Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/keli-wen/agy-staffnpx agentmods add skills/keli-wen/agy-staff/reviewerWrote 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/keli-wen/agy-staff/reviewer)<a href="https://agentmods.dev/skills/keli-wen/agy-staff/reviewer"><img src="https://agentmods.dev/badge/skills/keli-wen/agy-staff/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/keli-wen/agy-staff/reviewer"><img src="https://agentmods.dev/badge/skills/keli-wen/agy-staff/reviewer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 19 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Excessive Agency · line 51 Skill grants unrestricted tool access without appropriate constraints. An agent with unfettered tool access can perform arbitrary actions including file modification, network requests, and code execution.Fix: Restrict tool access to only the tools required for the skill's stated purpose. Use an explicit allowlist rather than granting blanket access.
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 | $0.00094 | $0.01238 |
| Opus 5 | $0.00047 | $0.00619 |
| Sonnet 5 | $0.00019 | $0.00248 |
| Haiku 4.5 | $0.00009 | $0.00124 |
Grade B, and why
reviewer scanned grade B with 1 finding 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 yesterday.
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.
Unrestricted tool accessmediumExcessive agency
A wildcard tool grant or "run any command" leaves no least-privilege boundary at all.
An unrestricted review of code from an untrusted author (a PR from a stranger, a patch from an unknown source) means prompt injection in that content could run arbitrary commands. For those reviews, consider `--restricte Copies of this mod
1 near-identical copy found in the catalogue:
- agy-reviewer — 86% identical, 22 lines differ
How it starts
The opening of the file, as written. The whole thing — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
agy reviewer
Run a second-opinion review through the agy staffer. You are a thin shell: compose the task, run the companion, return agy's review verbatim. Never fix the issues it finds.
The companion's template contributes only the reviewer stance, evidence discipline, and guardrails. Everything flavor-specific travels in the task string you compose — so pick the flavor first:
- Code review — the subject is code: a PR, a branch/ref, the working tree, a patch file, specific files. Read
references/code-review.mdand compose the task per it (evidence gathering, review axes, severity-ranked output).--jsonbelongs to this flavor only. - General review — the subject is a decision, plan, design, document, or set of claims. Read
references/general-review.mdand compose the task per it (multi-angle challenge). No fixed output format: state the deliverable's shape in the task if the user needs a specific one.
Locating the companion
This skill file lives at <plugin-root>/skills/reviewer/SKILL.md; resolve the companion path relative to this skill directory:
node "<skill-dir>/../../companion/agy-companion.mjs" review [flags] --prompt "what to review"
Pass the review subject verbatim via --prompt; use --prompt-file <path> or --stdin for long text, which a composed task usually needs.
[!IMPORTANT] Run this command unsandboxed — agy needs a localhost port and its OAuth token file, which harness sandboxes hide. In Codex, request escalated permissions for the command. Details:
../jobs/references/troubleshooting.md.
The review subject is the prompt
review is prompt-based: the user's request plus the flavor's framing is the task string. Do not gather diffs, write patch files, or translate the request into flags — agy collects the evidence itself. If the subject is ambiguous, agy reports the ambiguity instead of guessing; relay that and let the user sharpen the request. A task string is required; review with no subject exits with an error.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- yesterday Changed f00e2829b508
- 2d ago Changed · -2 lines d2504e1b97fb
- 6d ago Changed b47e64d2ea66
- 11d ago First seen · 59 lines · 94 tokens per session scan B 1d381b9e6740
reviewer is a skill published in the GitHub repository keli-wen/agy-staff (124 stars, last pushed today), licensed MIT. It adds 94 tokens to every session and 1,238 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it B with 1 finding (unrestricted tool access). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
mantis-plan
Formulates a targeted defensive security reviewing plan based on the active threat model and historical learnings. Use when starting a security review campaign to map the codebase boundaries and generate a roadmap (workspace/plan.json). Don't use for executing code reviews, writing test scripts, or patching code.
mantis-review
Independently reviews findings and filters out false positives. Use when consolidated findings need validation against the actual source code. Don't use for reproducing crashes or patching code.
mantis-researcher
Audits production source code files based on the strategy in workspace/plan.json. Use when a review plan exists and you need to perform static analysis and deep-dive reviews of targeted files. Don't use for planning, deduplicating, or writing patches.
mantis-summarize
Pre-processes the repository by generating security-focused summaries (mantis-summary.md) for each directory to make planning and research more efficient. Use when starting a review campaign to map the codebase before threat modeling and planning. Don't use for executing code reviews, writing test scripts, or patching…
mantis-launch
Launches automated vulnerability review campaigns on target files or repositories. Use to initiate Mantis vulnerability review pipelines with automated preflight checks, environment auto-configuration, and runtime overrides (sandboxes, models, endpoints). Don't use for configuring settings without scanning or for…
ocr
AI-powered multi-agent code review. Simulates a team of Principal Engineers reviewing code from different perspectives. Use when asked to review code, check a PR, analyze changes, or perform code review.