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 chrisallenlane/claude-swe-workflows --skill review-healthgit clone --depth 1 https://github.com/chrisallenlane/claude-swe-workflowsWrote 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/chrisallenlane/claude-swe-workflows/review-health)<a href="https://agentmods.dev/skills/chrisallenlane/claude-swe-workflows/review-health"><img src="https://agentmods.dev/badge/skills/chrisallenlane/claude-swe-workflows/review-health/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/chrisallenlane/claude-swe-workflows/review-health"><img src="https://agentmods.dev/badge/skills/chrisallenlane/claude-swe-workflows/review-health.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 | $0.00057 | $0.04699 |
| Opus 5 | $0.00028 | $0.02350 |
| Sonnet 5 | $0.00011 | $0.00940 |
| Haiku 4.5 | $0.00006 | $0.00470 |
Grade A, and why
review-health 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 — 307 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review-Health — Strategic Orientation for a Repository
A first-pass review skill for the moment you want to step back and assess a repo strategically: you've just inherited it, you're evaluating a FOSS project for adoption, you're onboarding a teammate, or you're revisiting your own project to decide where to invest. The skill produces an evidence-cited map of the repo's state — not a grade. Its output is built to inform strategic decisions about engagement, not to itemize every imperfection.
This skill is advisory only. It makes no changes. To act on findings, hand off to /refactor, /review-arch, /review-test, /review-security, or other specialists as the findings indicate.
Philosophy
Observation before interpretation. The skill's procedure enforces an OODA cadence — Observe, Orient, Decide, Act — with strict phase gates. The Observe phase collects signals without verdicts. Only after observation is complete does interpretation begin. This is the structural countermeasure to the most common failure mode of informal code review: fixating on the first file opened and building a distorted mental model from there.
"Good" is relational, not absolute. A 34% test-coverage finding is a different finding in a research prototype than in an OSS library with external consumers. The skill's calibration is anchored in reference classes (references/classes/): the repo is classified into a class first, and each dimension is evaluated against class-specific expectations. Classification is a cited, overridable output — the user sees which class was applied and can correct it in one line. Every downstream finding reflows against the correct class.
Every claim carries evidence. A finding without a file:line citation or a tool-output reference is not a finding; it is an assertion and must be dropped or demoted to the Coverage Manifest. This is enforced structurally (the phase-integrity check in §Evidence Discipline), not stylistically.
Named unknowns beat silent unknowns. The Coverage Manifest is first-class output. Tools that weren't available, signals that couldn't be computed, and questions that couldn't be answered are named explicitly with the reasons they couldn't be resolved. A honest "we couldn't assess X" beats a confident assessment that silently excluded X.
What ships with it
7 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.
- 12d ago First seen · 307 lines · 57 tokens per session scan A eec8db5af9f0
review-health is a skill published in the GitHub repository chrisallenlane/claude-swe-workflows (18 stars, last pushed 3mo ago), licensed MIT. It adds 57 tokens to every session and 4,699 once invoked, about $0.0003 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 skills, from other repositories
afc:learner
Review and promote learned patterns to project rules.
afc:architect
Architecture analysis and design review.
afc:pr-comment
Post structured review comments to GitHub PR.
afc:resolve
Address LLM bot review comments on PR — fix valid issues, dismiss false positives.
afc:review
Code review — review code, analyze PR diff, evaluate quality and correctness.
review-loop
Run the adversarial verification loop — implement, then hand the change to a fresh checker that did not write it, fix what it finds, and re-dispatch until APPROVE. Use before claiming any behavioural change is done, and on requests like "review loop", "adversarial review", "independent review", "get this verified"…