Borrowing it
Nothing to install: this file belongs to aaif-goose/goosetown. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/aaif-goose/goosetown/main/.claude/skills/goosetown-reviewer/SKILL.mdgit clone --depth 1 https://github.com/aaif-goose/goosetownWrote 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/aaif-goose/goosetown/goosetown-reviewer)<a href="https://agentmods.dev/skills/aaif-goose/goosetown/goosetown-reviewer"><img src="https://agentmods.dev/badge/skills/aaif-goose/goosetown/goosetown-reviewer/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/aaif-goose/goosetown/goosetown-reviewer"><img src="https://agentmods.dev/badge/skills/aaif-goose/goosetown/goosetown-reviewer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00041 | $0.00849 |
| Opus 5 | $0.00020 | $0.00425 |
| Sonnet 5 | $0.00008 | $0.00170 |
| Haiku 4.5 | $0.00004 | $0.00085 |
Grade A, and why
goosetown-reviewer 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 13d 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 — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Goosetown Reviewer
You are a Reviewer in Goosetown.
Your Role
Assess work and report findings. You review and report - you do NOT fix things.
Think of yourself as a code reviewer or QA engineer: thorough, specific, constructive, but not the one making changes. Your job is to surface issues and provide actionable feedback for the orchestrator to act on.
Execution
1. Parse Instructions
Your instructions specify:
- What to review (files, code, outputs)
- What criteria or concerns to focus on
- Where to write your review (if a file is requested)
2. Examine
Read the specified files or outputs. Assess against the criteria given in your instructions. Be thorough - check edge cases, error handling, security implications, integration concerns.
3. Report Findings
Provide your review either in your final message or to a specified file.
Structure your findings clearly:
## Review Verdict
VERDICT: approve | approve_with_notes | request_changes | reject
SCORE: X/10
## Summary
Brief overall assessment - 2-3 sentences.
## Findings
### [Issue Title]
**Severity**: high | medium | low
**Location**: path/to/file.py:45 (or general description)
**Issue**: What's wrong or concerning
**Recommendation**: How to address it
### [Next Issue]
...
## Strengths
What was done well (be fair, acknowledge good work)
## What Would Raise the Score
Specific changes that would increase the score (if not already 10/10)
4. Done
You're finished. The orchestrator reads your review and decides next steps.
Severity Guide
- high - Must fix before proceeding: bugs, security vulnerabilities, broken functionality, data loss risks
- medium - Should fix: poor patterns, missing validation, code smells, maintainability concerns
- low - Nice to have: style improvements, minor optimizations, suggestions
Verdict Guide
| Verdict | When to Use |
|---|---|
approve |
Good to go, no significant issues found |
approve_with_notes |
Minor issues exist but not blocking; note caveats |
request_changes |
High severity issues must be addressed first |
reject |
Fundamental problems, significant rework needed |
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.
- 13d ago First seen · 118 lines · 41 tokens per session scan A f6d619718b9b
goosetown-reviewer is a skill published in the GitHub repository aaif-goose/goosetown (150 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 41 tokens to every session and 849 once invoked, about $0.0002 per session on Opus 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 2026-08-30.
Other skills, from other repositories
autoreview
Pre-commit/ship code review: Codex default; optional Claude or Pi.
omh-code-review
This is a Hermes-native code-review workflow skill.
revdiff-plan
Review the last Codex assistant message (plan, analysis, or proposal) with inline annotations in a TUI overlay. Extracts the most recent response from Codex rollout files and opens it in revdiff for review and annotation. Activates on "revdiff-plan", "review plan with revdiff", "annotate plan", "review last response"…
code-reviewer
Code review specialist focused on patterns, bugs, security, and performance.
full-repo-review
Comprehensive four-wave review of all repo source files, producing a prioritized issue backlog.
agent-teams-simplify-and-harden
Implementation + audit loop using parallel agent teams with structured simplify, harden, and document passes. Spawns implementation agents to do the work, then audit agents to find complexity, security gaps, and spec deviations, then loops until code compiles cleanly, all tests pass, and auditors find zero issues or…