Conciseness

The body is lean and token-efficient: the math model is stated tersely ("B(m) = Σ w(t) · λ^(d(m,t) - 1)") with defaults (λ=0.5, α=0.3, β=0.2) and no explanation of concepts Claude already knows. Every section (inputs, model, signals, workflow, output shape) earns its place. Not 4: there is no padded or over-explained passage to trim.

Actionability

The core model is fully specified with concrete formulas, parameter defaults, tier interpretation, and an output template — actionable for an instruction-only skill. Not 5: the output shape is a skeleton with unlabeled fields rather than a concrete filled example, and scoring-signal weighting is left open ("whatever matters for the current priority set") without a concrete method or example calculation.

Workflow Clarity

A clear six-step numbered workflow (build target set → pull graph → compute scores → expand → rank → return) with a defined return payload. Not 3: the sequence is complete and unambiguous with concrete per-step outputs. Not 5: there are no explicit validation or sanity checkpoints (e.g., verifying graph data completeness before ranking), though the read-only analytical nature of the skill lowers that risk.

Progressive Disclosure

Well-organized single-file body with clear sections and a Related Skills map; no nested or buried references and nothing that obviously belongs in a separate file. Not 5: at ~150 lines it exceeds the simple-skill size where organization alone earns full marks, and a worked example or the output-format detail could be split into a reference file if the skill grows.

Specificity

Lists multiple specific concrete actions — "warm intro discovery, bridge scoring, and network gap analysis across X and LinkedIn" — with comprehensive coverage of the skill's scope. Not 4: there are no minor gaps; the three capabilities plus platform scope fully enumerate what the engine does.

Completeness

Has both a clear what ("weighted social-graph ranking for warm intro discovery, bridge scoring, and network gap analysis") and an explicit when ("Use when the user wants the reusable graph-ranking engine itself"). Not 5: the when-clause is phrased as a meta-distinction against sibling skills rather than concrete trigger phrases a user would naturally say; it could be more explicit.

Trigger Term Quality

Good natural keyword coverage ("warm intro", "bridge", "network", "rank", "X", "LinkedIn") that users would plausibly say. Not 5: common variations like "mutuals", "connections", or "introductions" are absent from the description even though the body's trigger examples rely on them.

Distinctiveness Conflict Risk

Clear niche (weighted graph-ranking engine) with an explicit negative boundary ("not the broader outreach or network-maintenance workflow layered on top of it") that minimizes conflict with the sibling skills it names. Minimal overlap risk.