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BF wechat-article-writer

End-to-end 微信公众号 (WeChat Official Account) article writing and publishing pipeline. 9-step multi-agent workflow: topic research → Chinese-first writing → blind quality review → fact-check → formatting → human preview → scrapbook illustrations → draft box publish. Use when user asks to write, draft, or publish a WeChat article, or says "forge write/draft/publish/topic/voice/status".

Not recommendedcritical or high security findings
modbender/skill-library-mcp Agent Skills author: modbender MIT 44 files · 2 scripts body ≈ 2 292 tokens Open the sourcegithub.com↗ analyzed 2 d ago

End-to-end 微信公众号 (WeChat Official Account) article writing and publishing pipeline.

As a process F 33/100 · Will not run — References files that are not bundled: references/scrapbook-prompt.md

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
80/100
safety, quality, tests
Safety 60%
81
Quality 40%
79
Run on models
none yet
Process rating
F
33/100
Will not run
References files that are not bundled: references/scrapbook-prompt.md
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

What is at stake

The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.

Dangerous commands
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. Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
  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 · 2

  • high Dangerous commands cmd-pipe-to-shell references/templates.md:203
    Downloads and executes remote code from an unrecognised host (pipe to shell)
    curl -fsSL https://ollama.ai/install.sh | sh
Medium and low: 1
  • low Dangerous commands cmd-privilege references/figure-generation-guide.md:147
    Privilege escalation / world-writable permissions (detector / deny-list definition)
    PUPPET='{"args":["--no-sandbox","--disable-setuid-sandbox"]}'
    detector

Files scanned: 43. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: references/scrapbook-prompt.md

Process rating: all ten parameters 33/100

Will not run. References files that are not bundled: references/scrapbook-prompt.md
  • 0Tools and files. 1 referenced file(s) missing: references/scrapbook-prompt.md
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 11 mutating operations with no state check
  • 40Consistency. Frontmatter name (wechat-article-writer) differs from the folder (wechat-article-forge)
  • 50Failures and branches. 0 branches, has a failure section
  • 100Steps. 10 steps
  • 100Execution cost. Instruction body is 2292 tokens
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low The response is described with custom markup (4 tags): a typed call is more reliable

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)
  • +3Output format is not stated: the model decides each time
  • -31 of 3 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 384: enough signal without eating the budget
  • +4Structure: 11 headings
  • +3Step-by-step instructions: 10 items
  • +4Has examples (2 code blocks)
  • +4Reference files are cited in the instructions (13 of 13)

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