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AC rednote-research

Research a topic through RedNote/Xiaohongshu discussion signals using either public-web mode (no login) or optional login-enhanced browser review when the user explicitly chooses deeper access. Use when checking RedNote community sentiment, reputation, latest policy/community updates, gossip/drama/news synthesis, local recommendations like restaurants/shops, when recovering evidence from weak public-web snippets/titles/OCR/subtitle fragments, or when analyzing posts, comments, screenshots, image posts, video/gif snippets, subtitles, or audio/transcript clues. Especially useful for prompts like "查小红书口碑", "搜 RedNote 讨论", "看看最近有什么风向/新政策", "总结八卦/争议", "找本地探店推荐", "分析评论区", "分析截图/视频/字幕", "根据截图线索继续搜", "总结某个账号最近发了什么", or "做一个 RedNote 社区情报初筛".

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 15 files body ≈ 4 142 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Research a topic through RedNote/Xiaohongshu discussion signals using either public-web mode (no login) or optional login-enhanced browser review when the…

As a process C 57/100 · Has gaps — weak spots: result and completion, inputs and preconditions

ProcedureMedia and videotype 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
97/100
safety, quality, tests
Safety 60%
100
Quality 40%
92
Run on models
none yet
Process rating
C
57/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Tools and files w 18
60
the three weakest of ten parameters · all ten

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 · 0

    ✓ No critical or high findings

    Files scanned: 15. 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 57/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 70Failures and branches. 10 branches
    • 70Execution cost. Instruction body is 4142 tokens
    • 85Steps. 165 steps, 1 vague phrases
    • 100Consistency. Name and required fields are in place
    • 100Running it twice. Mutating operations check current state
    • 100Progress reporting. Reports progress
    • low 13 top-level sections: this looks like several domains in one skill
    • low The response is described with custom markup (9 tags): a typed call is more reliable

    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

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 9 example trigger phrases
    • +3Description length 742: enough signal without eating the budget
    • +4Structure: 32 headings
    • +3Step-by-step instructions: 165 items
    • +4Reference files are cited in the instructions (10 of 10)
    • +3All 3 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 92.