read large webpage or knowledge
This skill processes large webpages and knowledge bases by segmenting content into manageable chunks, extracting key points from each segment in real-time, and systematically storing summaries in an organized knowledge base. Use it when extracting information from lengthy technical documents, research reports, policy materials, or web encyclopedias where maintaining traceability, structure, and focused analysis across multiple reading sessions is essential.
git clone --depth 1 https://github.com/inclusionAI/AWorld /tmp/read-large-webpage-or-knowledge && cp -r /tmp/read-large-webpage-or-knowledge/examples/aworld_quick_start/cli/skills/read_large_webpage ~/.claude/skills/read-large-webpage-or-knowledgeskill.md
### 🧠 Knowledge Base - **Target Scenarios**: Reading long technical documents, research reports, policy documents, web encyclopedias, etc. - **Core Capabilities**: Segment-based retrieval of original text, real-time summarization, and knowledge network construction. - **Supporting Tools**: `get_knowledge_by_lines` (segment-by-segment reading), `add_knowledge` (incremental summary writing). ### 📥 Input Specification Before starting to read, the following should be clarified: 1. The identifier of the knowledge resource to be read (e.g., URL, document ID, file path). 2. The number of lines or paragraph size to pull each time. 3. The current question or topic of focus, to maintain focus during summarization. 4. Output format requirements (paragraph summaries, bullet points, continuous records, etc.). ### 🛠️ Processing Pipeline 1. **Locate Range**: Determine the starting line number and reading length based on user input, and record offsets when necessary for continuation. 2. **Segment-by-Segment Reading**: Call `get_knowledge_by_lines` to pull the original content of the specified range. If the content is too long, it can be scheduled in multiple batches, and record the remaining unread ranges. 3. **Real-Time Analysis**: Extract key points from the pulled segments, annotate keywords, key information, potential issues, or data. 4. **Knowledge Deposition**: Write the refined key points into the knowledge base through `add_knowledge`, along with source line numbers, timestamps, or context descriptions, maintaining structure. 5. **Iterative Progress**: Repeat steps 2-4 until the entire text is read or the user-defined target depth is reached, while maintaining progress indices for recovery. 6. **Global Review**: At periodic nodes, merge stored summaries, generate overall context maps or summaries, and identify missing information. ### 🔁 Iterative Tips - If cross-segment comparison is needed, it is recommended to preserve original fragment IDs for traceability. - For key concepts, additional reasoning skills can be called for verification or expansion. - It is recommended to record unanswered questions in summaries, which should be prioritized when continuing to consult later. ### 📤 Output Template ``` 📍 Reading Progress - Source: ... - Range: Line ... - ... - Remaining: ... 📝 Summary Points - Point 1: ... - Point 2: ... - Point 3: ... 🧾 Stored Knowledge - Knowledge ID: ... - Summary: ... - Reference: ... ⚠️ Pending Issues - ... ``` ### ✅ Output Checklist - Is the reading range and remaining progress accurately annotated? - Does the summary cover key information and context? - Have key points been promptly written to the knowledge base and linked to sources? - Have unresolved issues or parts requiring in-depth exploration been recorded?
Create ad-ready product images (single or collage) by back-solving sub-image sizes from target output ratio, grounding scene design with media_comprehension, generating images via image_generator with strict request params and actor-count control, and pairing each deliverable with a short social tagline for 小红书/抖音.
Create ad-ready product video from product images, with or without character/subject images. The workflow leverages AI-powered image composition, scene understanding, and video generation. Video prompts should follow commercial shot language—visual hooks, product presence, hero shots, detail showcase, function expression, and dynamic visuals.
Automates browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, test web applications, or extract information from web pages.
A professional skill for App Evaluation (evaluating app's performance with score) and App Improvement (giving professional suggestions for improving the app's performance).
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Search and summarize the latest 7 days of AI news and X discussions using public sources plus browser-based X collection. Use for recent AI news, trends, X discussions, industry briefs, and summaries organized into hot topics, viewpoints, and opportunity areas.
An intelligent assistant specialized in handling media files (images/audio/video). **Only for media file analysis**, does not handle document types.\n\n✅ Media files that can be processed:\n- Images: .jpg, .jpeg, .png, .gif, .bmp, .webp, .svg\n- Audio: .mp3, .wav, .m4a, .flac, .aac, .ogg\n- Video: .mp4, .avi, .mov, .mkv, .webm, .flv\n\n❌ Files that cannot be processed (please do not trigger this skill):\n- Documents: .pdf, .doc, .docx, .txt, .md, .rtf\n- Spreadsheets: .xlsx, .xls, .csv, .tsv\n- Presentations: .pptx, .ppt, .key\n- Code: .py, .js, .ts, .java, .cpp, .go, .rs\n- Archives: .zip, .tar, .gz, .rar, .7z\n- Executables: .exe, .bin, .app, .dmg\n- Databases: .db, .sqlite, .sql\n- Configuration files: .json, .xml, .yaml, .yml, .toml, .ini\n- Web pages: .html, .htm, .css\n\n**Trigger conditions**: When the user explicitly requests to analyze image/audio/video content, or when the file extension belongs to the aforementioned media types.".
Analyzes and automatically optimizes existing agents by improving system prompts and tool configuration.