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BD multi-agent-team

多智能体协作（整合与进阶·元能力）。给定一项任务/一个待决问题，自动完成： 角色分工(提议者/批判者/事实核查/综合裁决) → 任务派发 → 多视角作答 → 交叉验证(reason-verify) → 投票聚合/辩论择优 → 输出带置信度的共识结论。 对标一线大模型智能体的「多 agent 辩论/自一致性」能力，且把每个 agent 的产出 都过一遍可靠自验证、按可靠度加权投票。当复杂决策需要多视角、且要 抑制单点幻觉时使用。

ClawHub Agent Skills author: qq435912743 v1.0.0 MIT-0 5 files body ≈ 239 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process D 38/100 · Unfinished process — weak spots: steps, result and completion, when it triggers

AnalyzerAI and agentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
70
Run on models
none yet
Process rating
D
38/100
Unfinished process
Steps w 15
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 0

✓ No critical or high findings

Files scanned: 5. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "agent_created"
  • note frontmatter-key unknown frontmatter key "visibility"

Process rating: all ten parameters 38/100

  • 0Steps. Prose only: no discrete steps
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 100Tools and files. No external tools needed
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 239 tokens
  • 100Running it twice. No mutating operations

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3No numbered steps or checklist
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +3Description length 215: enough signal without eating the budget
  • +4Structure: 5 headings
  • +4Has examples (2 code blocks)
  • +3All 2 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 70.

External checks

ClawHub: clean
This skill runs a local multi-agent-style decision helper and writes local reports/learning files, with no evidence of hidden exfiltration or destructive behavior.
LLM: benign (medium) · VirusTotal: · 14 Aug 2026