Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · AI
large-document-reader
Intelligently splits long academic or technical documents into chapters, generates structured JSON summaries for each, and creates a file system with a globa...
skill openclawclawhub Free
Intelligently splits long academic or technical documents into chapters, generates structured JSON summaries for each, and creates a file system with a globa...
⬇ 0 downloads ★ 0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- _meta.json, SKILL.md, scripts/extract_chapters.py, scripts/save_chapters.py
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.2
Provenance
- Publisher
- MRchenkuan
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
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
SituationActionUser provides a long document1. 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 questionProvide the content of the MASTER_INDEX.md file to the AI.User asks a detailed, chapter-specific questionProvide the corresponding single file from the ./chapters/ directory to the AI.Task completedPresent the generated file tree and MASTER_INDEX.md preview to the user.
Phase 1: Intelligent Splitting
Analyze Input: Receive the long document text or file path. Identify Structure: Automatically analyze the document to identify heading hierarchies (e.g., #, ##, 1., 1.1) to determine chapter boundaries. Prioritize user-specified splitting preferences. 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
Generate Summary per Chapter: For each file in ./chapters/, generate a corresponding JSON summary file. Structured Fields (JSON format): { "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
Aggregate Information: Collect data from all JSON files in ./summaries/. 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.
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
2 Scripts1 Docs1 Config
- SKILL.md
- scripts/extract_chapters.py
- scripts/save_chapters.py
- _meta.json