Conciseness

The body is telegraphic and dense — sections like "Baseline first. Never keep an experiment before baseline evidence exists" and "Treat configured regression gates as absolute" carry operational content with zero explanation of concepts Claude already knows. Every token earns its place, matching the level-5 lean/efficient anchor; there is no over-explanation to justify level 4.

Actionability

Concrete, executable artifacts are present: a copy-paste YAML ledger template with real commands ("./bin/ask evals run ... --json --robot"), a runnable shell iteration example ("uv run train.py --steps 200 --json", "pytest tests/regression_guard.py"), and an explicit decision rule "baseline - candidate >= min_delta". However, the generic workflow path ("Define parser contract, guard command, held-out checks...") stays at directive level without command templates outside the single LLM-training example domain — mostly executable with minor gaps, i.e. level 4 rather than the fully-covered level 5.

Workflow Clarity

The 9-step numbered Workflow sequences the loop with explicit validation checkpoints (baseline-first rule, per-iteration Verify and Guard, step 6 re-reading state), plus a dedicated Validation section with a feedback loop ("Fail fast: stop at first failed gate; do not proceed until it is fixed and rerun") and a Repair section defining fix-rerun-broaden ordering. This batch/potentially destructive skill has validation present, so the level-3 cap does not apply; it matches the level-5 anchor with explicit validation and error-recovery loops.

Progressive Disclosure

A dedicated "Progressive Disclosure" section maps each of the five reference files (autoresearch-project.md, contract.yaml, evals.yaml, task-profile.json, discovery-interview.md) to its purpose, and all five exist as substantive files one level deep with no further nesting. The body stays a lean overview while details live in the bundle — matching the level-5 anchor of well-signaled one-level-deep references with easy navigation.

Specificity

The description enumerates multiple concrete actions covering the full loop: "recording baselines, applying hypothesis patches, comparing metrics, protecting regression guards, and deciding keep, discard, rollback, or block" — comprehensive coverage of the skill's capabilities, matching the level-5 anchor rather than the level-4 anchor (which allows minor coverage gaps).

Completeness

It clearly answers both questions: the first sentence states what the skill does with concrete actions, and "Use when automated research is requested or a repo/skill needs evidence-backed research, metric tracking, or safe optimisation loops" is an explicit when-clause with concrete trigger phrases — exactly the level-5 anchor pattern. Level 4 would require the when-clause to be less explicit.

Trigger Term Quality

Natural phrases like "automated research is requested", "evidence-backed research", "metric tracking", and "safe optimisation loops" are terms users would plausibly say. However, common synonyms a user might naturally use — "run experiments", "benchmark", "iterate on" — are missing, so it fits the level-4 anchor (good coverage, a few natural terms missing) rather than level 5 (comprehensive coverage including synonyms).

Distinctiveness Conflict Risk

The bounded-experiment-loop niche with ledger/guard/keep-discard vocabulary is distinctive, but the trigger terms "metric tracking" and "safe optimisation loops" carry minor overlap risk with monitoring or performance-tuning skills, fitting level 4 (mostly distinct, minor overlap with closely related skills) rather than level 5 (minimal conflict risk).