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 wan-huiyan/claude-ecosystem-hygiene --skill test-effectiveness-auditorgit clone --depth 1 https://github.com/wan-huiyan/claude-ecosystem-hygieneWrote 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/wan-huiyan/claude-ecosystem-hygiene/test-effectiveness-auditor)<a href="https://agentmods.dev/skills/wan-huiyan/claude-ecosystem-hygiene/test-effectiveness-auditor"><img src="https://agentmods.dev/badge/skills/wan-huiyan/claude-ecosystem-hygiene/test-effectiveness-auditor/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/wan-huiyan/claude-ecosystem-hygiene/test-effectiveness-auditor"><img src="https://agentmods.dev/badge/skills/wan-huiyan/claude-ecosystem-hygiene/test-effectiveness-auditor.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 | $0.00177 | $0.03730 |
| Opus 5 | $0.00088 | $0.01865 |
| Sonnet 5 | $0.00035 | $0.00746 |
| Haiku 4.5 | $0.00018 | $0.00373 |
Grade B, and why
test-effectiveness-auditor scanned grade B with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Recursive force deletemediumDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
git -C "$PROJECT" worktree remove -f "$WT" 2>/dev/null || rm -rf "$WT" Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.
How it starts
The opening of the file, as written. The whole thing — 248 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Test Effectiveness Auditor v1.0
Answers the question: how helpful are our automated tests at catching bugs? Not by proxy metrics like coverage percent or test count, but by replaying real bugs that already happened and checking whether the test suite, as it stood just before the fix, actually failed on the buggy commit.
The honest baseline for "are tests worth it" is historical: bugs that made it to production despite the tests are the direct evidence of gaps; CI failures that forced a code change before merge are the direct evidence of catches. Everything else is speculation.
Why this matters
Most teams measure test health by coverage % (e.g. pytest --cov). Coverage tells you which lines executed, not whether any assertion would have failed when the behavior was wrong. A line can be 100% covered by a test that would pass under the bug. This audit inverts the question: take known bugs, rewind to the pre-fix commit, and observe whether the suite catches them.
Two methods, in priority order:
- Historical incident replay (primary signal) — for each documented bug, check out the pre-fix SHA in a worktree, run the suite, observe pass/fail, and classify.
- CI history analysis (secondary signal) — pull CI runs that forced a pre-merge change, classify by whether the failure represented a real logic/data/integration catch vs. noise (lint, formatting, flaky).
Mutation testing, test-layer ablation, and coverage-delta analysis are deliberately NOT in scope for v1. They're higher-cost methods whose ROI depends on first knowing the Method 1/2 baseline.
When to run
- On demand when the user asks about test quality or effectiveness
- After an incident to triage "tests should have caught this" vs "fundamentally hard to catch"
- Before a test-investment cycle (writing more tests), to target the biggest gaps first
- Proactively suggest after you notice: a team asking "should we write more tests?", a project with
docs/findings/ordocs/issues/accumulating, or a manager expressing doubt about CI ROI
What ships with it
6 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.
- 11d ago First seen · 248 lines · 177 tokens per session scan B 4ffbdefc9614
test-effectiveness-auditor is a skill published in the GitHub repository wan-huiyan/claude-ecosystem-hygiene (1 stars, last pushed 25d ago), licensed MIT. It adds 177 tokens to every session and 3,730 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it B with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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