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把小红书 Web 端当前登录用户「收藏」tab 的收藏笔记批量导出为本地 Markdown（每篇含标题、原文链接、正文、图片/视频 URL），支持 offset/count 分批断点续导。当用户需要导出/备份/检索自己的小红书收藏内容时唤起。

ClawHub Agent Skills author: 石建 v1.0.2 MIT-0 5 files body ≈ 487 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

把小红书 Web 端当前登录用户「收藏」tab 的收藏笔记批量导出为本地 Markdown（每篇含标题、原文链接、正文、图片/视频 URL），支持 offset/count 分批断点续导。当用户需要导出/备份/检索自己的小红书收藏内容时唤起。

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureWriting 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%
98
Quality 40%
70
Run on models
none yet
Process rating
C
53/100
Has gaps
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 · 2

✓ No critical or high findings

Medium and low: 2
  • low Obfuscation obf-base64-blob wc3-code.mjs:2
    Long base64-looking blob (detector / deny-list definition)
    const _0x58ce15=_0x4d13;(function(_0x5787d1,_0x53c4a9){const _0x19d87a=_0x4d13,_0xcf5d78=_0x5787d1();while(!![]){try{const _0x5d05cd=parseInt(_0x19d87a(0x1c0))/0x1*(parseInt(_0x19d87a(0x1c2))/0x2)+-pa
    detector
  • low Secrets in code secret-high-entropy-token wc3-code.mjs:2
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    const _0x58ce15=_0x4d13;(function(_0x5787d1,_0x53c4a9){const _0x19d87a=_0x4d13,_0xcf5d78=_0x5787d1();while(!![]){try{const _0x5d05cd=parseInt(_0x19d87a(0x1c0))/0x1*(parseInt(_0x19d87a(0x1c2))/0x2)+-pa
    detector

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 53/100

  • 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
  • 100Tools and files. No external tools needed
  • 100Steps. 20 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 487 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
  • -2localhost URLs: will not work for another user
  • +1No license
  • +2Single-language instructions
  • +3Description length 121: enough signal without eating the budget
  • +4Structure: 6 headings
  • +3Step-by-step instructions: 20 items
  • +4Has examples (3 code blocks)

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

External checks

ClawHub: suspicious
The documented Xiaohongshu export tool is mostly coherent, but the package also includes an obfuscated, undocumented LLM helper that does not fit the stated purpose.
LLM: suspicious (high) · 18 Aug 2026