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AD content-fetch-skill

多站点网页内容抓取工具，基于 Playwright 浏览器自动化抓取 Twitter/X.com 推文、知乎文章/回答、微信公众号文章、今日头条、虎嗅等站点，以及任意通用网页。统一通过 URL 自动匹配站点并执行抓取，输出结构化 JSON、页面截图和图片。当用户要求"抓取/爬取/下载/获取"网页文章、推文、公众号文章、知乎回答、新闻内容时使用。触发词：抓取推文、爬取 Twitter、爬取 X、下载推文、抓取知乎、爬取知乎、抓取公众号、爬取微信文章、抓取头条、爬取头条、抓取虎嗅、爬取虎嗅、抓取网页、爬取文章、下载文章、获取网页内容、保存网页、采集内容。

ClawHub Agent Skills author: RuiChong v1.0.0 MIT-0 12 files body ≈ 1 120 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ReferencePlaywrightInfrastructuretype 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%
99
Quality 40%
77
Run on models
none yet
Process rating
D
46/100
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

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
  • low Dangerous commands cmd-background-process references/troubleshooting.md:151
    Starts a background / autostarted process
    nohup ./mihomo -d . > /tmp/mihomo.log 2>&1 &

Files scanned: 12. 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 46/100

  • 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
  • 60Tools and files. Uses tools (web) that frontmatter does not declare
  • 100Steps. 19 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1120 tokens
  • 100Running it twice. No mutating operations
  • 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)
  • +3Output format is not stated: the model decides each time
  • -2localhost URLs: will not work for another user
  • +1No license
  • +2Single-language instructions
  • +3Description length 278: enough signal without eating the budget
  • +4Structure: 13 headings
  • +3Step-by-step instructions: 19 items
  • +4Has examples (5 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)
  • +3All 6 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.

External checks

ClawHub: suspicious
This is a real webpage scraping tool, but it deserves review because it can use logged-in browser sessions and includes anti-detection and security-bypass browser settings.
LLM: suspicious (high) · VirusTotal: · 29 May 2026