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multi-agent-meeting

模拟多个AI智能体协作开会并进行决策讨论的场景。适用于需要从多个专业角度分析问题、进行辩论和达成共识的场景,如项目决策、技术方案评审、商业策略制定等。

58

Quality

66%

Does it follow best practices?

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SecuritybySnyk

Passed

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Fix and improve this skill with Tessl

tessl review fix ./skills/multi-agent-meeting/multi-agent-meeting/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

57%

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

The body is well-organized with a clear five-phase workflow and good progressive disclosure through verified reference files, but its guidance is more descriptive than concretely actionable and lacks validation checkpoints. Tightening the examples and adding specific actionable detail per phase would lift the weaker dimensions.

Suggestions

Tighten the 使用示例 section and trigger-condition lists to reduce token overhead and move conciseness toward the lean score-3 anchor.

Add concrete, executable detail to each meeting phase (e.g., specific prompting patterns or output-checking criteria) rather than descriptive directives like "鼓励不同观点的碰撞".

Introduce an explicit validation/consensus-check step in the 共识收敛 or 决策生成 phase (e.g., confirm all agents' positions are reconciled before finalizing) to add a checkpoint to the workflow.

DimensionReasoningScore

Conciseness

The body is mostly efficient and does not teach concepts Claude already knows, but the examples section and enumerated trigger-condition lists add length that could be tightened, matching the score-2 anchor rather than the lean score-3 example.

2 / 3

Actionability

It offers concrete structure (five labeled phases, role-config fields, real reference files) but the guidance remains largely descriptive (e.g., "鼓励不同观点的碰撞") with no executable specifics, fitting the score-2 anchor of some-but-incomplete concrete guidance.

2 / 3

Workflow Clarity

The five meeting phases are clearly sequenced and numbered, but there are no explicit validation/checkpoint steps, matching the score-2 anchor of a present sequence with missing checkpoints; not capped lower since the task is non-destructive.

2 / 3

Progressive Disclosure

SKILL.md is a concise overview pointing to real one-level-deep resources (references/agent-roles.md, references/meeting-record-format.md, assets/meeting-templates/), all verified present and clearly signaled via the 资源索引 section, matching the score-3 anchor.

3 / 3

Total

9

/

12

Passed

Description

75%

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 clearly conveys both capability and trigger context with an explicit applicability clause, giving it strong completeness and a distinct niche. It is slightly held back by abstract action phrasing and formal rather than conversational trigger terms.

DimensionReasoningScore

Specificity

Names the domain and core actions ("模拟多个AI智能体协作开会并进行决策讨论", "辩论和达成共识") but stays at a fairly abstract level rather than enumerating multiple discrete concrete actions like the score-3 anchor.

2 / 3

Completeness

Explicitly answers what ("模拟多个AI智能体协作开会并进行决策讨论的场景") and when ("适用于...如项目决策、技术方案评审、商业策略制定等"), with an explicit trigger clause as the score-3 anchor requires.

3 / 3

Trigger Term Quality

Includes relevant trigger phrases ("项目决策、技术方案评审、商业策略制定") that users might say, but the formal phrasing misses more common conversational variations, matching the score-2 anchor.

2 / 3

Distinctiveness Conflict Risk

Multi-agent meeting/decision simulation is a distinct niche with its own triggers, unlikely to overlap with unrelated skills; it is not above 3 as the scale caps there.

3 / 3

Total

10

/

12

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
anbeime/skill
Reviewed

Table of Contents

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