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.

Args

• Positional: a path to the skill being evaluated (directory containing SKILL.md).
• --evals <path>: explicit path to the cases file. If omitted, discover.
• --mode baseline|variant|quality|trigger: which mode to run. May be repeated.
• --variant-path <path>: for variant mode, the stripped or prior-version skill to compare against.
• --project-root <path>: root of the project the skill belongs to. Default: walk up from the skill path looking for _bmad/ or .git/.
• --output-dir <path>: where run folders are written. Default: {bmad_builder_reports}/eval-runs/ if configured, else ~/bmad-evals/.
• --runs <n>: repeats per case for the variance benchmark. Default: 1 for a single check, higher when the user wants a stable mean.
• --headless / -H: non-interactive; emit final JSON only.

These map directly onto the script CLIs below; anything not listed there (case subsets, timeouts, workers) is in the script docstrings.

On activation

1. Resolve config the way bmad-workflow-builder does ({project-root}/_bmad/config.yaml then config.user.yaml, falling back to bmb/config.yaml). Resolve {user_name}, {communication_language}, {bmad_builder_reports} and apply them through the session.

2. If --headless was passed, set {headless_mode}=true, skip every confirmation below, pick the safest defaults, and proceed.

3. Resume check: glob the output dir for an in-progress run's .memlog.md. If one exists and matches this skill, read it once to rebuild state, then continue append-only. Capture decisions and direction changes into the run's memlog through scripts/memlog.py as they land.

4. Locate the skill and verify <skill-path>/SKILL.md exists. Halt with a clear error if it does not.

5. Resolve the adapter config per the discovery rules in references/platform-adapter.md (explicit --adapter, BMAD_EVAL_ADAPTER, adapter.json beside the cases file). When nothing is configured and the current runtime is Claude Code, use {skill-root}/assets/adapter-claude-code.json.

6. Discover the cases file. Look at --evals first, then <skill-path>/evals/, then <skill-path>/../../evals/<skill-name>/, then <project-root>/evals/<skill-name>/, then anywhere under <project-root>/evals/. Take the first match. If nothing is found, halt and say so; the runner does not invent cases.

7. Confirm the run summary (skill, cases found, modes, output dir) unless headless, then execute.

Run execution

Each case runs in a clean working directory with the skill under test staged into it and an environment built from scratch, so the host shell config, prior runs, and ancestor instruction files do not bias the result. The isolation contract lives in references/platform-adapter.md; there is no container, no terminal emulation, and no credential staging.

For baseline, variant, and quality modes:

uv run {skill-root}/scripts/run_evals.py \
  --cases <cases-file> --skill-path <skill> --output-dir <dir> \
  --mode quality|baseline|variant [--variant-path <skill>] \
  [--adapter <adapter.json>] [--runs N]

The script stages the skill and any case fixtures, applies any state_prefix to the input, runs each config (baseline = skill staged AND bare; variant = skill AND --variant-path), and writes <run-dir>/<config>/<case-id>/. It captures timing and token counts the moment each invocation completes and writes them to timing.json immediately, so a later crash never loses the measurement.

For trigger mode:

uv run {skill-root}/scripts/run_triggers.py \
  --skill-path <skill> --queries <queries-file> --output-dir <dir> \
  [--adapter <adapter.json>] [--runs-per-query N]

It stages a synthetic skill where the runtime discovers skills, sends each query through the adapter, and detects the skill-load tool call. Each query runs several times for stability. When the user wants to optimize the description rather than just measure it, follow references/description-optimization.md.

For quality mode, spawn the grader described in references/grader.md per case, passing the case's rubric, transcript path, artifacts dir (the case's cwd/), and a grading_path of <case-folder>/grading.json. The grader writes that file, gives no partial credit, and flags weak or non-discriminating assertions; relay that feedback. If a grader subagent errors, mark that case grading_error — never substitute a default verdict.

When --runs is greater than one, call uv run {skill-root}/scripts/aggregate_benchmark.py --baseline <run-dir>/<config-a> --variant <run-dir>/<config-b> to produce the mean, sample standard deviation, min, max, and the delta between configs (--runs <run-dir>/<config> for a single config's spread).

When a run fails or comes back weak and the user wants the skill improved from the results, follow references/self-improvement.md.

Artifacts

Every run writes a dated run folder under the output dir, and those artifacts are permanent. Each case folder holds its prompt, transcript, the cwd/ with any files the skill wrote, timing.json, and grading.json when quality mode ran. Never delete, overwrite, or rotate a run folder; disk usage is the user's call. The run's .memlog.md records the decisions and deltas so a resumed or audited run reads back cleanly.

Tell the user where the run folder is when you finish.

Outcomes

• The run reflects the skill's behavior in a clean working directory, not the behavior of the host shell with its memories and configs.
• Timing and token counts land on disk the moment they are measured.
• Failures cite specific expectations with evidence, and a pass that looks superficial is flagged rather than papered over.
• A baseline run that the skill no longer wins points to retiring the skill, not patching it.