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AC zhihu-to-wechat

全自动知乎热榜选题 → IT科技风格公众号文章生成 → 自动配图 → 微信服务号发布工作流。 当用户提到"知乎热点"、"公众号文章"、"帮我写公众号"、"热榜选题"、"微信推文"、"IT科技文章"、 "发布公众号"等场景时，必须触发此skill。适用于科技博主、IT自媒体、技术内容创作者。

ClawHub Agent Skills author: dachenchen690-droid v1.0.0 9 files body ≈ 710 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureInfrastructuretype 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
A
92/100
safety, quality, tests
Safety 60%
97
Quality 40%
84
Run on models
none yet
Process rating
C
53/100
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 · 3

✓ No critical or high findings

Medium and low: 3
  • low Secrets in code secret-high-entropy-token scripts/wechat_publisher.py:73
    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/wechat_publisher.py:200
    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/wechat_publisher.py:241
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    boundary = "----…Wxk"
    quoted

Files scanned: 9. 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 53/100

  • 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
  • 100Tools and files. No external tools needed
  • 100Steps. 38 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 710 tokens
  • 100Running it twice. No mutating operations
  • low 10 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

  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 2 example trigger phrases
  • +3Description length 145: enough signal without eating the budget
  • +4Structure: 12 headings
  • +3Step-by-step instructions: 38 items
  • +4Has examples (5 code blocks)
  • +4Reference files are cited in the instructions (2 of 2)
  • +3All 4 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.

External checks

ClawHub: suspicious
This skill has a coherent WeChat draft workflow, but it handles powerful account credentials and cached tokens with too little scoping and user control.
LLM: suspicious (high) · VirusTotal: · 29 May 2026