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.
git clone --depth 1 https://github.com/multiplex-ai/muggle-ai-teamsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/multiplex-ai/muggle-ai-teams/code-review)<a href="https://agentmods.dev/commands/multiplex-ai/muggle-ai-teams/code-review"><img src="https://agentmods.dev/badge/commands/multiplex-ai/muggle-ai-teams/code-review/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/multiplex-ai/muggle-ai-teams/code-review"><img src="https://agentmods.dev/badge/commands/multiplex-ai/muggle-ai-teams/code-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00043 | $0.00583 |
| Opus 5.5 | $0.00017 | $0.00233 |
| Sonnet 5.5 | $0.00009 | $0.00117 |
| Haiku 4.5 | $0.00004 | $0.00058 |
Grade A, and why
code-review scanned grade A with 0 findings 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 1mo 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Review
Run a structured 3-pass review on uncommitted or staged changes.
Process
-
Gather changes:
git diff --stagedandgit diffto see all modifications. If no diff, check recent commits withgit log --oneline -5. -
Read surrounding code: Don't review changes in isolation. Read the full file and understand imports, dependencies, and call sites.
-
Run three passes:
Pass 1: Code Quality
Security (CRITICAL — always flag):
- Hardcoded credentials, API keys, tokens
- SQL/NoSQL injection, XSS vulnerabilities
- Missing input validation, path traversal
- Authentication bypasses, CSRF gaps
- Insecure dependencies, exposed secrets in logs
Code Quality (HIGH):
- Bugs, logic errors, off-by-one errors
- Functions >50 lines, files >800 lines, nesting >4 levels
- Missing error handling, console.log statements
- Missing tests for new code paths, dead code
React/Node.js Patterns (HIGH):
- Missing useEffect dependency arrays, stale closures
- N+1 queries, unbounded queries, missing rate limiting
- Unvalidated request input, error message leakage
Performance (MEDIUM):
- O(n²) algorithms, missing caching, large bundle imports
Pass 2: Compliance
- CLAUDE.md rules for each repo touched (naming, structure, patterns)
- TypeScript strictness (no
any, explicit types) - Code style (longform property syntax, ~200 line files)
- Test coverage for changed behavior
Pass 3: Contract Consistency (skip if single-repo)
- Frontend/backend API type alignment
- Endpoint path consistency
- Error format consistency (
{ code, message, details })
Confidence-Based Filtering
- Only report issues with >80% confidence
- Consolidate similar issues
- Skip stylistic preferences unless they violate project conventions
Output Format
## MUST FIX
- [file:line] [description] — [why critical]
## SHOULD FIX
- [file:line] [description] — [why improves quality]
## NITPICK
- [file:line] [description]
## Summary
| Severity | Count |
|----------|-------|
| MUST FIX | N |
| SHOULD FIX | N |
| NITPICK | N |
Verdict: APPROVE / REQUEST CHANGES / BLOCK
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.
- 1mo ago First seen · 79 lines · 43 tokens per session scan A 1b455cd1b5c2
code-review is a command published in the GitHub repository multiplex-ai/muggle-ai-teams (2 stars, last pushed 2d ago), licensed MIT. It adds 43 tokens to every session and 583 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other commands, from other repositories
analyze-file
Deep analysis of a specific file using code-analyst sub-agent.
pi-plan
Multi-provider implementation planning — consult Codex and Gemini for architectural perspectives. Trigger: when the task involves architectural decisions, tech stack selection, evaluating multiple approaches, migration strategy, or complex implementation with multiple viable paths. Do NOT trigger for simple task…
select_review
A command for quickly reviewing only the code selected by the user. It reports three to five findings in Japanese with severity labels and suggested fixes, or briefly reports that no issue was found.
setup
Verify that the local Claude review runtime is installed and authenticated.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.