Xiaohongshu Skill#

This is a Python Playwright based OpenClaw skill for Xiaohongshu/Rednote public-content workflows. It reads page data from Xiaohongshu web pages, mainly through window.__INITIAL_STATE__, and returns structured JSON.

It includes the original capabilities:

• QR-code login and login status checking
• Keyword search with sort/type/time filters
• Note detail extraction
• Comment loading for note detail pages
• User profile extraction
• Explore feed extraction
• Optional publishing and interaction commands retained from the upstream skill

It also adds:

• Xiaohongshu Demand Discovery Collector

The demand discovery mode searches demand-style keywords, collects recent high-interaction notes and comments, cleans the data, removes raw user identity fields, and writes structured files for later LLM demand analysis or product-manager agent workflows.

What This Skill Is#

This skill is a Xiaohongshu content and comment collection tool. The demand discovery collector can:

• Search recent notes using demand-oriented keywords such as 求推荐, 避雷, 平替, 真实测评, 后悔买, 踩坑
• Filter search results to notes from the last week
• Sort by most comments by default
• Visit note detail pages
• Load comments
• Prefer notes published within the recent --days window
• Preserve notes whose publish time cannot be parsed as publish_time_unknown
• Clean comments by removing empty, duplicate, short-noise, and obvious advertising comments
• Save structured output files for downstream analysis

What This Skill Is Not#

This skill is not:

• A tool for bypassing Xiaohongshu login, captcha, rate limits, or risk controls
• An automatic like/comment/follow/publish bot for demand discovery
• An LLM demand analysis tool
• A product-manager agent
• A complete SaaS product
• An unrestricted high-volume crawler

Use it for learning, research, internal product validation, and small-scale public-content collection.

When OpenClaw Should Use It#

Prefer demand-discovery when the user says things like:

• “帮我跑一次小红书需求发现”
• “抓小红书最近几天高互动笔记和评论”
• “用小红书评论区挖用户需求”
• “分析小红书用户在求推荐、避雷、平替里的需求”
• “采集小红书热门笔记评论，后面喂给 LLM 分析”

Use search when the user only wants to search a Xiaohongshu keyword:

bash

python -m scripts search "关键词"

Use feed when the user provides a concrete note id/link context and wants note detail or comments:

bash

python -m scripts feed <feed_id> <xsec_token> --load-comments --max-comments=20

Use qrcode or check-login when login state is unknown or expired.

Do not use interaction or publishing commands for demand discovery. The collector itself does not call comment.py, interact.py, or publish.py.

Installation#

Run all commands from the skill root directory.

bash

pip install -r requirements.txt
playwright install chromium

On Linux/WSL, Chromium dependencies may also be required:

bash

playwright install-deps chromium

Login#

First login by QR code:

bash

python -m scripts qrcode --headless=false

Check login status:

bash

python -m scripts check-login

If cookies expire, run the QR-code login again.

Command Reference#

Search#

bash

python -m scripts search "美食" --sort-by=最新 --note-type=图文 --publish-time=一周内 --limit=10

Common search options:

• --sort-by: 综合, 最新, 最多点赞, 最多评论, 最多收藏
• --note-type: 不限, 视频, 图文
• --publish-time: 不限, 一天内, 一周内, 半年内
• --search-scope: 不限, 已看过, 未看过, 已关注
• --location: 不限, 同城, 附近
• --limit: returned result limit

Feed Detail#

bash

python -m scripts feed <feed_id> <xsec_token>
python -m scripts feed <feed_id> <xsec_token> --load-comments --max-comments=20

Explore Feed#

bash

python -m scripts explore --limit=20

The explore feed exists, but demand discovery should prefer keyword search in the first version.

User Profile#

bash

python -m scripts user <user_id> [xsec_token]
python -m scripts me

Demand Discovery Collector#

Basic command:

bash

python -m scripts demand-discovery

Small-scale test:

bash

python -m scripts demand-discovery --keywords "求推荐" --posts-per-keyword 1 --search-limit 3 --max-comments 5 --headless=false

Specify multiple keywords:

bash

python -m scripts demand-discovery --keywords "求推荐,避雷,平替" --posts-per-keyword 2 --search-limit 5 --max-comments 10 --headless=false

Use a keyword file:

bash

python -m scripts demand-discovery --keywords-file keywords.txt

Important parameters:

• --keywords: comma-separated keywords
• --keywords-file: UTF-8 text file, one keyword per line
• --days: recent-day window for note filtering, default 3
• --search-publish-time: Xiaohongshu search time filter, default 一周内
• --sort-by: default 最多评论, also supports 最多点赞
• --note-type: default 不限
• --posts-per-keyword: notes saved per keyword, default 3
• --search-limit: search results inspected per keyword, default 8
• --max-comments: valid comments saved per note, default 20
• --output-dir: output directory; default data/demand_discovery/<timestamp>/
• --timezone: default Asia/Shanghai
• --headless: true or false

Default demand keywords:

text

求推荐
避雷
平替
真实测评
后悔买
踩坑
好用吗
怎么选
值不值得买
学生党
新手必备
替代品
不好用
怎么解决

Output files:

• notes_clean.jsonl: one note-level record per saved/attempted note
• comments_clean.jsonl: cleaned comment-level records
• collection_summary.json: machine-readable summary and counters
• collector_report.md: human-readable report

The collector uses one browser session per run:

1. XiaohongshuClient.start()
2. LoginAction.check_login_status()
3. Reused SearchAction
4. Reused FeedDetailAction
5. XiaohongshuClient.close()

Privacy And Data Safety#

Demand discovery output must not save raw Xiaohongshu usernames, nicknames, avatars, or profile links. It writes author_hash instead:

text

sha256("xiaohongshu:" + raw_author_id)

The preferred raw author id is user_id. If unavailable, the collector may hash another available author field such as nickname or profile link, then discard the original value.

Safety And Compliance Boundaries#

• Collect only publicly accessible content.
• Do not bypass login, captcha, rate limits, or platform risk controls.
• If captcha is triggered, stop and ask the user to handle it manually.
• If login/cookie is invalid, stop and ask the user to log in again.
• Do not save raw usernames, nicknames, avatars, or profile links.
• Use author_hash only for deduplication and structured analysis.
• Do not call comment.py, interact.py, or publish.py for demand discovery.
• Do not run large high-frequency collections.
• Keep first-version usage small, conservative, and reviewable.

Troubleshooting#

• Not logged in: run python -m scripts qrcode --headless=false
• Cookie expired: login again by QR code
• Captcha triggered: stop collection, wait, and handle verification manually in visible browser mode
• Empty comments output: reduce batch size, test with --headless=false, and confirm comments load on the note page
• Too many publish_time_unknown: Xiaohongshu may have changed detail fields; inspect raw note detail structure before relying on recent-day filtering
• Detail failures from search results: demand-discovery uses pc_search as xsec_source; if this becomes unstable, test the original feed command behavior with pc_feed