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BD fill-content

筛出 mixdao 无正文条目（hasContent 为 false），按 URL 抓取正文；上传前用 AI 梳理为约 250 字案例描述（简体中文、突出人物/公司等）并以此替代正文回写。需 MIXDAO_API_KEY；更新时另需 ANTHROPIC_API_KEY。触发示例：「补全正文」「拉取无正文条目的正文并更新」「fill content」「无正文」「抓正文」「补全内容」「更新正文」「mixdao 正文」。

modbender/skill-library-mcp Agent Skills author: modbender MIT 5 files body ≈ 443 tokens Open the sourcegithub.com↗ analyzed 3 d ago

筛出 mixdao 无正文条目（hasContent 为 false），按 URL 抓取正文；上传前用 AI 梳理为约 250 字案例描述（简体中文、突出人物/公司等）并以此替代正文回写。需 MIXDAOAPIKEY；更新时另需…

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

ProcedureAI and agentsWriting and documentstype 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
87/100
safety, quality, tests
Safety 60%
95
Quality 40%
75
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.

Exfiltration 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 instructions or scripts send data out: environment variables, keys, file contents, chat history. You may never notice, because the agent performs the upload as "part of the task".

For the author

If the upload is not needed for the task, remove it: catalogs flag such skills and delist them. If it is needed, name the destination explicitly, say what leaves the machine, and give the user a switch.

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 · 1

✓ No critical or high findings

Medium and low: 1
  • medium Exfiltration net-redirectable-api-key scripts/03-update-from-temp.js:104
    Helper sends the API key to a host configured by an environment variable — the key can be redirected to another server
    API key + configurable base URL from environment

Files scanned: 5. 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 (fill-content) differs from the folder (fill-content-by-mixlab)
  • 60Tools and files. Uses tools (web, node) that frontmatter does not declare
  • 100Steps. 5 steps
  • 100Execution cost. Instruction body is 443 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

  • +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
  • +1No license
  • +2Single-language instructions
  • +3Description length 209: enough signal without eating the budget
  • +4Structure: 8 headings
  • +3Step-by-step instructions: 5 items
  • +4Has examples (4 code blocks)
  • +3All 3 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.