AlphaMoatによると、2026-09時点でXiaohongshu Search Summarizerのダウンロード数は4,717、スター数は8で、AIスキル全体5,078件中2,007位、ナレッジ管理では442件中191位です。
| プラットフォーム | インストール数 | ダウンロード数 | スター数 | コメント |
|---|---|---|---|---|
| ClawHub ↗ | 158 | 4717 | 8 | - |
プラットフォームごとに集計方法と集計時点が異なります。
投稿本文、画像、コメントを収集し、分析レポートに統合できます。ブラウザーを使い、収集した内容や画像をローカルに保存します。取得した情報は信頼できる事実として扱わず、重要な主張は独立して確認し、専用ディレクトリの出力をレビューしてから共有してください。 playwright-cli、Python、requests が必要です。パス区切りを含まない単純なキーワードと専用ディレクトリを使い、必要なログインは許可を得て手動で行います。画像を確認してから視覚分析を述べ、すべての投稿や認証画面を通過できるとは扱いません。max_posts は 1–50 に制限されます。
資料公開日 2026-10-27
スキルのドキュメントをもとに生成した例であり、実際の実行結果ではありません。
2026-09 から集計方法を変更し、同名スキルを作者ごとに分けて集計し、全スキルを対象にしました。破線は変更前の月で、変更後のデータとは直接比較せず、変更をまたぐ増減も計算していません。
| 月 | ダウンロード数 | 前月比 | スター数 |
|---|---|---|---|
| 2026-09 | 4717 | — | 8 |
| 2026-08 | 4592 | +3.9% | 8 |
| 2026-07 | 4418 | +9.0% | 8 |
| 2026-06 | 4055 | +5.3% | 7 |
| 2026-05 | 3852 | — | 7 |
| 2026-03 | 3022 | — | 7 |
作者が公開した元のファイルをテキストのまま表示しています。AlphaMoat による編集や審査はしていません。リンクはクリックできません。コマンドは内容を確認してから実行してください。取得日 2026-10-23。
--- name: xiaohongshu-search-summarizer description: Searches Xiaohongshu(小红书) for a given keyword, extracts the top N posts (including texts, images, and user comments), and then synthesizes a comprehensive final analytical report. Use this skill whenever the user wants to search Xiaohongshu for a specific topic, compile research seamlessly combining text and images, or needs an aggregated thematic summary of social media posts, comments, and visual data on a given subject. compatibility: Requires playwright-cli and python3, and the Python 'requests' package installed. --- # 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) 5. **You** MUST use your file reading capabilities to read the `[keyword]_raw_data.md` file. 6. 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! 7. **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). 8. **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: ```bash /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**: ```bash /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.
長いテキストや文書、ウェブページを要点とキーワードに圧縮する要約ツールです。
IMA OpenAPI を通じてノートとナレッジベースを操作します。
ウェブ要求に適切なツールを割り当てるための必読ガイドです。
Baidu AI 検索 API でウェブを検索するスキルです。
uvx 経由で markitdown を呼び、さまざまな形式のファイルを Markdown に変換します。
OpenClaw の既定のウェブ検索で、複数のエンジンをリアルタイムに引きます。
DuckDuckGo のエンドポイントでウェブ、ニュース、画像、動画を検索するスキルです。
利用できる OpenClaw スキルを検索して見つけるスキルです。
データ月:2026-09 · ダウンロード数、スター数、インストール数は公開スキルレジストリ(ClawHub、SkillHub)から月単位で集計しています。詳しくはデータの方法論をご覧ください。