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Xiaohongshu Search

Free

Efficiently search and extract Xiaohongshu notes by keyword.

by browser-act5.3k stars on browser-act/skills
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Updated Aug 5, 2026
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Free · Opens the source repo

What Xiaohongshu Search does

Xiaohongshu Search is a skill designed for users who need to efficiently find and extract notes from Xiaohongshu, a popular social media platform. By entering a keyword, users can retrieve a paginated list of notes that includes essential information such as the title, author, engagement statistics (likes, collects, comments), and a cover image URL. This skill is particularly useful for marketers, content creators, and researchers who want to monitor trends, discover KOL content, or gather insights from user-generated posts on Xiaohongshu.

To use the skill, the user must have a browser open to the Xiaohongshu search results page and be logged into their account. The skill operates by reading data already displayed on the page, which means it does not bypass any authentication or access controls. This ensures that the data extraction process is secure and respects user privacy. The skill utilizes Python scripts to automate the extraction process, making it faster and more efficient than manual copy-pasting.

Users can apply various filters to refine their search results, including sorting by popularity, recency, or engagement metrics. This flexibility allows users to tailor their searches to specific needs, whether they are looking for the most liked posts or the latest content. The skill also supports pagination, enabling users to scroll through results and load more items seamlessly.

Overall, Xiaohongshu Search is a valuable tool for anyone looking to leverage the vast amount of content on Xiaohongshu for research, marketing, or content creation purposes. It streamlines the process of finding relevant notes, saving users time and effort in their search endeavors.

When to use it

Use this skill when you need to quickly find and extract notes from Xiaohongshu based on specific keywords.

When not to use it

This skill is not suitable for users who are not logged into Xiaohongshu, as it requires authentication to access search results.

What you can build with it

Market Research

Use this skill to gather insights on trending topics and popular content on Xiaohongshu for market analysis.

Content Creation

Quickly find and extract user-generated content to inspire your own posts or campaigns.

KOL Monitoring

Monitor key opinion leaders' posts and engagement on Xiaohongshu to inform your influencer marketing strategies.

How to install Xiaohongshu Search

View source

1. Install with the skills CLI

npx skills add browser-act/skills/xiaohongshu-search --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by browser-act

Xiaohongshu — Search Notes

keyword → list of notes with title, author, engagement stats, xsecToken

Language

All process output to user (progress updates, process notifications) follows the user's language.

Objective

Search Xiaohongshu notes by keyword and extract the result list including engagement metrics and tokens for downstream detail lookup.

Prerequisites

  • Browser opened to https://www.xiaohongshu.com/search_result/?keyword={keyword}
  • User is logged in (avatar or username visible in the left sidebar)

Pre-execution Checks

1. Tool Readiness

If browser-act has been confirmed available in the current session → skip this step.

Invoke browser-act via Skill tool to load usage. If installation or configuration issues arise, follow its guidance to resolve then retry.

2. Login Verification

If login status for Xiaohongshu has been confirmed in the current session → skip this step.

Otherwise: open https://www.xiaohongshu.com and observe the left sidebar:

  • User avatar or "Me" entry visible → logged in, continue execution
  • "Login" button visible → not logged in, inform the user that login is required, use remote-assist to let the user scan the QR code

User refuses or cannot log in → terminate execution.

Capability Components

This Skill's operational boundary = what the user can manually do in their browser. It only reads data already displayed to the user on the page, never bypassing authentication or access controls. Its role is equivalent to copy-pasting on the user's behalf — the data is already on screen, automation merely saves time. JS code is encapsulated in Python files under the scripts/ directory, invoked via eval "$(python scripts/xxx.py {params})". $(...) is bash syntax; it is recommended to use the bash tool for execution.

Below are all atomic capabilities discovered and verified during the exploration phase, listed by command template with parameters. Simply invoke them as needed — no need to read scripts/*.py source code or re-verify. Only inspect scripts when execution fails for troubleshooting. Combine freely as needed during execution.

