Conciseness

Mostly dense, domain-calibrated content (gnomAD two-step gotchas, BS1 thresholds, predictor AUCs), but the ~60-line classify_acmg function fully restates the Bayesian points table printed just above it, and prose sections like "Handling Conflicting Evidence" and the Phase 2.5 conceptual framing could be tightened. Not 4 because the duplicated code block is a substantial redundancy rather than a minor trim.

Actionability

Fully executable guidance throughout: named tools with parameters and return shapes ("genome_build ('GRCh37' | 'GRCh38'), variant_description (HGVS, e.g. 'NM_007294.4:c.5266dup')"), a complete runnable classify_acmg function, concrete fallback chains, quantified minimums, an output template, and a file-naming convention. Copy-paste ready and covers the common cases.

Workflow Clarity

The phased pipeline (Phase 1 through 6 with 2.5/4.2/4.5 sub-phases) is clearly sequenced, with a Phase 2.9 short-circuit check, a dedicated "Tool Failure Fallbacks" section, per-phase alternatives ("If no key/server is provisioned, fall back to the ChIPAtlas/ENCODE annotation above and note the predictive gap"), and Quantified Minimums plus a CHECKLIST.md reference acting as explicit checkpoints — matching the validate/recover anchor.

Progressive Disclosure

References are clearly signaled and one level deep (References section plus inline "See CODE_PATTERNS.md for implementation details"), but the full ACMG algorithm — points table, classification cutoffs, and Python implementation (~120 lines) — is inlined even though ACMG_CLASSIFICATION.md is listed as covering exactly that scope, and none of the five referenced files (ACMG_CLASSIFICATION.md, CODE_PATTERNS.md, CHECKLIST.md, EXAMPLES.md, TOOLS_REFERENCE.md) exist in the bundle, so navigation dead-ends. This inlining-plus-missing-files combination fits the 3 anchor (content that should be separate is inline).

Specificity

Lists multiple specific concrete actions with comprehensive coverage: "VUS classification, pathogenicity assessment with cited criteria, structure-based variant impact (AlphaFold/PDB), non-coding/regulatory variant effect prediction with sequence deep-learning models (AlphaGenome, Enformer, Borzoi, ChromBPNet, Evo 2), and producing clinical-grade variant reports". Not 4 because coverage spans coding, non-coding, structural, and reporting workflows with no meaningful gaps.

Completeness

Clearly answers both: the opening sentence plus the capability list states what the skill does, and "Use this whenever a user asks about a variant's significance... or needs ACMG classification" gives an explicit when-clause with concrete trigger phrases. Matches the 5 anchor exactly.

Trigger Term Quality

Covers natural user phrases and their synonyms: "asks about a variant's significance", "intronic/promoter/enhancer/UTR non-coding variant's functional impact", "needs ACMG classification — even if they don't say 'ACMG'", plus domain-native terms (VUS, pathogenicity, molecular tumor boards, return of results). The explicit anticipation of users who omit the keyword 'ACMG' closes the usual synonym gap.

Distinctiveness Conflict Risk

The clinical variant-interpretation niche with ACMG triggers is distinct, but the description's breadth (AlphaFold/PDB structural impact, regulatory deep-learning models) overlaps with plausible sibling skills — the body itself cross-references a 'tooluniverse-sequence-analysis' skill. Not 5 because that overlap risk is more than minimal; not 3 because the core triggers (variant significance, ACMG classification) would not naturally fire a neighboring skill.