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 skills add myths-labs/muse --skill github-pr-reviewgit clone --depth 1 https://github.com/myths-labs/museWrote 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/skills/myths-labs/muse/github-pr-review)<a href="https://agentmods.dev/skills/myths-labs/muse/github-pr-review"><img src="https://agentmods.dev/badge/skills/myths-labs/muse/github-pr-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/skills/myths-labs/muse/github-pr-review"><img src="https://agentmods.dev/badge/skills/myths-labs/muse/github-pr-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 | $0.00074 | $0.01973 |
| Opus 5 | $0.00037 | $0.00986 |
| Sonnet 5 | $0.00015 | $0.00395 |
| Haiku 4.5 | $0.00007 | $0.00197 |
Grade B, and why
github-pr-review scanned grade B with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Reads agent configuration directoriesmediumAgent snooping
.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.
cat .claude/CLAUDE.md 2>/dev/null | head -50 # Review project conventions How it starts
The opening of the file, as written. The whole thing — 236 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GitHub PR Review
Resolves Pull Request review comments with severity-based prioritization, fix application, and thread replies.
Quick Start
# 1. Check project-specific instructions
cat .claude/CLAUDE.md 2>/dev/null | head -50 # Review project conventions
# 2. Get PR and repo info
PR=$(gh pr view --json number -q '.number')
REPO=$(gh repo view --json nameWithOwner -q '.nameWithOwner')
# 3. Fetch and list comments by severity
gh api repos/$REPO/pulls/$PR/comments | python3 -c "
import json, sys
comments = [c for c in json.load(sys.stdin) if not c.get('in_reply_to_id')]
def sev(b): return 'CRITICAL' if 'critical' in b.lower() else 'HIGH' if 'high' in b.lower() else 'MEDIUM' if 'medium' in b.lower() else 'LOW'
for s in ['CRITICAL','HIGH','MEDIUM','LOW']:
cs = [c for c in comments if sev(c['body'])==s]
if cs: print(f'{s} ({len(cs)}): ' + ', '.join(f\"#{c['id]}\" for c in cs))
"
# 4. For each comment: read -> analyze -> fix -> verify -> commit -> reply
# 5. Run tests: make test (or project-specific command)
# 6. Push when all fixes verified
Pre-Review Checklist
Before processing comments, verify:
- Project conventions: Read
.claude/CLAUDE.md,.kiro/steering/, or similar - Commit format: Check
git log --oneline -5for project style - Test command: Identify test runner (
make test,pytest,npm test) - Branch status:
git statusto ensure clean working tree
Core Workflow
1. Fetch PR Comments
PR=$(gh pr view --json number -q '.number')
REPO=$(gh repo view --json nameWithOwner -q '.nameWithOwner')
gh api repos/$REPO/pulls/$PR/comments > /tmp/pr_comments.json
2. Classify by Severity
Process in order: CRITICAL > HIGH > MEDIUM > LOW
| Severity | Indicators | Action |
|---|---|---|
| CRITICAL | critical.svg, "security", "vulnerability" |
Must fix |
| HIGH | high-priority.svg, "High Severity" |
Should fix |
| MEDIUM | medium-priority.svg, "Medium Severity" |
Recommended |
| LOW | low-priority.svg, "style", "nit" |
Optional |
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 5d ago First seen · 236 lines · 74 tokens per session scan B 22f4e7c8559c
github-pr-review is a skill published in the GitHub repository myths-labs/muse (32 stars, last pushed 2d ago), licensed MIT. It adds 74 tokens to every session and 1,973 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it B with 1 finding (reads agent configuration directories). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
work-unit-commits
Plan commits as reviewable work units. Trigger: implementation, commit splitting, chained PRs, or keeping tests and docs with code.
git-authoring
Authors and executes git work end to end — Conventional Commits messages, pull-request content, release notes, and pull-request review, plus the repository operations an engineer runs daily: branching, rebasing, squashing, cherry-picking, reverting, merge-conflict resolution, stashes, tags, remotes, and recovery…
git-pr-workflows-git-workflow
Orchestrate a comprehensive git workflow from code review through PR creation, leveraging specialized agents for quality assurance, testing, and deployment readiness. This workflow implements modern g.
gentle-ai-collab-perfect
Trigger: contributing to Gentleman-Programming/gentle-ai as an external collaborator. Strict issue-first workflow, honest PR bodies, contributor-vs-maintainer scope, chained-PR strategy, verification protocol, docstring coverage. Load whenever the active repo is Gentleman-Programming/gentle-ai and any part of the…
branch-pr
Create Gentle AI pull requests with issue-first checks. Trigger: creating, opening, or preparing PRs for review.
chained-pr
Trigger: PRs over 400 lines, stacked PRs, review slices. Split oversized changes into chained PRs that protect review focus.