karpathy-coder
by alirezarezvanikarpathy-coder helps AI coding agents expose assumptions, avoid over-abstraction, keep diffs surgical, and define verifiable goals. Includes SKILL.md, references, expected JSON outputs, and Python checks for assumptions, complexity, diff noise, and goal verification.
This skill scores 78/100, which makes it a solid listing candidate for directory users who want an agent-enforced coding-discipline workflow rather than a generic code-review prompt. It is clearly scoped, triggerable, and backed by practical scripts and examples, but users should notice the missing install command and some mismatch between claimed workflow components and the visible repository files.
- Strong triggerability: the frontmatter names concrete use cases such as "review my diff," "check complexity," "before I commit," and code-quality concerns where an LLM might overcode.
- Good agent leverage: four Python scripts target concrete failure modes—assumption linting, complexity checking, diff noise detection, and goal verification—with JSON expected outputs showing actionable findings.
- Useful progressive context: reference docs include anti-pattern before/after examples, enforcement patterns, and fuller explanations of the four Karpathy coding principles.
- SKILL.md lacks an install command, so directory users may need to infer installation from the surrounding Claude skills repository or reference docs.
- The main description claims a review agent, slash command, and pre-commit hook, but the provided tree evidence only shows scripts, references, and expected outputs, creating some adoption uncertainty.
Overview of karpathy-coder skill
What karpathy-coder is for
karpathy-coder is a coding-discipline skill for AI-assisted development. It helps an agent slow down before writing code, expose assumptions, avoid unnecessary abstractions, make smaller diffs, and define verifiable success criteria. The practical job is not “make code prettier”; it is to stop an LLM from confidently overbuilding, guessing requirements, or mixing unrelated changes into one commit.
Best-fit users and workflows
The karpathy-coder skill is a strong fit if you use Claude Code, Cursor, Codex CLI, Gemini CLI, opencode, Antigravity, or similar agentic coding tools and want a repeatable pre-commit review habit. It is especially useful for solo developers, staff engineers reviewing AI-generated diffs, and teams that want lightweight guardrails without adopting a full static-analysis platform.
Why this is more than a prompt
Unlike a one-off “keep it simple” instruction, karpathy-coder includes a structured SKILL.md, reference material, expected JSON outputs, and Python scripts for checking assumptions, complexity, diff noise, and goal verification. That makes it easier to apply the same review standard across planning, implementation, and Code Review instead of relying on the model to remember vague principles.
Main adoption tradeoffs
karpathy-coder works best as a judgment aid, not an automatic correctness guarantee. It can flag suspicious language, high complexity, noisy diffs, or missing verification steps, but it cannot know your product requirements unless you provide them. If your main need is security scanning, type checking, dependency auditing, or framework-specific linting, use those tools alongside this skill rather than replacing them.
How to Use karpathy-coder skill
karpathy-coder install options
For a skill-directory workflow, install with:
npx skills add alirezarezvani/claude-skills --skill karpathy-coder
If you are using Claude Code plugin-style installation, the repository references this pattern:
/plugin install karpathy-coder@claude-code-skills
After installation, inspect the skill path:
engineering/karpathy-coder/skills/karpathy-coder
Read SKILL.md first, then open references/karpathy-principles.md, references/anti-patterns.md, and references/enforcement-patterns.md. Preview expected_outputs/*.json to understand what the scripts consider a useful finding rather than treating the tool as a black box.
Inputs that make karpathy-coder usage better
A weak request is: “Review this change.” A better karpathy-coder usage prompt gives the agent the intended scope, the diff, constraints, and verification target:
Use karpathy-coder for Code Review. Goal: fix empty email validation only. Do not refactor unrelated form code. Review the diff for hidden assumptions, over-abstraction, noisy changes, and missing verification. Success criteria: existing tests pass, new empty-email test fails before the fix and passes after. If scope is unclear, ask before suggesting code.
