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XiaoHongShu (Little Red Book) data collection and interaction toolkit. Use when working with XiaoHongShu (小红书) platform for: (1) Searching and scraping notes/posts, (2) Getting user profiles and details, (3) Extracting comments and likes, (4) Following users and liking posts, (5) Fetching home feed and trending content. Automatically handles all encryption parameters (cookies, headers) including a1, webId, x-s, x-s-common, x-t, sec_poison_id, websectiga, gid, x-b3-traceid, x-xray-traceid. Supports guest mode and authenticated sessions via web_session cookie.

modbender/skill-library-mcp Agent Skills author: modbender MIT 28 files body ≈ 1 815 tokens Open the sourcegithub.com↗ analyzed 2 d ago

XiaoHongShu (Little Red Book) data collection and interaction toolkit.

As a process D 44/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureSecuritytype 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%
96
Quality 40%
82
Run on models
none yet
Process rating
D
44/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: xiaohongshu (modbender/skill-library-mcp)

How to improve

    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 · 4

    ✓ No critical or high findings

    Medium and low: 4
    • low Obfuscation obf-base64-blob scripts/request/web/encrypt/decrypt_xs_xsc.py:88
      Long base64-looking blob (quoted — discussed, not commanded)
      encoded_p = "2UQAPsHC+aIjq…sQh+aHCH0rEw/HAwn+Y+Aqly…dP0+hJnR…Abx+/ZIPeZl+eW
      quoted
    • low Secrets in code secret-high-entropy-token scripts/request/web/encrypt/decrypt_xs_xsc.py:88
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      encoded_p = "2UQAPsHC+aIjq…sQh+aHCH0rEw/HAwn+Y+Aqly…dP0+hJnR…Abx+/ZIPeZl+eW
      quoted
    • low Secrets in code secret-high-entropy-token scripts/request/web/encrypt/other/XhsFpGenerator.py:243
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      fp = {'x1': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/140.0.0.0 Safari/537.36', 'x2': 'false', 'x3': 'zh-CN', 'x4': '24', 'x5': '8', 'x6': '24', 'x7': 'G
      quoted
    • low Obfuscation obf-base64-blob scripts/request/web/encrypt/web_encrypt_config.ini:23
      Long base64-looking blob (quoted — discussed, not commanded)
      BASE…BLE = "NOPQ…p89+/AUVq…9xy"
      quoted

    Files scanned: 28. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 44/100

    • 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
    • 40Consistency. Frontmatter name (xiaohongshu) differs from the folder (xiaohongshutools)
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 85Steps. 40 steps, 2 vague phrases
    • 100Execution cost. Instruction body is 1815 tokens
    • 100Running it twice. No mutating operations
    • low 10 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 564: enough signal without eating the budget
    • +4Structure: 29 headings
    • +3Step-by-step instructions: 40 items
    • +4Has examples (17 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.