← ClaudeAtlas

evaluatelisted

Use for repeatable quality or safety evaluation of stochastic or judgement-bearing systems, prompts, agents, rankings, or artifacts. Not for deterministic tests or ordinary code review; use tdd or code-review.
mblauberg/provenant · ★ 2 · AI & Automation · score 68
Install: claude install-skill mblauberg/provenant
# Evaluate Turn judgement into a frozen, repeatable assurance result. Deterministic tests still run; this skill covers behaviour whose quality cannot be proven by an exit code alone. ## Contract Freeze the canonical evaluation plan before seeing results: - decision the evaluation informs and unacceptable failure modes; - hash-bound dataset/corpus, provenance, consent or licence, data policy and holdout boundary; - candidate and applicable comparator manifests, paired sampling, seeds, repetitions, timeouts, retries and exclusions; - metric ranges, aggregation, thresholds and per-metric regression margins; - deterministic preflight, rubric, blinded independent graders and disagreement handling; - safety, bias, privacy and adversarial cases proportionate to risk; - enclosing delivery run when this evidence supports a non-trivial outcome. Use [EVALUATION.template.json](templates/EVALUATION.template.json) and the [receipt contract](references/receipt.md). Keep raw examples outside the hot receipt; link safe relative artifacts and SHA-256 digests. ## Run 1. Validate fixtures, hashes, schemas and leakage controls before any judgement attempt. Deterministic failure stops or explicitly skips the frozen schedule. 2. Run each planned arm/family/repetition. Retain every attempt, retry and case row with actual adapter/provider/model/effort lineage and usage disposition. 3. Account for passes, failures, omissions, skips, exclusions, timeouts, invalid output and tool/p