git:20260705.85b9dcd to git:20260712.e55f142

# Andrej Kaparthy's coding style

<!-- Lemon AI Hub: Karpathy Standards -->

Behavioral guidelines to reduce common LLM coding mistakes. Merge with project-specific instructions as needed.

**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.

## Project Rules

Mandatory project rules live in `docs/rules/`. Read and apply them before touching

the affected areas:

- [`docs/rules/marketplace-sync.md`](docs/rules/marketplace-sync.md) — every plugin

in `plugins/` MUST be registered in `.claude-plugin/marketplace.json` in the same

change. Never add/rename/remove a plugin without syncing the marketplace.

## graphify

This project has a knowledge graph at graphify-out/ with god nodes, community structure, and cross-file relationships.

Rules:

- For codebase questions, first run `graphify query "<question>"` when graphify-out/graph.json exists. Use `graphify path "<A>" "<B>"` for relationships and `graphify explain "<concept>"` for focused concepts. These return a scoped subgraph, usually much smaller than GRAPH_REPORT.md or raw grep output.

- If graphify-out/wiki/index.md exists, use it for broad navigation instead of raw source browsing.

- Read graphify-out/GRAPH_REPORT.md only for broad architecture review or when query/path/explain do not surface enough context.

- After modifying code, run `graphify update .` to keep the graph current (AST-only, no API cost).

## 🔌 CLI Wrapper — cross-harness policy

When ANY AI harness (Claude Code, Codex, Gemini, Agy, OpenCode) needs to invoke another CLI:

1. **PREFER** `/cli-wrapper:<cli>-cli invoke <args>` over raw `<cli> <args>`

2. Wrapper validates flags via pre-built digest, post-processes output

3. Token savings: 60-95% per invocation vs raw CLI output

4. **Setup**: run `/cli-wrapper:cli-wrapper-setup` once per machine

5. **New CLI?** `/cli-wrapper wrap <new-cli>` to add to ecosystem

Wrapper agents handle cross-harness delegation (e.g., Claude spawns `codex-cli` agent to delegate to Codex). Never run raw `<cli> exec` — always through the wrapper.