Borrowing it
Nothing to install: this file belongs to ReviewStage/stage-cli. 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/ReviewStage/stage-cli/main/.agents/skills/quality-review/SKILL.mdgit clone --depth 1 https://github.com/ReviewStage/stage-cliWrote 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/reviewstage/stage-cli/quality-review)<a href="https://agentmods.dev/skills/reviewstage/stage-cli/quality-review"><img src="https://agentmods.dev/badge/skills/reviewstage/stage-cli/quality-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/reviewstage/stage-cli/quality-review"><img src="https://agentmods.dev/badge/skills/reviewstage/stage-cli/quality-review.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 | $0.00025 | $0.01150 |
| Opus 5.5 | $0.00010 | $0.00460 |
| Sonnet 5.5 | $0.00005 | $0.00230 |
| Haiku 4.5 | $0.00003 | $0.00115 |
Grade A, and why
quality-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 — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Quality Review
Overview
Dispatches one parallel Sonnet agent per bullet point in the ## Implementation Quality section of the nearest AGENTS.md. Each agent independently discovers what changed and checks the codebase against exactly one criterion. A triage pass then removes false positives, duplicates, and contradictions before the final report.
Workflow
1. PARSE → Extract each bullet point from ## Implementation Quality in AGENTS.md
2. DISPATCH → One Task agent per criterion (all in parallel, single message)
3. TRIAGE → Review all raw findings: drop false positives, duplicates, and contradictions
4. REPORT → Compile cleaned findings into summary table
Step 1: Parse Criteria from AGENTS.md
Read the project's AGENTS.md and extract every bullet point under ## Implementation Quality. Stop at the next ## heading — do not include bullets from any other section. Each - line becomes one criterion.
Do not hardcode criteria — always read from the current project's AGENTS.md so the skill stays in sync with the project's actual standards.
Step 2: Dispatch Parallel Agents
CRITICAL: All agents must be launched in a SINGLE message with multiple Task tool calls. Do not loop sequentially.
Use subagent_type: "Explore" and model: "sonnet" on every Task call.
Agent prompt template for each criterion (set model: "sonnet" on every Task call):
You are a focused code reviewer responsible for checking ONE specific quality criterion.
Criterion:
{criterion_text}
Your job:
1. Discover what changed — start with `git diff origin/main...HEAD` or `git diff main...HEAD`.
2. Feel free to explore the broader codebase or search the web for anything — do as much research as needed to make a confident judgment.
3. Check whether the changes comply with your assigned criterion.
4. Report ALL violations you find — do not stop at the first one.
Output format (exactly this structure, nothing else):
**{criterion_short_name}**: PASS | WARN | FAIL
- `file:line` — description of violation (repeat for every violation found)
(omit bullet lines entirely if PASS)
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 · 106 lines · 25 tokens per session scan A f65bfe7355cc
quality-review is a skill published in the GitHub repository ReviewStage/stage-cli (274 stars, last pushed 1mo ago), licensed MIT. It adds 25 tokens to every session and 1,150 once invoked, about $0.0001 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 2026-08-30.
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