Eval

/eval — Plugin Evaluation Harness

Validate the plugin against rubric-scored test cases. Two tiers shipped today:

• Tier 1 — linters (shipped, free): frontmatter, schema, hook-reference, char-limit, JSON-syntax, AST checks. No LLM calls.
• Tier 2 — judge-calibration drift smoke (shipped, opt-in): for each sampled rubric, the Haiku judge scores the shipped good + bad calibration samples, and the result is checked against a ±0.5 score-band tolerance around the score encoded in each filename (<scenario>.score-X.X.md). This catches three drift classes:
  1. Rubric drift — someone edits the rubric in a way that breaks scoring
  2. Judge model drift — Anthropic ships a new Haiku version that scores samples differently
  3. Sample drift — someone edits a calibration sample so it no longer hits its band
• Tier 3 — behavioral (planned, not shipped): full executor + judge + blind-comparator per skill case. runner.py --tier 3 currently exits with error code 3.

When to use

• Pre-release validation — run Tier 1 linters (free, no LLM) across all skills before tagging a release
• Calibration-drift detection — run Tier 2 to catch rubric drift, judge-model drift, or sample drift against the ±0.5 score-band tolerance
• Skill tuning — scope Tier 1 to one skill (/eval <skill-name>) or Tier 2 to one rubric (/eval --tier 2 --rubric <name>) while iterating
• Release gate — /eval --all to run Tier 1 + Tier 2 in a single pass

Invocation

Shipped commands:

/eval                                  # Tier 1 linters across all skills
/eval feature-design                   # Tier 1 lint scoped to one skill
/eval --tier 2                         # Tier 2 judge-calibration smoke (default 10 rubrics × 2 samples)
/eval --tier 2 --rubric feature-design # Tier 2 limited to one rubric
/eval --tier 2 --dry-run               # plan-only; no API calls (also auto-applies if anthropic SDK unavailable)
/eval --all                            # Tier 1 + Tier 2 in one run

Planned (not yet shipped — runner returns error code 3):

/eval --tier 3                         # full behavioral suite (planned)
/eval --skill <name> --tier 3          # Tier 3 per-skill (planned)
/eval --baseline <skill>               # capture per-skill baseline scorecard (planned)
/eval --all --resume                   # resume after interruption (planned, Tier 3 only)

Tier 2 details

Sample plan (default seed 42): 10 rubrics × 2 samples = 20 judge calls. Override via runner flags --seed N, --sample-rubrics N, --samples-per-rubric N, --rubric NAME.

Requires ANTHROPIC_API_KEY and the anthropic Python SDK. Without either, the runner reports DRY-RUN ONLY and skips actual API calls.

Default judge model: Haiku (claude-haiku-4-5). Per-rubric override allowed when calibration Spearman drops below threshold (see plugin/eval/config.json → judge_models).

Token budgets

Per plugin/eval/config.json:

Output

• Tier 1: stderr findings + summary count of CRITICAL / WARNING. Exit code 0/1/2.
• Tier 2: per-sample PASS/FAIL with score delta + dry-run skip count. Exit code 0/1/2.

Failure modes

• Tier 3 invoked: runner.py --tier 3 (and any --baseline/--resume/per-skill Tier 3 form) is not shipped — the runner exits with error code 3. Use Tier 1 or Tier 2 instead.
• Missing API key or SDK: Tier 2 requires ANTHROPIC_API_KEY and the anthropic Python SDK. Without either, the runner reports DRY-RUN ONLY, skips all API calls, and performs no real scoring.
• Token cap exceeded: Tier 2 has a 50K soft / 150K hard cap per plugin/eval/config.json; a plan that would breach the hard cap is rejected before any API call.
• Calibration drift detected: a sampled good/bad sample scoring outside its ±0.5 band fails (rubric drift, judge-model drift, or sample drift) — exit code 1; inspect the per-sample score delta to localize which class drifted.
• Future surfaces referenced: plugin/eval/cases/ and plugin/eval/results/ directories do not exist, and the Tier-3 behavioral suite + blind-comparator are not yet wired — relying on those today fails. (The eval-judge agent itself is shipped — plugin/agents/eval-judge.md powers RALF subjective oracles now — but the Tier-3 behavioral path that would invoke it from /eval is not yet wired.)

Integration

• Wraps: plugin/eval/runner.py (entry) + plugin/eval/tier2.py (Tier 2 implementation)
• Reads: plugin/eval/config.json (token caps + judge models + calibration thresholds)
• Reads: plugin/eval/judge-rubrics/<rubric>.md (per-rubric scoring criteria)
• Reads: plugin/eval/calibration/<rubric>/{good,bad}/<scenario>.score-X.X.md (Tier 2 calibration samples — 45 rubrics × 6 samples = 270 total)
• Honors: userConfig.ralph_session_* caps if eval cases ever trigger embedded RALF (Tier 3, planned)
• Used by: /plugin-doctor --calibrate-judge (Tier 2 opt-in)

Future surfaces (not currently wired)

These are referenced by the planned Tier 3 design but not implemented:

• plugin/eval/cases/<skill>/*.json — per-skill behavioral test cases. Directory does not exist; expect to ship in Phase 4.
• plugin/eval/results/<run-id>/ — per-run output artefacts.
• Tier-3 behavioral-suite invocation path that wires plugin/agents/eval-judge.md (shipped) to plugin/eval/cases/ + plugin/eval/results/ — the judge agent exists today; the case/result harness that drives it does not.
• --baseline <skill> capture to .committed/eval-baselines/<release-tag>.json.
• Blind-comparator (third agent in isolated context with skills suppressed) — design-only.

Do not write code that depends on these surfaces today; they are documented for design continuity, not behavior.