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multi-expert-analyzer2

召唤多领域专家智能体,对任何棘手、跨学科、有争议或开放性的问题做并行深度分析,再经过一轮克制的事实核查与红队反驳,最后由一个"干净大脑"的终稿撰写者把所有内容无缝重写成一篇第一人称、面向小白、图文并茂、有人味、留有证伪空间的深度长文。触发条件:用户提出任何值得深挖的复杂问题,尤其是跨领域、需要多方视角、需要权威数据支撑的问题;提到"帮我深度分析"、"多专家视角"、"专家怎么看"、"这个问题涉及哪些领域"、"帮我搞清楚 XX 到底是怎么回事"、"用第一性原理分析一下"、"这个观点站得住脚吗"、"帮我论证一下"、"multi-expert-analyzer2",或者只是抛出一个开放性问题如"为什么 XX 会发生"、"如何看待 XX 现象"、"XX 到底靠不靠谱"。即使用户没有明确说"专家"或"深度分析"这几个字,只要问题本身跨越多个专业领域、需要审慎论证而非泛泛而谈,也应使用本 skill。本 skill 与仓库中其他类似名称的技能(如 multi-expert-analyzer、multi-expert-analyzer1)相互独立,不共享模板或输出结构,请完全按照本文件的流程执行。

65

Quality

77%

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SecuritybySnyk

Passed

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tessl review fix ./skills/skills_for_claude_web/multi-expert-analyzer2/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

67%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.

The body is a well-structured, actionable workflow with clear sequencing, self-verification loops, and concrete file-naming conventions. Its main weakness is conciseness: requirement lists are duplicated across the expert and final-draft steps, and several rationale paragraphs restate motivation Claude can infer.

Suggestions

Consolidate the duplicated requirements: 证伪/证明, 第一性原理/前置条件, and 适用边界 appear in both Step 2 and Step 4 — reference a single shared list instead of restating it.

Trim the motivational rationale ('核心理念…' and the sub-agent/context-pollution explanation) down to the procedural instructions; Claude can infer the why.

Add at least one worked example (a sample question → abbreviated expert draft → final article excerpt) so the qualitative final-draft criteria ('有人味', '标题要一针见血') have a concrete anchor.

DimensionReasoningScore

Conciseness

Mostly procedural and assumes Claude's competence (no basic-concept padding), but it restates the same requirements — 证伪/证明, 第一性原理/前置条件, 适用边界 — in both Step 2 and Step 4, and includes motivational rationale paragraphs ('核心理念…', the sub-agent/context-pollution explanation) that could be trimmed.

3 / 5

Actionability

Concrete, executable guidance throughout: exact file paths (markdown/expert-<领域简称>.md, markdown/fact-check.md, markdown/red-team.md, markdown/final-<主题slug>.md, assets/*.svg), an 8-point expert checklist, and a specific final-draft requirement list; the gap is the absence of any worked input/output example.

4 / 5

Workflow Clarity

A clearly sequenced 6-step process with explicit checkpoints (Step 2 point 8 self-verify with a fix→re-check loop, Step 6 final self-check against the Step 4 list) and checklists; the minor gap is that validation is subjective self-inspection rather than objective pass/fail criteria.

4 / 5

Progressive Disclosure

No bundle files exist and none are needed for this self-contained workflow; the body is well-organized with clear section headers (何时使用, 整体流程, 第一步…第六步, 输出清单) and a flow diagram, though the ~120-line monolith could optionally offload the detailed checklists to a reference.

4 / 5

Total

15

/

20

Passed

Description

87%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.

The description is strong on completeness and trigger-term coverage, clearly stating both what the skill does and when to use it with many natural trigger phrases. Its main weakness is distinctiveness: the broad entry criteria and near-identical sibling names mean the natural-language triggers do not reliably single out this version.

Suggestions

Add disambiguating triggers that distinguish this skill from multi-expert-analyzer / multi-expert-analyzer1 — currently the natural-language triggers match all three equally and only the literal 'multi-expert-analyzer2' string is distinctive.

Narrow the entry criteria: '任何值得深挖的复杂问题' and generic open-question patterns ('为什么 XX 会发生', '如何看待 XX 现象') invite triggering on almost any question; tie triggers more tightly to the multi-expert + fact-check + red-team + rewrite pipeline intent.

State what makes this version's output or structure different from the siblings so the right skill can be chosen from behavior, not just from a version number.

DimensionReasoningScore

Specificity

Names the domain (multi-expert analysis of complex questions) and several concrete workflow actions — '召唤多领域专家智能体', '并行深度分析', '事实核查', '红队反驳', '重写成…长文' — giving broad coverage of what each phase does.

4 / 5

Completeness

Explicitly answers both 'what' (summon experts → fact-check → red-team → clean-brain rewrite into a first-person long-form article) and 'when' (a dedicated 触发条件 clause with concrete trigger phrases, plus a fallback rule for implicit triggers).

5 / 5

Trigger Term Quality

Comprehensive set of natural phrases users would actually say — '帮我深度分析', '多专家视角', '专家怎么看', '用第一性原理分析一下', '这个观点站得住脚吗', '为什么 XX 会发生', '如何看待 XX 现象' — covering synonyms and open-question variations.

5 / 5

Distinctiveness Conflict Risk

The skill claims independence from sibling skills multi-expert-analyzer and multi-expert-analyzer1, but the natural-language triggers ('任何值得深挖的复杂问题', '为什么 XX 会发生') are broad and shared with those siblings; only the literal string 'multi-expert-analyzer2' truly disambiguates, so overlap risk with similar skills remains.

3 / 5

Total

17

/

20

Passed

Validation

100%

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

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
digoal/blog
Reviewed

Table of Contents

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