plugins/agent-agentic-os/skills/optimize-agent-instructions/SKILL.md
Audits and rewrites AI agent instruction files (CLAUDE.md, GEMINI.md, .github/copilot-instructions.md) in any repo. Strips stale or foreign content, applies Karpathy's four behavioral principles, ensures platform-specific sections, and makes each file authoritative rather than a copy of another. Trigger when the user says "optimize my CLAUDE.md", "audit agent instructions", "improve my CLAUDE.md", "apply Karpathy principles to my agent files", "clean up my copilot instructions", "review my GEMINI.md", or "update my AI instruction files".
npx skillsauth add richfrem/agent-plugins-skills optimize-agent-instructionsInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Audits and rewrites the AI agent instruction files in a repo. Works on any project — not just agent-plugins-skills. The goal is files that are authoritative, concise, and guide AI behavior through explicit principles rather than hoping for defaults.
Run these checks silently before asking anything:
1. Which instruction files exist?
ls CLAUDE.md GEMINI.md .github/copilot-instructions.md 2>/dev/null
2. Ask the user:
If instruction files are missing for active platforms, offer to create them.
Read each file, then score it against the Quality Checklist:
Structure
Karpathy Principles — verify all four are present:
Platform-specific (if applicable)
Report the audit score before rewriting. Example:
CLAUDE.md: 6/8 checks pass
✗ No Karpathy principles section
✗ Stale artifact at EOF
✓ No self-referential framing
...
For each file that scored poorly, propose changes:
Get confirmation before writing. Show the full proposed content for each file.
Before rewriting any files, read the Karpathy principles example at references/sample-claude-md. This file contains the authoritative representation of the principles derived from forrestchang/andrej-karpathy-skills.
Write each file using the canonical structure below.
# <Title (e.g., CLAUDE.md or Copilot Instructions)>
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]
` ` `
(Note: Do not escape backticks in actual file, use regular markdown codeblock formatting)
Strong success criteria let you loop independently. Weak criteria ("make it work") require constant clarification.
---
**These guidelines are working if:** fewer unnecessary changes in diffs, fewer rewrites due to overcomplication, and clarifying questions come before implementation rather than after mistakes.
---
## Project-Specific Rules
<Project-specific rules. Keep existing rules from previous versions, or omit section if none exist.>
For GEMINI.md only — append after the main content:
## Gemini CLI Tool Mapping
| Claude Code | Gemini CLI equivalent |
|:------------|:----------------------|
| `Read` | `read_file` |
| `Write` | `write_file` |
| `Edit` | `replace_in_file` |
| `Bash` | `run_shell_command` |
| `Glob` | `glob` |
| `Grep` | `grep` |
Skills in `.agents/skills/` use Claude Code tool names in their SKILL.md files.
When executing skills via Gemini, translate tool references using the table above.
For .github/copilot-instructions.md — title line should be authoritative:
# Copilot Instructions for <repo-name>
> Authoritative rules for all AI agents (Claude Code, Copilot, Gemini) working in this repo.
> Mirrors CLAUDE.md — keep in sync.
After writing, re-run the Quality Checklist mentally. Confirm:
Report:
=== optimize-agent-instructions Complete ===
Files updated:
✓ CLAUDE.md — 8/8 checks pass
✓ GEMINI.md — 8/8 checks pass
✓ .github/copilot-instructions.md — 8/8 checks pass
Karpathy principles: ✓ all four present in all files
Stale artifacts removed: 2
Foreign content removed: 1
Platform sections added: Gemini tool mapping
## Project-Specific Rules.The four behavioral principles in this skill are derived from Andrej Karpathy's observations on LLM coding pitfalls, as distilled by forrestchang/andrej-karpathy-skills.
testing
Skill for creating and managing isolated git worktrees (`.worktrees/issue-NNN`) for issue execution branches. USE ONLY when setting up or cleaning up isolated git worktrees for specific issue execution. DO NOT USE for managing local task files (use `task-agent`) or escalating tasks to issues (use `github-issue-backlog-agent`).
data-ai
Skill for orchestrating the end-to-end GitHub issue lifecycle flow: Issue -> Worktree -> Implementation -> PR Creation -> Resolution Closure. USE ONLY when running or dry-running full lifecycle orchestration for resolving an issue with a PR. DO NOT USE for isolated worktree management only (use `issue-worktree-agent`) or logging issues (use `github-issue-agent`).
tools
Automatically ranks GitHub issues (P0-P3) based on friction tier, frequency, and blockages, synchronizing priority labels and GitHub Projects v2 custom fields.
testing
Bridge skill for escalating ephemeral local task scratchpad items (`tasks/*.md`) into durable, taxonomy-validated, evidence-rich GitHub Issues. USE ONLY when promoting a single-session local task into durable repository backlog. DO NOT USE for managing local kanban boards (use `task-agent` instead) or directly querying/commenting on issues (use `github-issue-agent` instead).