Conciseness

The body is efficient — it assumes the flywheel context and never re-explains SFT/DPO basics, e.g. 'Curation is the work that remains — which traces clear a quality bar...'. Not 5 because some rationale is repeated (the goldens-leakage warning appears in full in both the body and the reference) and benchmark citations like 'SRFT reports 32.2% vs. 30.9% on SWE-bench' pad beyond what the instruction needs; not 3 because the padding is minor, not a section's worth of over-explanation.

Actionability

Concrete numeric guidance ('select the rejected member at μ−2σ', 'the top 5k of a 16.5k candidate pool'), an explicit input JSON schema, and exact field mappings, with fully executable worked conversions verified to live in references/conversion-recipes.md (select_pair, rejection_sample, filter_holdout code). Not 5 because some body guidance defers without specifying — the top 'fraction' for rejection sampling is unnamed in the body and the secret/PII scan names no tool or pattern; not 3 because the bundle as a whole gives executable, copy-paste-ready recipes for the common cases.

Workflow Clarity

Validation checkpoints for this batch operation are explicit and fail-closed: 'conversion fails closed (the row is dropped...)', the goldens holdout, dedup against the existing training set, and the provenance check, plus 'Only when valid' style gating in the reference's holdout recipe ('Run filter_holdout before the messages-only stripping'). Not 5 because the sequence itself is implicit — the workflow must be assembled from the SFT, Pairs, and Hygiene sections rather than a numbered flow; not 3 because checkpoints are present and explicit, which the score-3 anchor lacks.

Progressive Disclosure

The body is a well-sectioned overview (Principle, SFT, Pairs, Hygiene, Related Skills) and defers all worked examples to a single reference that exists, is one level deep, and whose contents are enumerated at the point of reference ('a graded trace to SFT row, trace pair to DPO pair, correction to SFT row, the rejection-sampling loop, and the goldens-holdout check... live in references/conversion-recipes.md'). Verified the reference file matches that description exactly with no nested references, matching the score-5 anchor.

Specificity

Lists several specific actions — 'Convert evaluation traces and production logs into SFT examples and preference pairs', 'applying rejection sampling', 'building DPO pairs from passing and failing runs' — which goes beyond the 1-2 actions of a score-3 anchor. Not 5 because the skill's hygiene duties (secret scanning, goldens holdout, provenance) are absent from the description, leaving a minor coverage gap.

Completeness

Explicitly answers both: 'what' — converting traces/logs into SFT examples and preference pairs — and 'when' via a three-clause 'Use when...' ('when graded traces or failure examples exist and need to become training data, when applying rejection sampling..., or when building DPO pairs from passing and failing runs') with concrete trigger phrases. Matches the score-5 anchor exactly.

Trigger Term Quality

Natural practitioner phrases like 'training data', 'rejection sampling', 'DPO pairs', and 'passing and failing runs' give good keyword coverage a user would plausibly say. Not 5 because common synonyms such as 'fine-tuning dataset' or 'preference data' are missing; not 3 because multiple natural multi-word trigger phrases are present, not just a generic keyword or two.

Distinctiveness Conflict Risk

Clear niche — converting already-graded eval traces into training data — with distinct triggers ('graded traces', 'rejection sampling to model outputs', 'DPO pairs from passing and failing runs') that would not naturally fire for sibling skills like dataset curation or eval harnessing. Minimal conflict risk, matching the score-5 anchor; the score-4 anchor's 'minor overlap risk' does not fit since the trigger terms are unique to this conversion step.