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BC OpenClaw Multi-Agents

别再用一个 AI 单打独斗。这个 Skill 帮你在 OpenClaw 里组建专属 AI 团队：Manager 规划派活，Worker 各自执行，每个交付物强制过质量门。61 个历史人物人格可选，你的团队做完任务不会直接交卷。 Stop relying on a single AI. This skill helps you build a dedicated AI team inside OpenClaw: a Manager that plans and delegates, Workers that specialize, and a mandatory QA gate on every deliverable. 61+ historical persona options — your team actually checks its work before handing it over.

ClawHub Agent Skills author: Ryan v1.1.0 MIT-0 31 files · 5 scripts body ≈ 10 090 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 56/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, consistency

GeneratorInfrastructureAI 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
83/100
safety, quality, tests
Safety 60%
99
Quality 40%
59
Run on models
none yet
Process rating
C
56/100
Has gaps
Inputs and preconditions w 11
0
Progress reporting w 2
0
When it triggers w 12
20
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.
  2. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Secrets in code secret-high-entropy-token README_EN.md:17
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    <img src="https://img.shields.io/badge/Personas-61%2B_H…CF6?style=…" alt="Personas">
    quoted

Files scanned: 31. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning body-long SKILL.md body ≈ 10090 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 56/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 40Consistency. Frontmatter name (OpenClaw Multi-Agents) differs from the folder (openclaw-multi-agents)
  • 40Execution cost. Instruction body is 10090 tokens: crowds the task out of the window
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 100Steps. 63 steps
  • 100Failures and branches. 4 branches, has a failure section
  • 100Running it twice. Mutating operations check current state
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 10 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (15 tags): a typed call is more reliable

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)
  • -225 emoji in the instructions: noise for the model
  • -35 of 5 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 406: enough signal without eating the budget
  • +4Structure: 39 headings
  • +3Step-by-step instructions: 63 items
  • +3Output format is stated explicitly
  • +4Has examples (33 code blocks)
  • +4Reference files are cited in the instructions (4 of 4)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 59.

External checks

ClawHub: suspicious
This skill fits its multi-agent purpose, but it needs Review because it reads and reuses OpenClaw history, creates persistent agents, and under-discloses network and isolation risks.
LLM: suspicious (high) · VirusTotal: · 29 May 2026