Conciseness

The body is lean and dense: tables instead of prose (evidence-type/reverse-search table, 6-dimension qualitative table, time-budget table), an ASCII evidence-chain skeleton, and per-pass time budgets — with zero padding or explanation of concepts Claude already knows. Not 4 because there is no over-explanation to trim; every section carries operational information.

Actionability

Guidance is largely executable: copy-paste-ready reverse-search commands per evidence type (e.g. `data:"<具体数字>"`, `<作者> <期刊> <年份>`), a concrete default weighting (L1=30%/L2=30%/L3=40%), a report template to copy, and a named script. It falls short of the 5 anchor only because `scripts/evidence-extractor.py` is referenced twice with no invocation example or arguments, so that step is not fully copy-paste ready.

Workflow Clarity

The six-pass workflow is explicitly sequenced ("按顺序执行,每遍只解决一层问题"), each pass has a time budget and a defined question, and verification checkpoints are explicit: reverse-search per high-risk evidence, "每个核心主张找 ≥2 个独立(无相互引用)来源" triangulation, counter-evidence (反证 R) in the chain check, plus a degradation path when time is short. This matches the anchor with explicit validation steps and error/edge handling.

Progressive Disclosure

SKILL.md is an overview that inlines only the decision-level detail and points to a real, one-level-deep bundle: all 7 referenced reference files, the script, and the report template exist, spot-checking confirms the reference files are substantive (peer cross-links at most, no 'see details.md' chains), and a Resources section indexes each file with its purpose. Content is appropriately split and navigation is easy.

Specificity

The description lists multiple concrete actions — "逐段逐句验证文章真伪、证据链与逻辑结构" plus "checking logical consistency, evidence chains, source reliability, fabrication, non-quantifiable conclusions, and producing a scored report with original-text citations" — comprehensive coverage with no vague filler. Not 4 because coverage spans verification, evidence-chain analysis, fabrication detection, and a defined output artifact, matching the multi-action comprehensive anchor.

Completeness

It explicitly answers both: what ("三层审查模型" that verifies authenticity, evidence chains, and logical structure, producing a scored cited report) and when ("Use when the user asks to fact-check, verify, audit, or evaluate the credibility of..."). This matches the anchor example for a clear what plus a concrete 'Use when' trigger clause; neither half is missing or weak.

Trigger Term Quality

Natural trigger terms users would actually say are well covered: "fact-check, verify, audit, or evaluate the credibility of an article, essay, report, opinion piece, social-media post, or any written claim" — synonyms (fact-check/verify/audit/evaluate) and artifact types are enumerated. Not 4 because essentially all common phrasings of this need are present rather than 'a few natural terms missing'.

Distinctiveness Conflict Risk

It occupies a clear niche (article/claim credibility auditing with a scored, citation-backed report) with triggers that are distinct from writing, summarizing, or general research skills. Minor overlap with source-verification behaviors exists, but the enumerated triggers are specific enough that conflict risk is minimal.