SKILLEMALL.ai
GitHub ↗ RU Sign in

BF wechat-mp-auto-publisher

自动撰写技术文章并发布到微信公众号 - 从选题到发布全流程自动化

ClawHub Agent Skills author: lizz0725 v1.0.0 MIT-0 21 files body ≈ 1 382 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process F 30/100 · Will not run — References files that are not bundled: /home/ray/.../examples/images/img1.png

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
B
78/100
safety, quality, tests
Safety 60%
96
Quality 40%
52
Run on models
none yet
Process rating
F
30/100
Will not run
References files that are not bundled: /home/ray/.../examples/images/img1.png
Tools and files w 18
0
Result and completion w 14
0
When it triggers w 12
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 text references files that are not there: add them or drop the references.
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 · 4

✓ No critical or high findings

Medium and low: 4
  • low Exfiltration read-dotenv README.en.md:306
    Reads a .env file
    cat ~/.openclaw/.env
  • low Exfiltration read-dotenv README.en.md:309
    Reads a .env file
    cat ~/.openclaw/workspace/skills/wanx-image-generator/.env
  • low Exfiltration read-dotenv README.md:306
    Reads a .env file
    cat ~/.openclaw/.env
  • low Exfiltration read-dotenv README.md:309
    Reads a .env file
    cat ~/.openclaw/workspace/skills/wanx-image-generator/.env

Files scanned: 21. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-short description under 40 chars: too little signal for triggering
  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning missing-ref reference to a missing file: /home/ray/.../examples/images/img1.png

Process rating: all ten parameters 30/100

Will not run. References files that are not bundled: /home/ray/.../examples/images/img1.png
  • 0Tools and files. 1 referenced file(s) missing: /home/ray/.../examples/images/img1.png
  • 0Result and completion. Does not say what the result is
  • 0When it triggers. No condition that starts the skill
  • 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
  • 30Running it twice. 2 mutating operations with no state check
  • 100Steps. 45 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1382 tokens
  • low 11 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 32: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • -4Absolute local paths (C:\Users, /home/…): not portable
  • -213 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +4Structure: 27 headings
  • +3Step-by-step instructions: 45 items
  • +4Has examples (15 code blocks)
  • +3All 3 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 52.

External checks

ClawHub: suspicious
This skill is openly meant to create and publish WeChat content, but it combines broad activation, credential-backed external actions, and unsafe command execution patterns that need review before installation.
LLM: suspicious (high) · VirusTotal: · 28 May 2026