Karpathy Coding Principles

Derived from Andrej Karpathy's observations on LLM coding pitfalls.

Tradeoff: These guidelines bias toward caution over speed. For trivial tasks, use judgment.

When to Apply

• Classification: quick-win, feature, architecture-change, hotfix
• Phase: /plan (assumption surfacing), /implement (simplicity + surgical), /review (scope audit)
• Trigger: All non-trivial coding tasks — this is a behavioral baseline, not a domain skill

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.

Self-check: "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.

Checklist

During /plan:

• Assumptions stated explicitly before proposing solution
• Ambiguities surfaced — multiple interpretations presented if they exist
• Simplest viable approach chosen (justify if not the simplest)

During /implement:

• No features beyond what was asked
• No abstractions for single-use code
• Every changed line traces to the user's request
• Pre-existing code left alone unless directly required
• Success criteria defined before implementation begins

During /review:

• Diff contains only requested changes — no drive-by refactoring
• Code is not overcomplicated — could a simpler version work?
• Orphaned imports/variables from THIS change are cleaned up
• Pre-existing dead code mentioned but not deleted

Code Simplification Checklist

When reviewing or refactoring, apply Chesterton's Fence — understand WHY before removing:

• Deep nesting (>3 levels) → extract to named functions
• Long functions (>50 lines) → split by responsibility
• Poor naming → rename to reveal intent
• Speculative abstractions → remove if only one caller exists
• Duplicate logic → extract only if 3+ occurrences (not 2)

Simplification rules:

• Preserve behavior exactly — simplification is NOT a feature change
• Fewer lines is not always simpler — a 1-line nested ternary is worse than a 5-line if/else
• Separate simplification commits from feature commits — never mix

Heading-Scoped Read Note

For phase-entry loading, read only:

• When to Apply
• Checklist
• Code Simplification Checklist

Load numbered principle sections (## 1–## 4), Common Rationalizations, Anti-Patterns, and References on full read or cache miss only.

Common Rationalizations

Anti-Patterns

• Silent assumption: Picking one interpretation without surfacing alternatives
• Speculative flexibility: Adding config/abstraction layers "for the future"
• Drive-by cleanup: "Improving" adjacent code that wasn't part of the request
• Vague completion: Claiming "done" without verifiable success criteria
• Rationalizing shortcuts: Using plausible-sounding excuses to skip verification steps

References

• Source: multica-ai/andrej-karpathy-skills (MIT declared upstream; no root LICENSE artifact; provenance asserted)
• Simplification enrichment: addyosmani/agent-skills — code-simplification (MIT)
• Complements: writing-plans (Think Before Coding), verification-before-completion (Goal-Driven Execution)
• Guardrails: .agent/rules/engineering_guardrails.md §7 Scope Discipline