large-document-reader

# Literature Structuring Expert Automatically decompose long documents (papers, reports, books) into a structured, AI-friendly knowledge base. Splits by chapter, generates machine-readable summaries, and builds a navigable index to overcome context limits. ## When to Use This Skill Use this skill when the user: - Has a document that is too long for the AI's context window. - Needs to perform cross-chapter analysis or get a high-level overview of a long text. - Wants to build a reusable, queryable knowledge base from a PDF, Markdown, or text file. - Asks: "How can I get my AI to read this whole book/paper?" ## Quick Reference | Situation | Action | |-----------|--------| | User provides a long document | 1. Analyze and split it into chapters.<br>2. Generate a JSON summary for each chapter.<br>3. Create a master index file. | | User asks a high-level, cross-chapter question | Provide the content of the `MASTER_INDEX.md` file to the AI. | | User asks a detailed, chapter-specific question | Provide the corresponding single file from the `./chapters/` directory to the AI. | | Task completed | Present the generated file tree and `MASTER_INDEX.md` preview to the user. | ## Core Workflow ### Phase 1: Intelligent Splitting 1. **Analyze Input**: Receive the long document text or file path. 2. **Identify Structure**: Automatically analyze the document to identify heading hierarchies (e.g., `#`, `##`, `1.`, `1.1`) to determine chapter boundaries. Prioritize user-specified splitting preferences. 3. **Execute Split**: Split the document into independent plain-text files by chapter. * **Naming Convention**: `{sequence_number}_{chapter_title}.md` (e.g., `01_Introduction.md`). * **Storage Location**: All chapter files are saved in the `./chapters/` directory. ### Phase 2: Summary Generation & Structuring 1. **Generate Summary per Chapter**: For each file in `./chapters/`, generate a corresponding JSON summary file. * **Structured Fields** (JSON format): ```json { "chapter_id": "Unique identifier matching the filename, e.g., 02_1", "chapter_title": "Chapter Title", "abstract": "Core summary of the chapter, 200-300 words.", "keywords": ["Keyword1", "Keyword2", "Keyword3"], "key_points": ["Key point one", "Key point two"], "related_sections": ["IDs of other chapters strongly related to this one"] } ``` * **Storage Location**: JSON summary files are saved in the `./summaries/` directory (e.g., `01_Introduction.summary.json`). ### Phase 3: Create Global Index 1. **Aggregate Information**: Collect data from all JSON files in `./summaries/`. 2. **Generate Index**: Create a global index file, `MASTER_INDEX.md`. * **Content**: Lists all chapters' IDs, titles, a short abstract preview, and keywords in a Markdown list or table. * **Purpose**: Provides a "bird's-eye view" for quick navigation and high-level Q&A. ## Final Deliverables & File Structure Upon completion, the following file tree is generated: ``` Project_Root/ ├── chapters/ # 【Source Repository】Contains all split chapter texts (.md files) │ ├── 01_Introduction.md │ ├── 02_1_Experimental_Methods.md │ └── ... ├── summaries/ # 【Summary Repository】Contains all structured JSON summaries │ ├── 01_Introduction.summary.json │ ├── 02_1_Experimental_Methods.summary.json │ └── ... └── MASTER_INDEX.md # 【Global Navigation】Core document summary index ``` ## Usage Instructions for the User **For Global, Cross-Chapter Queries** (e.g., “What is the paper's main thesis?”): * Provide the content of the `MASTER_INDEX.md` file to the AI. This is token-efficient. **For Specific, In-Depth Queries Within a Chapter** (e.g., “What were the parameters in the 'Methods' section?”): * Provide the corresponding single chapter file from the `chapters/` directory to the AI for full context.