Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/xenitv1/claude-code-maestro/clean-codenpx skills add xenitV1/claude-code-maestro --skill clean-codegit clone --depth 1 https://github.com/xenitV1/claude-code-maestroWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00038 | $0.01517 |
| Opus 5 | $0.00019 | $0.00758 |
| Sonnet 5 | $0.00008 | $0.00303 |
| Haiku 4.5 | $0.00004 | $0.00152 |
Grade A, and why
clean-code scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Asks for rootlowPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
| `chmod 777` | Principle of least privilege | Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.
How it starts
The opening of the file, as written. The whole thing — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.
<domain_overview>
🛡️ CLEAN CODE: THE FOUNDATION
Philosophy: This skill is the FOUNDATION - it applies to ALL other skills. Every piece of code must pass these gates. ALGORITHMIC ELEGANCE MANDATE (CRITICAL): Never prioritize "clever" code over readable, intent-revealing engineering. AI-generated code often fails by introducing unnecessary abstractions or using vague naming conventions that obscure logic. You MUST use intent-revealing names for every variable and function. Any implementation that increases cognitive complexity without a proportional gain in performance or scalability must be rejected. Avoid "Hype-Driven Development"—proven patterns trump trending but unstable frameworks. </domain_overview> <iron_laws>
🚨 IRON LAWS
1. NO HALLUCINATED PACKAGES - Verify before import
2. NO LAZY PLACEHOLDERS - Code must be runnable
3. NO SECURITY SHORTCUTS - Production-ready defaults
4. NO OVER-ENGINEERING - Simplest solution first
</iron_laws> <security_protocols>
📦 PROTOCOL 1: SUPPLY CHAIN SECURITY
LLMs hallucinate packages that sound real but don't exist.
- Verify before import -
npm searchorpip showfor unfamiliar packages - Prefer battle-tested - lodash, date-fns, zod over obscure alternatives
- Check npm audit / pip-audit before adding new dependencies
- Pin versions in production - no
^or~for critical deps 2025 AI Package Risks:
- Never import AI "wrapper" libraries without verification
- LLM SDKs: Use official only (openai, anthropic, google-generativeai)
- Vector DBs: Stick to established (pinecone, weaviate, chromadb)
🔐 PROTOCOL 2: SECURITY-FIRST DEFAULTS
Frontend Security:
| Forbidden | Required |
|---|---|
dangerouslySetInnerHTML |
DOMPurify sanitization |
| Inline event handlers | Event delegation |
eval(), new Function() |
Static code only |
| Storing tokens in localStorage | httpOnly cookies |
| Backend Security: | |
| Forbidden | Required |
| ----------- | ---------- |
CORS: * |
Explicit origin whitelist |
| Raw SQL strings | Parameterized queries |
chmod 777 |
Principle of least privilege |
| Hardcoded secrets | Environment variables + validation |
| API Security (2025): |
- Rate limiting on ALL public endpoints
- Input validation at the gate (Zod/Pydantic)
- Output sanitization for AI-generated content
- PASETO > JWT for new projects </security_protocols> <modularity_and_placeholder_rules>
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 144 lines · 38 tokens per session scan A 25e3e8a18cf8
clean-code is a skill published in the GitHub repository xenitV1/claude-code-maestro (230 stars, last pushed 7mo ago), licensed MIT. It adds 38 tokens to every session and 1,517 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (asks for root). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…