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AC xiaohongshu-note-acquire

小红书作品笔记获取工具。根据关键词获取小红书作品数据，支持按最多点赞、最多评论、最多收藏排序，支持按日期范围筛选，支持单次最多1w条笔记，结果以结构化数据展示。当用户需要获取小红书作品、查询小红书高赞内容、搜索xhs收藏笔记、爬取rednote评论笔记、搜索小红书爆款笔记时使用。触发词：小红书获取、小红书作品、小红书爆款、小红书搜索、小红书热门、小红书笔记查询、小红书高赞、小红书收藏、小红书评论。

ClawHub Agent Skills author: why20261 v0.1.0 MIT-0 26 files body ≈ 2 271 tokens Open the sourceclawhub.ai↗ analyzed 29 h ago

小红书作品笔记获取工具。根据关键词获取小红书作品数据，支持按最多点赞、最多评论、最多收藏排序，支持按日期范围筛选，支持单次最多1w条笔记，结果以结构化数据展示。当用户需要获取小红书作品、查询小红书高赞内容、搜索xhs收藏笔记、爬取rednote评论笔记、搜索小红书爆款笔记时使用。触发词：小红书获取、小红书作品、小红书…

As a process C 53/100 · Has gaps — 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
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
74
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 · 0

✓ No critical or high findings

Files scanned: 26. 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")
  • note frontmatter-key unknown frontmatter key "homepage"

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. 40 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2271 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
  • -229 emoji in the instructions: noise for the model
  • +2Single-language instructions
  • +3Description length 200: enough signal without eating the budget
  • +4Structure: 20 headings
  • +3Step-by-step instructions: 40 items
  • +4Has examples (4 code blocks)
  • +4Reference files are cited in the instructions (3 of 3)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.

External checks

ClawHub: suspicious
The skill appears to retrieve public Xiaohongshu data as advertised, but it automatically saves complete results to a predictable temporary log directory without clear privacy, retention, or opt-out controls.
LLM: suspicious (high) · VirusTotal: · 16 Sept 2026