multi-expert-analyzer

深度思考分析解答任何领域的棘手问题。当用户提出的问题具备以下任一特征时必须触发此 skill：(1) 涉及多个领域（如"AI 对就业的影响"涉及技术、经济、社会学、哲学）；(2) 涉及争议性观点或对立立场（如"比特币是不是泡沫"）；(3) 需要严谨论证、权威数据支撑、第一性原理推理（如"为什么中国新能源车销量这么猛"）；(4) 用户明确要求深度分析、多视角交叉验证、给出适用边界和证伪/证明手段。即便用户没有点名"深度分析"，只要问题本身具备跨学科、权衡性、需多专家视角的特征，都应触发本 skill，而不是给出浅尝辄止的单线回答。本 skill 不适用于闲聊、事实查询、明确要求简短回复的场景。

Quality

86%

Does it follow best practices?

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Securityby

Passed

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Quality

Content

77%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A highly actionable and well-sequenced orchestration skill with strong validation feedback loops, but it pays for it in token efficiency: quality constraints and prompt rationales are repeated across phases, and the large prompt templates that belong in separate reference files are all inlined rather than progressively disclosed.

Suggestions

Extract the four Agent prompt templates (expert, fact-checker, red-team, synthesizer) into separate reference files under references/ and point to them with one-level-deep links, cutting SKILL.md substantially and improving progressive_disclosure.

De-duplicate the recurring quality constraints (第一性原理 / 数据权威 / 边界 / 可证伪 / 克制): state them once in the '关键质量约束' section and reference rather than re-explaining them inside each phase's prompt and the final checklist.

Trim the multi-paragraph rationale blocks (e.g., the '如果我是 X' and '敢放人味' explanations) to a single directive line each to reduce over-explanation of concepts the model already grasps.

Dimension	Reasoning	Score
Conciseness	Mostly efficient but carries notable redundancy — the SVG naming rules, '克制' restraint principle, and 第一性原理/边界/证伪 quality constraints are each restated across the expert prompt, synthesis prompt, checklist, and '灵魂' section, and the '如果我是 X' guidance is elaborated with multi-paragraph rationale that could be trimmed.	3 / 5
Actionability	Fully executable guidance: copy-paste-ready Agent prompt templates with {placeholder} substitution, explicit output paths (markdown/experts/N-<领域>.md), mkdir -p commands, and concrete output formats covering the common orchestration cases.	5 / 5
Workflow Clarity	Clear five-phase sequence (阶段0→4) with explicit validation checkpoints — expert self-verification with '不通过就重写' retry loops, fact-checker and red-team as validation stages feeding back into rewrites, a final checklist, and an exception-handling section with feedback loops for mid-flow problem changes and quality failures.	5 / 5
Progressive Disclosure	Good internal structure (section headers, workflow diagram, output directory tree) but no references/, scripts/, or assets/ bundles exist and the four large prompt templates (专家/核查员/红队/合成) — clear candidates for separate reference files — are all inlined in a ~384-line monolithic SKILL.md.	3 / 5
	Total	16 / 20 Passed

Description

95%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A high-quality description that clearly states both capability and explicit trigger conditions with natural user-language examples, plus thoughtful exclusions that reduce conflict risk. The only minor gap is that the named actions are abstract analytical processes rather than concrete file-level operations.

Dimension	Reasoning	Score
Specificity	Names the domain (cross-disciplinary tricky questions) and lists several concrete analytical actions — '多视角交叉验证', '第一性原理推理', '给出适用边界和证伪/证明手段' — but they are abstract process actions rather than fully enumerated concrete operations, leaving minor coverage gaps.	4 / 5
Completeness	Explicitly answers both 'what' (深度思考分析解答棘手问题) and 'when' via four numbered trigger conditions plus an explicit '不适用于闲聊、事实查询、明确要求简短回复' exclusion clause, with concrete trigger phrases throughout.	5 / 5
Trigger Term Quality	Comprehensive coverage of natural user-language trigger phrases with concrete examples users would actually say ('AI 对就业的影响', '比特币是不是泡沫', '为什么中国新能源车销量这么猛') plus synonyms like '深度分析' and '多视角'.	5 / 5
Distinctiveness Conflict Risk	Clear niche (cross-disciplinary multi-expert deep analysis with first-principles and falsification) with an explicit disambiguation against 'product-multi-role-analysis' and concrete non-trigger scenarios, giving minimal conflict risk.	5 / 5
	Total	19 / 20 Passed

Validation

93%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
relative_links	Relative link issues: 2 missing	Warning

	Total	15 / 16 Passed

Repository: digoal/blog
Path: skills/multi-expert-analyzer/SKILL.md
Commit: 3b9c83d

Reviewed: 3 days ago

Table of Contents

Discovery Implementation Validation

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