DOM: extract search results

Navigate to the search page (business parameters injected via URL), wait for the Vue SSR state to populate, then extract from window.__INITIAL_STATE__.search.feeds:

  1. navigate https://www.xiaohongshu.com/search_result/?keyword={keyword}
  2. wait stable
  3. (optional) apply filters — see AI Workflow below
  4. eval "$(python scripts/extract-search.py --limit {limit})"

Parameters:

  • {keyword}: URL-encoded search keyword (e.g., travel, coffee)
  • --limit: max items to return from current feeds buffer, default 20

Output example:

{
  "total": 44,
  "hasMore": true,
  "page": 2,
  "items": [
    {
      "id": "69d8cd8c0000000022002295",
      "xsecToken": "ABpK6gG0Dmt6MoVt60wJf-J0VMaCw5Y1Hi766ap7uWrxE=",
      "type": "normal",
      "title": "Not Switzerland! This is a natural grassland in Fujian!!",
      "userId": "5bac4e3f7a4c7300016a6b88",
      "nickname": "half-goose",
      "likedCount": "5149",       // likes
      "collectedCount": "4170",   // collects/saves
      "commentCount": "390",      // comments
      "coverUrl": "https://sns-..."
    }
  ]
}

Error handling: if error: true is returned, verify the page URL is a search result page and wait stable has completed before retrying.

AI Workflow: apply sort and note-type filters (before extraction)

Run this workflow before the extraction step when the user specifies a sort order or note type. Uses the filter panel on the search result page:

  1. state — locate the "Filter" button in the top-right area of the search content area → click <index>

  2. Wait for filter panel to appear (visible on the right side of the page)

  3. For sort orderstate locate the desired sort tag in the "Sort By" row → click <index>

    UI LabelfilterParams value
    General (default)general
    Latesttime_descending
    Most Likedpopularity_descending
    Most Commentedcomment_descending
    Most Collectedcollect_descending
  4. For note typestate locate the desired type tag in the "Note Type" row → click <index>

    UI LabelfilterParams value
    All (default)
    Videovideo-note (site internal)
    Image-textimage-text-note (site internal)
  5. state locate the "Collapse" button at the bottom of the filter panel → click <index>

  6. wait stable

  7. Then run: eval "$(python scripts/extract-search.py --limit {limit})"

Enum Parameters

[AI] sort — filterParams.tags[0] value for the sort_type filter. Acquisition: open filter panel via state + click, read "Sort By" row options. Verified values: general, time_descending, popularity_descending, comment_descending, collect_descending.

[AI] note_type — filterParams.tags[0] value for the filter_note_type filter. Acquisition: open filter panel via state + click, read "Note Type" row options. Verified values: video-note type (obtained by clicking "Video" option), image-text-note type (obtained by clicking "Image-text" option).

time_filter [collection failed]: time filter API parameter value not captured — UI interaction applies filter but POST body parameter mapping was not observed.

Pagination

DOM Pagination: scroll down --amount 3000wait stable → re-run eval "$(python scripts/extract-search.py --limit {limit})". Each scroll loads ~20 more results into feeds. Termination: hasMore: false in extraction output.

Success Criteria

result.items.length >= 1 AND result.items[0].id is non-null

Known Limitations

  • Search requires login; without login the page shows a QR code overlay and feeds is empty
  • Filter interaction applies changes to the current page's Vue state; after page navigation or reload, filters reset to defaults

Execution Efficiency

  • Batch orchestration: Write a bash script to loop through the command templates serially within a single session; do not parallelize within one browser (prone to triggering anti-scraping restrictions). Refer to rate information in "Known Limitations" above to add appropriate intervals. To increase throughput, open multiple stealth browser sessions and distribute work across them — each session has an independent fingerprint so rate limits apply per session
  • Test before batch execution: After writing a batch script, you must first test with 1-2 items to verify the script runs correctly; only then run the full batch. Never skip testing and execute in batch directly
  • Reduce redundant pre-operations: When multiple steps depend on the same prerequisite state, complete them in batch under that state to avoid repeatedly establishing the same state
  • Error resumption: Save results item by item during batch processing; on failure, resume from the breakpoint rather than starting over

Experience Notes

Path: {working-directory}/browser-act-skill-forge-memories/xiaohongshu-data-xiaohongshu-search.memory.md (working directory is determined by the Agent running the Skill, typically the project root or current working directory)

Before execution: If the file exists, read it first — it records unexpected situations encountered during past executions (e.g., a strategy has become ineffective); adjust strategy order accordingly.

After execution: If an unexpected situation is encountered (strategy became ineffective, page redesigned, anti-scraping upgraded, better path discovered), append a line: {YYYY-MM-DD}: {what happened} → {conclusion}

Normal execution does not write to the file. Do not record what keywords were used or how many results were returned — those are task outputs, not experience.

Frequently asked questions about Xiaohongshu Search

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