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微信公众号每日文章自动创作技能。搜索热点 → 撰写SEO优化文章 → 生成配图 → 上传草稿箱。含爆款标题模板、内容类型模板、搜一搜SEO优化。

ClawHub Agent Skills author: jimo970 v1.8.0 MIT-0 8 files body ≈ 2 059 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureWriting and documentsInfrastructuretype 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
86/100
safety, quality, tests
Safety 60%
98
Quality 40%
68
Run on models
none yet
Process rating
C
51/100
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.
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 · 2

✓ No critical or high findings

Medium and low: 2
  • low Secrets in code secret-high-entropy-token scripts/create_draft.py:42
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    boundary = '----…0gW'
    quoted
  • low Secrets in code secret-high-entropy-token scripts/create_draft.py:63
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    boundary = '----…0gW'
    quoted

Files scanned: 8. 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")

Process rating: all ten parameters 51/100

  • 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
  • 30Running it twice. 2 mutating operations with no state check
  • 100Tools and files. No external tools needed
  • 100Steps. 69 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2059 tokens
  • low 15 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 72: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • -231 emoji in the instructions: noise for the model
  • -31 of 2 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +4Structure: 34 headings
  • +3Step-by-step instructions: 69 items
  • +4Has examples (19 code blocks)
  • +4Reference files are cited in the instructions (3 of 3)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 68.

External checks

ClawHub: suspicious
This looks like a real WeChat article automation skill, but it also adds under-scoped Feishu/Douyin distribution features and unsafe remote image downloading that users should review before installing.
LLM: suspicious (high) · VirusTotal: · 29 May 2026