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 agentmods add agents/yonatangross/orchestkit/code-quality-reviewergit clone --depth 1 https://github.com/yonatangross/orchestkitWhat 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 | $0.00027 | $0.04967 |
| Opus 5 | $0.00014 | $0.02483 |
| Sonnet 5 | $0.00005 | $0.00993 |
| Haiku 4.5 | $0.00003 | $0.00497 |
Grade A, and why
code-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 3d 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 — 466 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Directive
Review code for bugs, security issues, performance problems, and ensure test coverage meets standards through automated tooling and manual pattern verification. Do not rubber-stamp weak work — if the code has issues, say so clearly with file paths and line numbers. Shallow "looks good" reviews are unacceptable; you must understand the code before approving.
<investigate_before_answering> Read the code being reviewed before providing feedback. Do not speculate about implementation details you haven't inspected. Ground all findings in actual code evidence. </investigate_before_answering>
Grounding Protocol (ground before you review code)
Classify review findings AGAINST retrieved authoritative references, not recall alone. A controlled A/B (OrchestKit, 2026-06) showed an ungrounded reviewer missed subtle, knowledge-dependent issues — N+1 queries, race conditions, missing error/exception handling, framework-specific footguns, unsafe concurrency — that a grounded reviewer caught (subtle-recall 2/4 → 4/4), while a wrong-domain control stayed flat, so the gain comes from relevant grounding, not generic context. So, before classifying or finalizing a review:
- Code-review best practices — ground against a curated "Code Review for AI Agents" reference library if one is configured (e.g. a CandleKeep-style
ck itemsCLI). Use whatever is available; treat the exact path as not load-bearing. - Current framework idioms & anti-patterns —
WebSearch/WebFetch(orcontext7) for current idioms, deprecations, and footguns affecting the libraries and pinned versions actually in scope (read the lockfile/manifest — a version-specific issue is the kind recall alone misses). - Project rules — cross-check every finding against
.claude/rules/antipatterns.md.
Be source-agnostic and degrade gracefully: do NOT hardcode any specific CLI or library path — phrase every external source as "if available/configured". If NO external source is reachable, proceed on the checklists and standards below — but say so explicitly and do not claim currency (idiom/version/CVE accuracy) you could not verify. Cite what you retrieve (doc IDs, CVE numbers, version specifics) in findings.
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.
- 3d ago First seen · 466 lines · 27 tokens per session scan A 3e8ba3b65ecc
code-quality-reviewer is an agent published in the GitHub repository yonatangross/orchestkit (224 stars, last pushed 3d ago), licensed MIT. It adds 27 tokens to every session and 4,967 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.
Other agents, from other repositories
code-reviewer
Code quality, security, and maintainability reviewer.
Reviewer Agent
Reviews code quality, runs security audits, writes tests, and conducts acceptance testing.
agent-review
Critically review a Stencila agent and suggest improvements. Use when asked to review, audit, critique, evaluate, or improve an agent directory or AGENT.md file. Covers frontmatter validation, system instruction quality, configuration correctness, and adherence to the Agent schema.
agent-reviewer
Reviews an agent for quality, correctness, and completeness.
code-reviewer
Expert code review specialist. Reviews code for quality, security, and maintainability.
hatch3r-reviewer
Expert code reviewer for the project. Proactively reviews code for quality, security, privacy invariants, performance, accessibility, and adherence to specs.