Conciseness

The body is directive and never explains concepts Claude already knows, but the extraction list is stated three times: "## Inputs" ("exact theorem / lemma / proposition / corollary statement ... notation ... proof sketch"), "Step 1: Extract: exact claim, assumptions, notation, proof sketch", and again in "Step 2: Normalize the Claim". This is the 'mostly efficient but could be tightened' anchor rather than anchor 4, where only minor instances of over-explanation remain. It is not anchor 2 because there is no concept-explanation padding.

Actionability

Guidance is fully concrete: an exact copy-paste output-file template ("## Required File Structure"), three enumerated output modes with their exact contents, named statuses, an explicit verification checklist, and precise formatting rules ("use $...$ for inline math"). Although code-free, the scoring note says instruction-only skills are not penalized for absence of code when guidance is actionable, and this guidance leaves nothing abstract.

Workflow Clarity

A clearly sequenced 6-step workflow with an explicit feasibility checkpoint (Step 3 triage), a final verification checklist (Step 6), and an error-recovery loop ("If a key step still cannot be justified, downgrade the status and write a blockage report instead of forcing a proof"). This matches the anchor-5 example of validation steps plus feedback loops.

Progressive Disclosure

The body is well-organized into clearly headed sections (Constants, Workflow, Required File Structure, Output Modes, Key Rules) in a single flat file, which matches 'good structure; most content is appropriately placed' with minor gaps. It does not reach anchor 5 because at ~220 lines the output-mode details and file-structure template could be split into one-level-deep reference files, and it is not anchor 3 since no content is mis-filed or hard to navigate.

Specificity

Multiple specific concrete actions are listed — "Writes rigorous mathematical proofs", "fill in missing proof steps", "formalize a proof sketch", "determine whether a claimed proof can actually be completed" — giving comprehensive coverage of the skill's capability surface in third-person voice. It clearly matches the 'multiple specific concrete actions; comprehensive coverage' anchor and exceeds the anchor-4 example, which has minor coverage gaps.

Completeness

It explicitly answers both what ("Writes rigorous mathematical proofs for ML/AI theory") and when ("Use when asked to prove a theorem ... or determine whether a claimed proof can actually be completed") with concrete trigger phrases, structurally identical to the anchor-5 example. The 'Use when' clause is explicit and detailed, so the anchor-4 'when could be more specific' cap does not apply.

Trigger Term Quality

Trigger coverage is unusually thorough: "prove a theorem, lemma, proposition, or corollary", "fill in missing proof steps", "formalize a proof sketch", plus Chinese synonyms (补全证明, 写证明, 证明某个命题) that mirror the English phrasings. This meets the 'comprehensive coverage including synonyms' anchor rather than the anchor-4 case of missing natural terms.

Distinctiveness Conflict Risk

It occupies a clear niche (mathematical proof drafting/verification for ML/AI theory) with distinct triggers around theorems, lemmas, and proof completion that few other skills would claim. Overlap with generic paper-editing or writing skills is minimal, matching the 'clear niche with distinct triggers; minimal conflict risk' anchor.