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10个AI模块=1支MCN团队，覆盖选题策划、标题优化、内容写手、SEO优化、视频导演、封面设计、社群运营、数据分析、商务报价、多平台排期，一个人完成从选题到变现的全链路。说"帮我想选题""怎么拍视频""接广告多少钱"等自然语言即可触发。

ClawHub Agent Skills author: Pluto417-Qing v0.1.0 MIT-0 5 files body ≈ 7 269 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

10个AI模块=1支MCN团队，覆盖选题策划、标题优化、内容写手、SEO优化、视频导演、封面设计、社群运营、数据分析、商务报价、多平台排期，一个人完成从选题到变现的全链路。说"帮我想选题""怎么拍视频""接广告多少钱"等自然语言即可触发。

As a process C 52/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureCanvaMarketingtype 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
C
71/100
safety, quality, tests
Safety 60%
100
Quality 40%
28
Run on models
none yet
Process rating
C
52/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
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.
  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 · 0

✓ No critical or high findings

Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • error name-missing SKILL.md: frontmatter has no `name`
  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning body-long SKILL.md body ≈ 7269 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 52/100

  • 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 7269 tokens
  • 100Tools and files. No external tools needed
  • 100Steps. 230 steps
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. No mutating operations
  • low 21 top-level sections: this looks like several domains in one skill

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)
  • +3Description length 119: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • -2129 emoji in the instructions: noise for the model
  • +2Single-language instructions
  • +4Structure: 32 headings
  • +3Step-by-step instructions: 230 items
  • +4Has examples (33 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 28.

External checks

ClawHub: clean
This is a content-creator assistant skill that gives planning, writing, SEO, analytics, pricing, and scheduling advice without hidden execution, persistence, or account-control behavior.
LLM: benign (high) · VirusTotal: · 31 Jul 2026