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 me2resh/apexyard --skill eval-agentsgit clone --depth 1 https://github.com/me2resh/apexyardWrote 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/me2resh/apexyard/eval-agents)<a href="https://agentmods.dev/skills/me2resh/apexyard/eval-agents"><img src="https://agentmods.dev/badge/skills/me2resh/apexyard/eval-agents/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/me2resh/apexyard/eval-agents"><img src="https://agentmods.dev/badge/skills/me2resh/apexyard/eval-agents.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 369 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 Agent Snooping · line 388 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.
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 | $0.00044 | $0.07251 |
| Opus 5.5 | $0.00018 | $0.02900 |
| Sonnet 5.5 | $0.00009 | $0.01450 |
| Haiku 4.5 | $0.00004 | $0.00725 |
Grade A, and why
eval-agents 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 — 426 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Writing: read docs/skills/shared/writing-rule.md before you write a durable artifact.
/eval-agents — Review-Agent Eval Harness
Scores one of the framework's review agents (Rex / Hakim / Tariq) against a labeled corpus of real, already-reviewed PRs with frozen ground-truth defect sets. Reports catch-rate, false-positive-rate, and approve-precision (the headline metric) against a configurable pass/fail threshold.
Why this isn't an LLM-judge rating review prose
Spike #825 tested that shape first — score a review agent's text output against a 0-4 rubric, calibrated against recorded verdicts — and found it at chance on the one question that matters: was an approval justified? A fluent, verification-heavy wrong approval scored as well as or better than genuinely correct approvals (docs/spike-825/findings.md). This skill instead:
- Never asks a judge to rate review text. Ground truth is a frozen set of real defects, established once, offline, by a human, from actual re-review disagreements and confirmed fixes — never re-derived at run time, never established by the agent being measured.
- Runs the agent-under-test fresh against the corpus entry's diff, and mechanically/semantically compares its findings to the frozen defect set: caught / missed / false-alarm.
- Reports approve-precision as the headline metric — the rate at which the agent's approvals were actually justified. A missed BLOCKING/HIGH defect is an automatic WARN regardless of the aggregate score.
Full rationale: AgDR-0089. Corpus format: docs/eval-agents/SCHEMA.md.
Usage
/eval-agents rex # run against the seeded starter corpus
/eval-agents hakim --corpus docs/eval-agents/corpus/hakim.json
/eval-agents tariq --corpus my-corpus.json # Use a custom Tariq corpus.
/eval-agents rex --check-only # validate corpus schema only, no spawns
What ships with it
9 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.
- lib/mine_disagreements.py 23 KB runs code
- lib/snapshot-diff.sh 3.3 KB runs code
- lib/validate-corpus.sh 8.7 KB runs code
- tests/fixtures/mine-disagreements/edge-cases.json 4.9 KB
- tests/fixtures/mine-disagreements/merged-prs.json 11 KB
- tests/smoke.sh 3.4 KB runs code
- tests/test_mine_disagreements.py 17 KB runs code
- tests/test_mine_disagreements.sh 5.6 KB runs code
- tests/test-snapshot-diff.sh 3.2 KB runs code
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 · +28 lines f850c329e19c
- 1mo ago Changed · +4 lines 1797d008f8fd
- 1mo ago First seen · 394 lines · 44 tokens per session scan A 77ab35775b39
eval-agents is a skill published in the GitHub repository me2resh/apexyard (533 stars, last pushed today), licensed MIT. It adds 44 tokens to every session and 7,251 once invoked, about $0.0002 per session on Opus 5.5. 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
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adversarial-reviewer
Stress-test a code change for concrete correctness defects, unsafe assumptions, and failure modes. Use when the user explicitly asks for an adversarial review, a deep bug hunt, or to tear a change apart, or to check your own change before submitting it. Do not use for ordinary review, style feedback, or general…
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Trigger: Go tests, go test coverage, Bubbletea teatest, golden files. Apply focused Go testing patterns.
semgrep-rule-variant-creator
Creates language variants of existing Semgrep rules. Use when porting a Semgrep rule to specified target languages. Takes an existing rule and target languages as input, produces independent rule+test directories for each language.
brooks-sweep
Full-sweep mode: runs a unified analysis across all quality dimensions — code decay, architecture, tech debt, and test quality — then applies fixes directly to the codebase. Safe changes are auto-applied; risky changes are confirmed before execution. Drawing on twelve classic engineering books. Triggers when: user…
plugin-review
Review plugin quality with tiered checks and dependency scoping. Use for PR and pre-release audits.