Conciseness

The body is largely efficient — domain-specific tool calls, threshold tables, and decision rules that Claude does not already know, with no padding explaining basic concepts. However the ~45-line optional ThermoMPNN section (vendor SaaS listings, conda install walkthrough, license notes) and chatty asides like 'Not as clean as DynaMut2's purely academic endpoint, which is why TU defaults to DynaMut2' are trimmable. This matches the score-4 anchor (efficient; minor instances of over-explanation that could be trimmed) rather than 5, since every token does not quite earn its place.

Actionability

Every step has copy-paste-ready tool invocations with exact parameters (e.g. 'ESM_explain_variant_mechanism(sequence=..., position=175, ref_aa="R", alt_aa="H", window=8, top_k_features=5)'), numeric decision thresholds (pLDDT >70, ΔlogP < −1, ddG > +1 kcal/mol), a variant-string parsing regex, and a fill-in reporting template. This matches the score-5 anchor (fully executable, copy-paste ready, covering the common cases).

Workflow Clarity

Steps 0–7 are clearly sequenced with explicit validation checkpoints: the reference-residue assert in Step 0 ('assert sequence[position - 1] == ref_aa'), the benign-score gating in Step 1, the pLDDT reliability gating in Step 2, and the honest evidence-grading and limitations sections in Step 7. This matches the score-5 anchor (clear sequence with explicit validation and feedback on conflicting signals); it is not a destructive/batch operation so no cap applies.

Progressive Disclosure

The skill is a single monolithic ~290-line SKILL.md with no bundle files (no references/, scripts/, or assets/ exist). Section headers are good, but the advanced optional content — the entire ThermoMPNN local-install, SaaS-vendor, and license material — is inlined in the main file rather than split into a one-level-deep reference file. This matches the score-3 anchor (some structure, but content that should be separate is inline); it is above 2 because the in-file organization and navigation are solid, but below 4 because the optional material clearly belongs in its own file.

Specificity

The description lists multiple concrete actions — 'Propose the mechanism by which a missense variant causes loss-of-function (LoF), synthesizing evidence from 5 independent layers' with each layer named (AlphaMissense, AlphaFold, ESMC, SAE, DynaMut2) — plus a concrete distinction between 'structural stability LoF' (mis-folding) and 'direct functional disruption'. It uses third-person voice throughout. This matches the score-5 anchor (multiple specific concrete actions, comprehensive coverage) rather than 4, since there are no minor gaps in coverage of what the skill does.

Completeness

It explicitly answers 'what' ('Propose the mechanism... synthesizing evidence from 5 independent layers... Distinguishes structural stability LoF from direct functional disruption') and 'when' ('Use for coding missense variants where you need a mechanistic causal model, not just a pathogenicity score') with concrete trigger phrasing. This matches the score-5 anchor exactly; the 'when' clause is both explicit and specific, not merely present as at 4.

Trigger Term Quality

Natural domain terms are present — 'missense variant', 'loss-of-function (LoF)', 'pathogenicity score', 'mechanistic causal model' — which a user needing this skill would plausibly say. However it misses common synonyms and variations such as 'amino acid substitution', 'protein variant', or 'missense mutation', so it fits the score-4 anchor (good keyword coverage; a few natural terms missing) rather than 5's comprehensive synonym coverage.

Distinctiveness Conflict Risk

The description carves out a clear niche — mechanistic causal modeling of coding missense variants — and actively distinguishes itself from adjacent capabilities ('not just a pathogenicity score'), minimizing conflict risk with generic variant-interpretation skills. It matches the score-5 anchor (clear niche with distinct triggers, minimal conflict risk).