Conciseness

The body is exceptionally dense: nearly every line carries non-obvious, domain-specific procedural detail (hash reconciliation, evidence-sufficiency gates, state-machine rules) and it never explains concepts Claude already knows — matching “efficient; minor instances of over-explanation”. Not a 5 because the 校验, 控制, and 约束 sections restate validation/gating rules several times, and the 輸出管理 rules partially duplicate the common-constraints block, so a small amount could be trimmed.

Actionability

Concrete, executable guidance dominates: copy-paste-ready commands (validate_report.py / check_completion.py with flags), exact defaults (3 review rounds × 3 reviewers, max 3 parallel interpretations, 5–25 queries, 3–7 initial candidates), concrete paths, and per-step reference links. It stops short of anchor 5 because several orchestration steps invoke sibling skills (research-topic-extractor, research-literature-radar, parallel-vibe) without showing their invocation, and placeholders like {最终报告路径} leave small gaps for the common case.

Workflow Clarity

Seven explicitly numbered phases with stop conditions and gates at each boundary (e.g., “未全量对账或 hash 漂移时停止”), an explicit feedback loop for recovery (phase_entry.py --mode retry superseding the attempt, re-running novelty checks after hypothesis rewrites), a dedicated 失败与恢复 section, and a final completion checklist (required Verifier + BSK Gate + completed status + check script). This matches anchor 5's “explicit validation steps; feedback loops for error recovery; checklists for complex processes”; anchor 4's “minor validation gaps” does not apply — validation is pervasive.

Progressive Disclosure

The SKILL.md is a genuine overview: five top-level guides in references/ are linked inline at their point of use (runtime-guide, research-synthesis, report-template, novelty-check, agent-review-prompt — all real files), scripts are listed and referenced, and sections are well organized. It misses anchor 5 because states/index.json and verifiers/index.json introduce a second reference level (SKILL.md → index.json → states/<state>/STATE.md), which the rubric counts as 2+ levels of nesting rather than the “one-level-deep references” the top anchor requires.

Specificity

The description names the domain and four concrete actions — “提出科学问题、凝练可证伪假设、寻找创新点或判断研究想法价值” (propose scientific questions, distill falsifiable hypotheses, find innovation points, judge research-idea value) grounded in “文献调查” — plus concrete input types (研究资料、实验结果、论文草稿、PR/仓库信息). Not a 5 because downstream capabilities like novelty-checking and report generation are absent, leaving minor gaps in coverage; well above a 3 since multiple specific actions are listed rather than 1–2.

Completeness

Both halves are present: the “当用户提供…时使用” clause gives an explicit, concrete trigger (inputs + desired outcomes), and the “what” is stated through the purpose actions (提出科学问题、凝练可证伪假设…). It falls short of anchor 5 only because the capability statement is embedded in the when-clause rather than leading as a standalone “what this does” sentence; it clearly exceeds anchor 4's weaker-'when' profile since the trigger guidance is highly specific and even includes an explicit non-applicability clause.

Trigger Term Quality

Good natural-language trigger coverage: users would plausibly say 研究资料 (research materials), 实验结果 (experimental results), 论文草稿 (paper draft), PR/仓库信息, and goal phrases like 提出科学问题/创新点. A few common phrasings are missing (e.g., 查新, 选题, 开题, “评估 idea”), so it sits between anchor 4 (“good keyword coverage; a few natural terms missing”) and 5, closer to 4.

Distinctiveness Conflict Risk

A clear niche (literature-grounded research ideation) with distinct triggers, and the explicit “⚠️ 不适用” boundary redirects adjacent requests to research-plan, research-literature-review, and plain brainstorming — directly minimizing wrong-skill triggering. This matches anchor 5 (“clear niche with distinct triggers; minimal conflict risk”) and exceeds anchor 4, which only tolerates minor overlap without active disambiguation.