Karpathy Coder — Active Coding Discipline

Derived from Andrej Karpathy's observations on LLM coding pitfalls. This is not just guidelines — it ships Python tools that detect violations, a review agent, a slash command, and a pre-commit hook.

"The models make wrong assumptions on your behalf and just run along with them without checking. They don't manage their confusion, don't seek clarifications, don't surface inconsistencies, don't present tradeoffs, don't push back when they should."

"They really like to overcomplicate code and APIs, bloat abstractions, don't clean up dead code... implement a bloated construction over 1000 lines when 100 would do."

"LLMs are exceptionally good at looping until they meet specific goals... Don't tell it what to do, give it success criteria and watch it go."

— Andrej Karpathy

The four principles

1. Think Before Coding

Don't assume. Don't hide confusion. Surface tradeoffs.

• State 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.

The test: 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.

• 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.
• 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.

For multi-step tasks, state a brief plan:

1. [Step] → verify: [check]
2. [Step] → verify: [check]
3. [Step] → verify: [check]

Slash command

/karpathy-check

Python tools (

scripts/

)

All tools are stdlib-only. Run with

--help

complexity_checker.py

diff_surgeon.py

assumption_linter.py

goal_verifier.py

Sub-agent

karpathy-reviewer

/karpathy-check

Pre-commit hook

hooks/karpathy-gate.sh

complexity_checker.py

diff_surgeon.py

.claude/settings.json

References

• references/karpathy-principles.md

  — the source quotes, deeper context, when to relax each principle

• references/anti-patterns.md

  — 10+ before/after examples across Python, TypeScript, and shell

• references/enforcement-patterns.md

  — how to wire hooks, CI integration, team adoption

When to relax

These principles bias toward caution over speed. For trivial tasks (typo fixes, obvious one-liners), use judgment. The principles matter most on:

• Non-trivial implementations (>20 lines changed)
• Code you don't fully understand
• Multi-step tasks with unclear requirements
• Anything that will be reviewed by humans

Cross-tool compatibility

Installs via plugin for Claude Code. For other tools, copy the principles into your schema file:

CLAUDE.md

AGENTS.md

AGENTS.md

.cursorrules

AGENTS.md

Related skills (chains via

context: fork

)

• self-eval

  — honest quality scoring after completing work

• code-reviewer

  — broader code review; karpathy-coder focuses on the 4 LLM-specific pitfalls

• llm-wiki

  — compound knowledge; karpathy-coder ensures you don't overcomplicate while building it