This works because the skill is designed around four checks: assumption management, simplicity, surgical change, and measurable goals. The more explicit your boundary is, the easier it is for the agent to push back on unnecessary work.
Suggested working sequence
Use karpathy-coder before implementation, during review, and before commit:
- Before coding: ask the agent to list assumptions and possible interpretations.
- During planning: require each step to include
verify: [specific check]. - After coding: run a review focused on diff size, unrelated edits, complexity, and dead code.
- Before commit: compare the final diff against the original goal.
For local checks, inspect the scripts before running them:
scripts/assumption_linter.pyscripts/complexity_checker.pyscripts/diff_surgeon.pyscripts/goal_verifier.py
The repository examples show commands such as:
python scripts/complexity_checker.py src/ --threshold strict
and:
python scripts/diff_surgeon.py
Adjust paths to match where the skill is installed in your environment.
Repository files to read first
Start with SKILL.md for the agent behavior and trigger language. Then read references/anti-patterns.md because it contains the most practical before/after examples, such as turning “export user data” into clarifying questions about users, fields, format, and destination. Use expected_outputs/goal_verifier.json to understand how the skill evaluates plans that lack verification steps. This reading path gives faster value than browsing every file in order.
karpathy-coder skill FAQ
Is karpathy-coder good for beginners?
Yes, with one caveat: beginners should treat its pushback as a learning tool, not as absolute truth. The skill is helpful because it names common AI coding mistakes clearly: guessing requirements, adding premature architecture, changing too many files, and skipping verification. Beginners still need tests, compiler feedback, and human review for domain correctness.
How is it different from ordinary code review prompts?
A normal prompt often produces broad comments. karpathy-coder gives the agent a narrower review lens: “What assumption did we make?”, “Is this simpler than necessary?”, “Is this diff surgical?”, and “How do we verify success?” That makes the output more actionable when reviewing AI-generated code, especially before committing.
When should I not use karpathy-coder?
Do not use karpathy-coder as your only review layer for security-critical code, compliance workflows, database migrations, or performance-sensitive systems. It may help ask better questions, but it is not a substitute for threat modeling, load testing, schema review, or production observability. Also avoid it when you intentionally need a broad redesign; the skill is biased toward small, constrained changes.
Does karpathy-coder fit existing engineering tools?
Yes. It complements linters, formatters, test runners, type checkers, and pre-commit frameworks. The repository includes Python scripts and enforcement-pattern references, so teams can move from passive guidance to active checks. Before wiring automation, confirm installed file paths and decide whether findings should warn, block, or simply annotate review.
How to Improve karpathy-coder skill
Improve karpathy-coder results with stronger briefs
The most common failure mode is giving the skill too little context. Replace “make this better” with a brief that includes:
- the exact user-facing goal
- files or modules in scope
- files explicitly out of scope
- acceptable complexity level
- required tests or manual checks
- whether the agent may refactor
This lets karpathy-coder distinguish a useful simplification from an unwanted rewrite.
Calibrate reviews with anti-pattern examples
Use references/anti-patterns.md as a calibration set. If your team repeatedly sees the same issue, such as adding caching before measuring latency or introducing a Strategy pattern for one function, paste a short project-specific example into your prompt. The skill performs better when “too complex” is grounded in your codebase rather than left as taste.
Iterate after the first output
A good first review should produce findings, but the second pass is where value increases. Ask the agent to classify each issue as must fix, should fix, or acceptable tradeoff, then request the smallest patch for only the must fix items. This preserves the skill’s surgical-change bias and prevents review comments from becoming another source of scope creep.
Tune thresholds and enforcement level
If the scripts are too noisy, start with advisory use instead of blocking commits. Run complexity and diff checks manually for a week, compare findings with real reviewer concerns, then decide whether to tighten thresholds or add pre-commit integration. The best karpathy-coder guide for a team is usually a short local convention: when to ask clarifying questions, what counts as noisy diff, and which verification steps are mandatory.