead code, mention it - don't delete it.
When your changes create orphans:
- Remove imports/variables/functions that YOUR changes made unused.
- Don't remove pre-existing dead code unless asked.
The test: Every changed line should trace directly to the user's request.
4. Goal-Driven Execution
Define success criteria. Loop until verified.
Transform tasks into verifiable goals:
- "Add validation" → "Write tests for invalid inputs, then make them pass"
- "Fix the bug" → "Write a test that reproduces it, then make it pass"
- "Refactor X" → "Ensure tests pass before and after"
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.
Related skills
How it compares
Pick karpathy-guidelines over language-specific lint skills when the problem is agent behavior and diff discipline rather than syntax or framework rules.
FAQ
When should developers apply karpathy-guidelines?
karpathy-guidelines applies when writing, reviewing, or refactoring code with an AI agent. It instructs agents to state assumptions, avoid overcomplication, make surgical edits, and define verifiable success criteria before finishing a task.
Does karpathy-guidelines slow down trivial agent tasks?
karpathy-guidelines biases toward caution over speed by design. Developers should use judgment on trivial tasks—the skill targets situations where LLM overcomplication, hidden confusion, or unverifiable changes create real merge risk.
Is Karpathy Guidelines safe to install?
skills.sh reports 3 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.