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 skills add PracticalSwan/agent-skills --skill review-agentgit clone --depth 1 https://github.com/PracticalSwan/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/skills/practicalswan/agent-skills/review-agent)<a href="https://agentmods.dev/skills/practicalswan/agent-skills/review-agent"><img src="https://agentmods.dev/badge/skills/practicalswan/agent-skills/review-agent/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/practicalswan/agent-skills/review-agent"><img src="https://agentmods.dev/badge/skills/practicalswan/agent-skills/review-agent.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 | $0.00048 | $0.01077 |
| Opus 5 | $0.00024 | $0.00539 |
| Sonnet 5 | $0.00010 | $0.00215 |
| Haiku 4.5 | $0.00005 | $0.00108 |
Grade A, and why
review-agent 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.
How it starts
The opening of the file, as written. The whole thing — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review Agent
Inspect the requested target directly and return every finding that the author would likely fix. Do not modify files, create commits, push branches, post review comments, or delegate the review to another agent.
Review the change
- Read the applicable
AGENTS.mdinstructions. - Inspect the complete diff for the requested target and enough surrounding code to understand each changed path.
- Identify concrete regressions introduced by the change. Continue through the whole diff after finding the first issue.
- Check the relevant tests and call sites to confirm that each finding is real and actionable.
For a base-branch review, compare the changes that would actually merge rather than diffing
directly against the branch tip. Resolve the comparison ref to the branch's upstream when that
upstream exists and is ahead of the local branch; otherwise use the local branch. Run
git merge-base HEAD <comparison-ref>, then inspect git diff <merge-base-sha>. If the local
branch cannot be resolved, try its configured upstream explicitly before reporting that the target
is unavailable.
Flag an issue only when all of these are true:
- It affects correctness, security, performance, or maintainability in a meaningful way.
- It is discrete and actionable.
- It was introduced by the reviewed change.
- The affected scenario or call path can be demonstrated from the code.
- The author would probably fix it if they knew about it.
Do not flag speculative concerns, pre-existing problems, intentional behavior changes, or style nits that do not obscure the code.
Write the result
Present findings first, ordered by severity. Use one entry per issue in this form:
[P1] Imperative finding title — path/to/file.rs:line
Follow the title with one short paragraph explaining the affected scenario and why the behavior is wrong. Keep the cited range as small as possible and make sure it overlaps the reviewed diff.
Use these priorities:
P0: universal release blocker or critical failure.P1: urgent defect that should be fixed next.P2: ordinary defect that should be fixed.P3: low-impact issue that is still worth fixing.
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.
- 4d ago Changed 750550179f22
- 6d ago Changed 14f29f6af7e4
- 8d ago First seen · 108 lines · 48 tokens per session scan A 4f9ac8a04322
review-agent is a skill published in the GitHub repository PracticalSwan/agent-skills (14 stars, last pushed 4d ago), licensed MIT. It adds 48 tokens to every session and 1,077 once invoked, about $0.0002 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 2026-09-03.
Other skills, from other repositories
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.
plugin-quality
A review guide for checking Zhin.js plugins before release. It covers plugin structure, feature declarations, resource cleanup, message sending, and security.
pr-review
Review a GitHub pull request and post one formal review — advance the existing discussion and give precision-first, high-signal feedback. Judgement on the diff, not a build gate — CI validates that it builds, and a targeted probe is allowed as evidence. Use when asked to review a PR or on a cron PR scan.
autonomous
Use when five specialized autonomous agents (code, deploy, planning, research, review) working as a coordinated pipeline. From spec to shipped code with automated planning, research, review, and deployment gates. Use when working with autonomous agents.
review-agent
Use when reading code changes with adversarial intent to find bugs, security holes, logic errors, and performance traps.
code-review
The shared rubric for reviewing a code change — precision-first, high-signal findings only (Critical/Important), what to check (correctness, contracts between producer and consumer, security, edge cases, regression risk, test coverage), and what is NOT a finding (pre-existing issues, linter territory, aspirational…