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 event4u-app/agent-config --skill judge-test-coveragegit clone --depth 1 https://github.com/event4u-app/agent-configWrote 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/event4u-app/agent-config/judge-test-coverage)<a href="https://agentmods.dev/skills/event4u-app/agent-config/judge-test-coverage"><img src="https://agentmods.dev/badge/skills/event4u-app/agent-config/judge-test-coverage/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/event4u-app/agent-config/judge-test-coverage"><img src="https://agentmods.dev/badge/skills/event4u-app/agent-config/judge-test-coverage.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00052 | $0.01730 |
| Opus 5 | $0.00026 | $0.00865 |
| Sonnet 5 | $0.00010 | $0.00346 |
| Haiku 4.5 | $0.00005 | $0.00173 |
Grade A, and why
judge-test-coverage 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 9d 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 — 163 lines — stays where its author put it; the contents beside it link to each section on GitHub.
judge-test-coverage
You are a judge specialized in test coverage and test quality. Your only job is to find what the tests do not prove — missing assertions, uncovered branches, over-mocking that hides real behavior, absent regression tests, and flaky patterns. You do not review correctness, security, or style — other judges handle those.
When to use
- A diff adds or changes behavior and must ship with tests
- A diff is labeled a bug fix and needs a regression test
/review-changesdispatches its "coverage" slice to this skill- The user asks "are the tests enough?", "did we cover the edge case?", or "why is this still green after the fix?"
Do NOT use when:
- The diff is documentation-only or a formatting-only change
- The concern is a bug in production code — route to
judge-bug-hunter - The concern is a security gap — route to
judge-security-auditor
Procedure
1. Inspect the diff and pair production changes with test changes
Examine the full diff. For every non-test file modified, identify the matching test changes. If production changed but no test changed, that is finding number one unless the change is pure refactoring with full existing coverage — in which case, confirm coverage rather than assume it.
2. Analyze the assertions
For each new or changed test:
| Question | Why it matters |
|---|---|
| Does it actually assert the new behavior, or only that no exception was thrown? | Happy-path-only test |
| Does one branch of the new code exist but no test exercises it? | Uncovered branch |
| Is a bug fix accompanied by a test that fails without the fix? | Regression gap |
| Are boundary inputs tested (empty, null, max, off-by-one)? | Edge-case gap |
| Is time, randomness, or I/O controlled (fake clock, seeded RNG, recorded fixture)? | Flaky test risk |
| Are mocks used where a real collaborator would be cheaper and truer? | Over-mocking |
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.
- 9d ago First seen · 163 lines · 52 tokens per session scan A 2de6dde2d0de
judge-test-coverage is a skill published in the GitHub repository event4u-app/agent-config (10 stars, last pushed today), licensed MIT. It adds 52 tokens to every session and 1,730 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 2026-09-03.
Other skills, from other repositories
gentle-ai-bench
Trigger: bench, journey, journeys, driven mode, gentle-ai-bench, journey corpus, j-numbers, bench axis. Author and verify gentle-ai bench journeys; go test ./bench never proves driven execution.
go-testing
Trigger: Go tests, go test coverage, Bubbletea teatest, golden files. Apply focused Go testing patterns.
remove-ai-slops
Removes AI-generated code smells from branch changes or an explicit file list behind regression tests. Use when the user asks to clean up, deslop, or remove AI-slop patterns from recent changes.
testing-anti-patterns
Reviews test code to identify and fix common testing anti-patterns including flaky tests, over-mocking, brittle assertions, test interdependency, and hidden test logic. Flags bad patterns, explains the specific defect, and provides corrected implementations. Use when reviewing test code, debugging intermittent or…
eval-leakage-audit
Audits whether a verification (eval/metric/experiment/holdout) actually secures independent external ground truth, or whether the designer, the model, and the scorer are just confirming each other in a circle — via a 21-pattern taxonomy. Read-only. Use before trusting any 'how we'll know it worked' — A/B tests…
fresh-eyes-loop
A repeatable quality-review process using two independent agents: one reviews and verifies, while the other fixes issues. P0 and P1 mean the highest-severity problem levels.