本文へ移動
cccskills
無料GitHub で公開

xiaohongshu-search

Search Xiaohongshu (RedNote / xhs) notes by keyword and return a paginated list with title, author, engagement stats (likes, collects, comments), cover image URL, and xsecToken for detail lookup. Use when user mentions find notes on xiaohongshu, search rednote, search xhs, scrape xiaohongshu search, xiaohongshu keyword search, rednote post search, xhs search results, monitor xiaohongshu topics, KOL content discovery via xiaohongshu, xiaohongshu note list, rednote scrape, xhs data collection, collect xiaohongshu posts, xiaohongshu topic search, xiaohongshu content monitoring, rednote post list, xhs keyword scrape.

インストール方法を見る

含まれるファイル(2)

  • SKILL.md8.4 KB
  • scripts/extract-search.py1.6 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

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 order — state 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 type — state 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 3000 → wait 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.

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Extracts comprehensive wholesale product data from 1688.com product detail pages: title, tiered pricing, SKU variants with dimensions/weight, product images, seller info, shop scores, buyer protection, cross-border flags, product attributes, coupon/promotion data, and review stats. Use when user mentions 1688, 1688.com, wholesale China, alibaba wholesale, B2B China sourcing, Chinese wholesale scraper, 1688 product scrape, 1688 offer, 1688 detail, extract 1688 data, pull 1688 listings, get wholesale price, 1688 supplier info, factory stats 1688, 1688 SKU variants, 1688 product attributes, 1688 shop score, DSR score 1688, 1688 buyer protection, 1688 cross-border, 1688 dropship. Also applies to: scraping bulk product data from 1688 by offer ID list, monitoring 1688 supplier metrics, extracting 1688 pricing tiers for resale analysis.

日本語の概要は準備中です。原文の説明を表示しています。

browser-act/skills6,1382026年8月24日 更新

Fetches complete Airbnb listing details for a given numeric listing ID via the internal GraphQL API, returning title, room type, description, amenities, photos, coordinates, city, house rules, highlights, ratings, review count, bedroom configuration, and property overview. Use when user mentions Airbnb listing details, Airbnb property info, Airbnb room details, get Airbnb listing data, Airbnb amenities list, Airbnb house rules, Airbnb property description, Airbnb detail page scraper, Airbnb rooms detail, Airbnb property page data, Airbnb listing info, fetch Airbnb room details, pull Airbnb listing.

日本語の概要は準備中です。原文の説明を表示しています。

browser-act/skills6,1382026年8月24日 更新

Extracts Airbnb accommodation search results from a destination query via SSR-embedded data, returning listing ID, URL, name, coordinates, rating, price, photos, and badge info for each result, plus pagination cursors for multi-page retrieval. Use when user mentions Airbnb search results, Airbnb listings, vacation rental search, short-term rental listings, scrape Airbnb, get Airbnb data, find rentals on Airbnb, Airbnb destination search, Airbnb property list, Airbnb stays search, Airbnb accommodation results, pull Airbnb listings, collect Airbnb search data, Airbnb scraper, Airbnb search page extraction, Airbnb search by destination.

日本語の概要は準備中です。原文の説明を表示しています。

browser-act/skills6,1382026年8月24日 更新

Amazon Alexa for Shopping Q&A automation: submits questions to Amazon's Alexa/Rufus AI shopping assistant and collects response text; supports optional keyword search context (navigate to search results page before asking for category-specific answers). Use when user mentions Amazon Alexa, Rufus, Amazon shopping assistant, Amazon AI chat, ask Amazon, Amazon Q&A, automate Alexa questions, Rufus chatbot, Amazon assistant automation, collect Alexa responses, bulk question submission to Amazon, keyword search context, category research. Also applies to extracting Amazon product recommendations from conversational AI, automating repeated queries to Amazon's AI shopping feature, collecting Alexa shopping responses at scale, or market research within a specific product category.

日本語の概要は準備中です。原文の説明を表示しています。

browser-act/skills6,1382026年8月24日 更新

This skill helps users extract structured product details from Amazon using a specific ASIN (Amazon Standard Identification Number). Use this skill when the user asks to get Amazon product details by ASIN, lookup Amazon product title and price using ASIN, extract Amazon product ratings and reviews count for a specific ASIN, check Amazon product availability and current price, get Amazon product description and features via ASIN, enrich product catalog with Amazon data using ASIN, monitor Amazon product price changes for specific ASINs, retrieve Amazon product brand and material information, fetch Amazon product images and specifications by ASIN, validate Amazon ASIN and get product metadata.

日本語の概要は準備中です。原文の説明を表示しています。

browser-act/skills6,1382026年8月24日 更新

This skill helps users extract structured best-selling product data from Amazon via the BrowserAct API. Agent should proactively apply this skill when users express needs like search for best selling products on Amazon, extract Amazon product data based on keywords, find top rated Amazon products, monitor Amazon competitor prices and sales, discover trending products on Amazon marketplace, extract Amazon product titles prices and ratings, gather Amazon product sales volume for market research, search Amazon best sellers in specific region, collect Amazon product reviews and promotion details, analyze Amazon product availability and badges, get Amazon product data for market analysis.

日本語の概要は準備中です。原文の説明を表示しています。

browser-act/skills6,1382026年8月24日 更新

browser-act のスキルをすべて見る

このスキルの問題を報告する