SKILLEMALL.ai
GitHub ↗ RU Sign in

AC tsmf-wechat-oa

微信公众号全自动写作系统。支持 20 种精美主题，自动生成封面，一键推送草稿箱。 适用场景： - 用户需要生成公众号文章草稿 - 用户想要切换多种排版主题 - 用户需要自动化内容生产流程 - 用户想要批量生成主题演示文章 <example>用户: "帮我写一篇关于人工智能的公众号文章"</example> <example>用户: "Generate a WeChat article about coffee culture"</example> <example>用户: "公众号文章排版，用科技主题"</example>

ClawHub Agent Skills author: onegrown v3.0.0 MIT-0 54 files body ≈ 1 810 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

GeneratorWriting 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
A
90/100
safety, quality, tests
Safety 60%
97
Quality 40%
79
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 Exfiltration read-dotenv CONTRIBUTING.md:51
    Reads a .env file
    cp .env.example .env
  • low Exfiltration read-dotenv README.md:45
    Reads a .env file
    cp .env.example .env
  • low Exfiltration read-dotenv SKILL.md:470
    Reads a .env file
    cp .env.example .env

Files scanned: 54. 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. 26 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1810 tokens
  • 100Running it twice. No mutating operations
  • 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

  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • -35 of 12 scripts are never mentioned in SKILL.md
  • +2Single-language instructions
  • +5Description quotes 3 example trigger phrases
  • +3Description length 266: enough signal without eating the budget
  • +4Structure: 46 headings
  • +3Step-by-step instructions: 26 items
  • +4Has examples (30 code blocks)
  • +4Reference files are cited in the instructions (1 of 5)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.

External checks

ClawHub: suspicious
This skill mostly matches its WeChat writing and draft-publishing purpose, but it also includes under-disclosed draft deletion, broad host inspection, and cleanup/Git scripts that users should review before installing.
LLM: suspicious (high) · VirusTotal: · 29 May 2026