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🏛️ 三省六部制 · OpenClaw Multi-Agent Orchestration System — 9 specialized AI agents with real-time dashboard, model config, and full audit trails. Use when: (1) 需要多智能体协作完成复杂任务, (2) 需要实时管理和监控AI代理状态, (3) 需要配置和调度不同AI模型, (4) 需要完整的操作审计和合规日志, (5) 需要构建复杂的AI工作流编排, (6) 需要智能体之间的任务分配和协调, (7) 需要权限管理和资源分配, (8) 需要安全策略和合规审查.

ClawHub Agent Skills author: Ache v1.0.0 MIT-0 9 files · 2 scripts body ≈ 4 226 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process D 37/100 · Unfinished process — weak spots: steps, result and completion, when it triggers

AnalyzerAI and agentsInfrastructureData and analyticstype 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
37/100
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

    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: 8. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 37/100

    • 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
    • 70Execution cost. Instruction body is 4226 tokens
    • 100Tools and files. No external tools needed
    • 100Consistency. Name and required fields are in place
    • 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
    • -2localhost URLs: will not work for another user
    • -215 emoji in the instructions: noise for the model
    • -41 reference files, but SKILL.md never points to them: the model will not open them
    • -32 of 2 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +3Description length 307: enough signal without eating the budget
    • +4Structure: 30 headings
    • +4Has examples (17 code blocks)

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

    External checks

    ClawHub: suspicious
    This skill appears purpose-aligned, but its default setup can expose a sensitive dashboard and alter the user’s Python environment without enough control.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026