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 zsutxz/ClaudeLearning --skill bmad-eval-runnergit clone --depth 1 https://github.com/zsutxz/ClaudeLearningWrote 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/zsutxz/claudelearning/bmad-eval-runner)<a href="https://agentmods.dev/skills/zsutxz/claudelearning/bmad-eval-runner"><img src="https://agentmods.dev/badge/skills/zsutxz/claudelearning/bmad-eval-runner.svg" alt="Measured on agentmods" 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 | $0.00047 | $0.01873 |
| Opus 5 | $0.00023 | $0.00937 |
| Sonnet 5 | $0.00009 | $0.00375 |
| Haiku 4.5 | $0.00005 | $0.00187 |
Grade A, and why
bmad-eval-runner 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 8d 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 — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill Eval Runner
You run a skill's evals and report what they say. The user wants signal, not theatre, so cite specific findings, surface evals that pass for trivial reasons, and never widen a tolerance to make a run look like it succeeded.
The runner is platform-agnostic. Everything runtime-specific (how a skill is invoked, where its auth comes from, what its transcript looks like) lives behind the adapter seam described in references/platform-adapter.md. No model name is hardcoded anywhere in this skill.
The four modes
Each mode answers a different question about a skill. Pick the one that matches what the user is asking, or run several.
| Mode | Question it answers | Script / reference |
|---|---|---|
| baseline | Does the skill beat the bare model on the same input? | references/eval-format.md, scripts/run_evals.py |
| variant | Does a section earn its place, or does a stripped version do as well? | references/eval-format.md, scripts/run_evals.py |
| quality | Does the output meet the named rubric? | references/grader.md, references/eval-format.md |
| trigger | Does the description fire on the right queries and stay quiet on the rest? | references/platform-adapter.md, scripts/run_triggers.py |
Baseline runs every case twice — once with the skill staged into the clean working directory and once with nothing staged — so the bare model is measured as the long-term floor under identical conditions. Variant runs the full skill against a stripped smallest-version of itself to settle whether a section is doing real work. Quality grades one config's output against a rubric with the read-only grader. Trigger measures real firing through the adapter and can optimize the description across rounds; the optimization loop lives in references/description-optimization.md.
A case is input + rubric + optional state_prefix + optional fixture files. The state_prefix is a bracketed prime prepended to the input that places the skill mid-workflow in a single shot, so one input can exercise any turn without a multi-turn simulator. The full case format and the strong-versus-weak expectation taxonomy are in references/eval-format.md.
What ships with it
12 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.
- assets/adapter-claude-code.json 359 B
- references/description-optimization.md 6.6 KB
- references/eval-format.md 7.8 KB
- references/grader.md 5.6 KB
- references/platform-adapter.md 5.2 KB
- references/self-improvement.md 5.7 KB
- scripts/aggregate_benchmark.py 8.4 KB runs code
- scripts/memlog.py 7.3 KB runs code
- scripts/run_evals.py 23 KB runs code
- scripts/run_triggers.py 17 KB runs code
- scripts/tests/test_env_isolation.py 2.9 KB runs code
- scripts/tests/test_trigger_detection.py 4.2 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.
- 8d ago First seen · 99 lines · 47 tokens per session scan A 5fbce2be0bd4
bmad-eval-runner is a skill published in the GitHub repository zsutxz/ClaudeLearning (5 stars, last pushed 1mo ago), licensed MIT. It adds 47 tokens to every session and 1,873 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 2026-08-31.
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