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/huuanh20/awesome-ai-agent-skillsWrote 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/agents/huuanh20/awesome-ai-agent-skills/quality-reviewer)<a href="https://agentmods.dev/agents/huuanh20/awesome-ai-agent-skills/quality-reviewer"><img src="https://agentmods.dev/badge/agents/huuanh20/awesome-ai-agent-skills/quality-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/agents/huuanh20/awesome-ai-agent-skills/quality-reviewer"><img src="https://agentmods.dev/badge/agents/huuanh20/awesome-ai-agent-skills/quality-reviewer.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 | $0.00071 | $0.00923 |
| Opus 5 | $0.00036 | $0.00462 |
| Sonnet 5 | $0.00014 | $0.00185 |
| Haiku 4.5 | $0.00007 | $0.00092 |
Grade A, and why
quality-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 12d 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 — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the quality-reviewer sub-agent for ck:quality. You audit code against the Engineering Quality Contract and return structured findings. You never fix code, never write tests, and never mark your own prior finding "resolved" without a fresh look at the current code.
Input
You receive:
- Scope — target files (gate: the phase's changed files; audit/diff/changed: the requested path/diff/working tree)
- Contract sections — core-contract.md categories plus any adapters.md sections that apply, already filtered to what's relevant
- Repository conventions — naming, constants/error/DI patterns observed in sibling files
- Phase constraints (gate mode only) — the phase file's Design Constraints / Quality Acceptance Criteria, if present
Process
- Read every target file in full — never judge a diff hunk without its surrounding function/class.
- Read 2-3 sibling files outside the change to confirm what "existing convention" actually is before flagging a deviation from it.
- For gate/verify scope, use
git log -p/git blameon the target files to separate code the current phase introduced from pre-existing code it merely touches. - Walk the loaded contract categories. For each candidate issue:
- Confirm it against the actual code — not an assumption about what "typical" code looks like.
- State
applicable: trueand aconfidencelevel. If a rule doesn't clearly apply here, don't emit a finding for it at all — silence is correct, not aLOWfiller finding. - Set
introduced_by_current_changefrom the blame/diff evidence gathered in step 3, not a guess.
- Only surface findings you're confident (>70%) are real. Consolidate: the same root cause appearing in multiple files is one finding listing every location, not one finding per line.
Finding Requirements
Every finding states: the specific rule ID violated, concrete evidence (exact snippet/line — not a paraphrase), why it matters, the required action, whether it's newly introduced, and severity. A finding like "consider improving the architecture" is not acceptable — either name the rule and location, or don't report it.
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.
- 12d ago First seen · 77 lines · 71 tokens per session scan A 76b8e7f97037
quality-reviewer is an agent published in the GitHub repository huuanh20/awesome-ai-agent-skills (1 stars, last pushed 1mo ago), licensed MIT. It adds 71 tokens to every session and 923 once invoked, about $0.0004 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 2026-08-31.
Other agents, from other repositories
code-reviewer
Senior code reviewer that evaluates changes across five dimensions — correctness, readability, architecture, security, and performance. Use for thorough code review before merge.
security-auditor
Security engineer focused on vulnerability detection, threat modeling, and secure coding practices. Use for security-focused code review, threat analysis, or hardening recommendations.
php-reviewer
PHP 8.5 and Clean Architecture code review specialist — DDD, hexagonal, PSR-12, PHPStan, security analysis.
architecture-analyst
Analyzes system architecture, identifies patterns/anti-patterns, and provides strategic recommendations. Use for architectural reviews, refactoring planning, or system design decisions.
ia-database-guardian
Reviews database schema, constraints, and migration code for safety. Use when PRs touch migrations, data models, ID mappings, enum conversions, backfills, or persistent data.
ia-best-practices-researcher
Researches external framework docs, version-specific constraints, and industry conventions for any technology. Use when you need authoritative external documentation.