quality-review

quality-review is a skill for Claude Code, Codex from ReviewStage/stage-cli. It costs 25 tokens per session (1,150 once invoked), scanned A, original, MIT.

A code-review workflow that checks changes against the implementation-quality rules in the nearest AGENTS.md file, a repository instruction file for coding agents. It assigns each rule a separate review and then removes duplicate or unsupported findings.

In plain words
What is it for?
Use it to review a set of code changes, read the applicable quality criteria, check every criterion independently, and produce a cleaned summary of findings.
Why use it?
It turns a broad quality review into checks tied to the project's actual rules. This helps avoid overlooking a requirement or reporting the same problem several times.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/reviewstage/stage-cli/quality-review
Any agent
npx skills add ReviewStage/stage-cli --skill quality-review
Clone the repo
git clone --depth 1 https://github.com/ReviewStage/stage-cli

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for quality-review

README.md
[![agentmods](https://agentmods.dev/badge/skills/reviewstage/stage-cli/quality-review.svg)](https://agentmods.dev/skills/reviewstage/stage-cli/quality-review)
Your own site
<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="Measured on agentmods" height="20"></a>
Per session 25 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,150 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00025 $0.01150
Opus 5 $0.00013 $0.00575
Sonnet 5 $0.00005 $0.00230
Haiku 4.5 $0.00003 $0.00115

Measured 4d ago against content hash f65bfe7355cc, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 4d 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.

.agents/skills/quality-review/SKILL.md · 106 lines

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)

Read the full file on GitHub · 106 lines

Changes

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.

  1. 4d ago First seen · 106 lines · 25 tokens per session scan A f65bfe7355cc

Subscribe to this mod's changes

quality-review is a skill published in the GitHub repository ReviewStage/stage-cli (267 stars, last pushed 23d ago), licensed MIT. It adds 25 tokens to every session and 1,150 once invoked, about $0.0001 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.

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