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Xiaohongshu Search Summarizer

piekill/xiaohongshu-summarizer-skill
172 installs6 stars
Summary

This automates scraping Xiaohongshu posts for any keyword you throw at it, bypassing the platform's aggressive anti-bot defenses by spinning up a headed Playwright browser that looks like a real user. It grabs the top N posts with their text, images, and comments, downloads everything locally, then hands you a raw markdown file. The real value is in phase two: Claude reads that file, actually views the images with vision capabilities, and synthesizes everything into a proper analytical report instead of just dumping post lists at you. You'll need playwright-cli and Python with requests installed. Useful when you're doing market research or competitive analysis and need to pull together what Chinese users are actually saying and showing about a topic on Little Red Book.

Install to Claude Code

npx -y skills add piekill/xiaohongshu-summarizer-skill --skill xiaohongshu-search-summarizer --agent claude-code

Installs into .claude/skills of the current project.

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Files
SKILL.mdView on GitHub

Xiaohongshu Search and Summarize

This skill automates the process of extracting high-quality multi-modal content (text + images) from Xiaohongshu (小红书) and actively assists you in generating a deeply integrated, analytical final report for the user. Due to Xiaohongshu's aggressive anti-scraping mechanisms, direct HTTP requests or naive scraping often result in 404s or blocks. This skill natively bypasses these by simulating a real user through the playwright-cli in a headed browser window.

It operates in two distinct phases:

Phase 1: Subagent Data Collection

  1. Simulate a search for the keyword on Xiaohongshu in a headed browser.
  2. Advance through image sliders to fully load all lazy pictures from the top N posts.
  3. Extract titles, descriptions, top comments, and all high-resolution images.
  4. Download those images to a local directory and generate a raw data document ([keyword]_raw_data.md).

Phase 2: AI Multi-Modal Synthesis (Your Job)

  1. You MUST use your file reading capabilities to read the [keyword]_raw_data.md file.
  2. Inside the raw data markdown, you will find paths to image files. You MUST use your file reading / vision capabilities on these image file paths to actually ingest and "see" their visual content. If you skip this step, you are only reading file names, not the images themselves!
  3. You analyze the texts, summarize the genuinely useful comments (discarding noise like "pm me"), and interpret the semantic content of the images you just viewed (e.g. diagrams, guidelines, step-by-step UI flows).
  4. You compile everything into a beautifully synthesized, single comprehensive report rather than just a linear list of posts.

Dependencies

  • playwright-cli (Must be available on the path)
  • python3 (Required to download images and stitch the raw data markdown)
  • requests Python package (pip install requests) — used by parse.py to download images

Usage Instructions

Step 1: Run the Extraction Script

Execute the wrapper script in scripts/run.sh. It accepts the following arguments:

/bin/bash <skill_dir>/scripts/run.sh "YOUR KEYWORD" <MAX_POSTS> <OUTPUT_DIRECTORY>
  • YOUR KEYWORD: The search term to look up on Xiaohongshu.
  • <MAX_POSTS>: (Optional, default = 10) The number of top posts to scan.
  • <OUTPUT_DIRECTORY>: (Optional, default = ./) Directory where the raw data and images will be saved.

Example execution:

/bin/bash ~/.claude/skills/xiaohongshu-search-summarizer/scripts/run.sh "openclaw使用场景" 10 "./xhs_report_openclaw_scenarios"

Step 2: Read Raw Data & Images

Once the bash script finishes successfully, navigate to the OUTPUT_DIRECTORY and use your file reading capabilities to ingest the generated [keyword]_raw_data.md file.

Inside this file, you will find descriptions, comments, and file paths pointing to post_X_img_Y.webp or post_X_img_Y.jpg.

Step 3: Synthesis & Summarization

This is the most critical step. Do not just return the raw markdown file to the user. Instead, write a polished comprehensive markdown report that reorganizes the information logically, while retaining a high level of detail.

Follow these strict compilation rules:

  • Do not list posts individually (e.g. avoid "Post 1: ... Post 2: ...").
  • Read the Images: You MUST use your file reading and vision capabilities on the .webp or .jpg image files found in the raw data directory to interpret their contents.
  • Detailed & Comprehensive Synthesis: Provide a highly detailed summary that includes diverse viewpoints, nuances, and specific examples found across different posts. Avoid over-summarizing or losing important context; preserve the richness and diversity of the information.
  • Extract and merge themes: Group ideas by concepts, steps, recurring themes, or pros/cons.
  • Evaluate comments: Merge insights from valuable comments directly into the core narrative. Skip useless or repetitive comments, but preserve diverse opinions or helpful counter-arguments from the comments section.
  • Integrate images contextually: Embed the most relevant and high-quality images directly into the flow of your final report to support the analytical points being made. Describe their visual meaning based on what you saw with your vision capabilities.
  • Save to OUTPUT_DIRECTORY: Save your beautifully compiled final Markdown report using your file writing capabilities directly into the same <OUTPUT_DIRECTORY> as the raw data (e.g., <OUTPUT_DIRECTORY>/[keyword]_synthesis.md), and give the user the path to it.

Error Handling

If you encounter 404 Not Found or "element not visible" errors during the browser invocation:

  • Keep in mind that Xiaohongshu may demand a login challenge. If the site pauses waiting for a login, instruct the user to verify the playwright-cli browser window and perform necessary authentication manually, then try the script again.
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First SeenJun 3, 2026
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