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BD wechat-mp-draft

将本地 Markdown 文章上传到微信公众号草稿箱。当用户提到"上传文章到公众号"、"发布到微信公众号"、"推送到公众号草稿"等场景时应使用本技能。本技能通过调用微信公众平台 API，自动完成 Markdown 转 HTML、封面图生成/上传、创建草稿等全流程操作。

ClawHub Agent Skills author: Sean zheng v1.0.0 MIT-0 8 files body ≈ 477 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process D 41/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationInfrastructuretype 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
88/100
safety, quality, tests
Safety 60%
94
Quality 40%
78
Run on models
none yet
Process rating
D
41/100
Unfinished process
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

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Dangerous commands medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.

For the author

Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.

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
  • medium Dangerous commands cmd-eval-dynamic scripts/run_upload.py:14
    Dynamic code execution from decoded/untrusted input
    exec(open('upload_draft.py', encoding='utf-8').read())
  • low Secrets in code secret-high-entropy-token scripts/upload_draft.py:38
    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 41/100

  • 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
  • 40Consistency. Frontmatter name (wechat-mp-draft) differs from the folder (wechat-mp-draft-cnspica)
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 100Steps. 5 steps
  • 100Execution cost. Instruction body is 477 tokens
  • 100Running it twice. No mutating operations

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
  • -33 of 4 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 3 example trigger phrases
  • +3Description length 134: enough signal without eating the budget
  • +4Structure: 9 headings
  • +3Step-by-step instructions: 5 items
  • +4Has examples (5 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.

External checks

ClawHub: suspicious
This skill appears to automate WeChat draft publishing, but its artifacts reportedly include under-disclosed draft-reading/export behavior and exposed WeChat credentials.
LLM: suspicious (medium) · VirusTotal: · 29 May 2026