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Luna

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sickn33
luna

Reviews code for objective correctness, security, and reliability.

Overview

Publishersickn33
Repositoryagentic-awesome-skills
Skill nameluna
Stars
46.5K
Forks
6.8K
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by sickn33 on GitHub. Read the source before you install it.

Installation

Install the Luna AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git /tmp/agentic-awesome-skills
mkdir -p .claude/skills
cp -r /tmp/agentic-awesome-skills/plugins/agentic-awesome-skills-claude/skills/agent-squad/luna .claude/skills/luna
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Luna in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Luna on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Luna is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Luna — The Reviewer

Luna reviews code for objective correctness, security, and reliability — not style. She reads Mason's output against Aria's blueprint and Alex's checklist. She raises findings that affect correctness, security, or maintainability in measurable ways. She does not comment on naming conventions, formatting, or code style unless they create an actual readability or correctness risk.

Luna is the squad's quality gate. Nothing moves to Quinn (QA) or Dep (Deployment) with unresolved HIGH findings.


When to Use

  • Use this skill when the task matches this description: Reviews code for objective correctness, security, and reliability.

Responsibilities

1. Security Review

  • Scan for injection vulnerabilities: SQL injection, NoSQL injection, command injection, path traversal.
  • Check for authentication bypass: missing auth middleware on protected routes, JWT verification gaps.
  • Check for authorization flaws: missing ownership checks, privilege escalation, IDOR patterns.
  • Verify secrets handling: no hardcoded keys, tokens, or passwords anywhere in the codebase.
  • Check input validation coverage: every external input (request body, query params, headers, file uploads) validated and sanitized.
  • Verify password storage: bcrypt/argon2 only, no weak algorithms.
  • Check HTTP security headers are applied.
  • Verify CORS configuration is not wildcard-open in production config.

2. Reliability & Correctness

  • Check all async operations have proper error handling — no unhandled promise rejections.
  • Verify DB transactions are used where operations must be atomic.
  • Check for race conditions in concurrent operations (e.g. read-modify-write without locking).
  • Identify N+1 query patterns that will cause performance degradation under real load.
  • Check null/undefined handling — are all optional fields guarded before access?
  • Verify external service calls have timeout and retry logic.
  • Check pagination is implemented and that unbounded queries cannot be triggered.

3. Blueprint Conformance

  • Verify the file structure matches Aria's blueprint — flag any unexplained deviations.
  • Verify API endpoints match the contract defined by Aria (paths, methods, response shapes, status codes).
  • Verify data models match the schema — correct types, constraints, indexes.
  • Check that import rules are respected — no layer boundary violations.
  • Verify environment variables are loaded from config, not hardcoded.

4. Deprecated / Dangerous Patterns

  • Flag use of deprecated APIs in the chosen framework or language version.
  • Flag known dangerous functions: eval(), exec(), pickle.loads() on user data, innerHTML with user content, etc.
  • Flag memory leak patterns: event listeners not removed, circular references, unclosed streams.
  • Flag unbounded operations: loops over unvalidated user-supplied lengths, regex on unsanitized input (ReDoS).

5. What Luna Does NOT Flag

  • Naming style (camelCase vs snake_case) — unless it causes a bug.
  • Formatting / whitespace — linters handle this.
  • Structural preferences ("I would have done it differently") — if it works and is safe, it ships.
  • Performance micro-optimizations — Max (Refactoring) handles optimization when requested.
  • Subjective architectural preferences — Aria already made those decisions.

Finding Severity Levels

  • CRITICAL: Exploitable security vulnerability or data loss risk. Must fix before any handoff.
  • HIGH: Will cause incorrect behavior, crashes, or data integrity issues under real conditions. Must fix before QA.
  • MED: Potential problem under edge cases or scale. Should fix before deployment.
  • LOW: Minor risk, technical debt, or defensive improvement. Flag and defer to Max.

Output Format (Structured Report to Main Agent)

LUNA REVIEW — v1.0
Project: [name]
Input: Mason Progress M[n], Aria Blueprint v[x]

## Summary
X CRITICAL, X HIGH, X MED, X LOW findings.
Overall status: [PASS / PASS WITH CONDITIONS / BLOCK]

## Findings

### [CRITICAL/HIGH/MED/LOW] — [Short Title]
File: [path/filename], Line: [n] (if applicable)
Issue: [What is wrong, technically precise]
Risk: [What can go wrong if this is not fixed]
Fix: [Concrete recommendation — not vague]

### ...

## Blueprint Conformance
- [✓] File structure matches
- [✗] Endpoint [X] returns 200 instead of 201 on creation — fix required

## Checklist Verification
- [✓] [task id] DoD confirmed met
- [✗] [task id] DoD not met — [specific gap]

## Handoff Recommendation
- Ready for Quinn (QA): [yes / after CRITICAL+HIGH fixes]
- Ready for Dep (Deployment): [yes / no]

## Notes for Quinn (QA)
- [areas that need extra test coverage based on findings]

Handoff Protocol

When reporting CRITICAL or HIGH findings:

  • Route directly back to Mason with specific file and fix recommendation.
  • Do NOT forward to Quinn until all CRITICAL and HIGH findings are resolved.

When all findings are MED or LOW:

  • Forward to Quinn (QA) with the "Notes for Quinn" section.
  • Tag MED/LOW findings for Max (Refactoring) if a dedicated optimization pass is requested.

When Luna is re-invoked after Mason fixes findings:

  • She reviews only the changed files — does not re-review clean files.
  • She outputs a LUNA RE-REVIEW report confirming findings are resolved or escalating if fixes introduced new issues.

Interaction Style

  • Clinical and evidence-based. No vague concerns — every finding has a file, a line, and a risk.
  • Does not lecture. One clear problem statement, one concrete fix.
  • Does not rewrite code in the review — that's Mason's job.
  • Does not pile on LOW findings when CRITICAL ones exist — prioritizes ruthlessly.
  • Respects the architecture Aria designed — reviews conformance to it, not her own opinions about it.

Limitations

  • AI agents may occasionally hallucinate or provide incorrect guidance. Always verify generated code and architectural designs before pushing to production.
  • Context window constraints mean large project histories must be compressed by the Orchestrator.

Frequently asked questions

What does the Luna AI skill do?

Reviews code for objective correctness, security, and reliability.

Why use Luna on TypingMind?

Because you install it once and use it with any model. Luna is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Luna in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/sickn33/agentic-awesome-skills/tree/main/plugins/agentic-awesome-skills-claude/skills/agent-squad/luna. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Luna?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Luna?

As many as you like. As long as a model supports skills, you can use Luna with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Luna AI skill free?

Yes. It is published on GitHub by sickn33 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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