← Back to Skills Marketplace
Document Learning
by
yzqzuigao-ui
· GitHub ↗
· v1.0.0
· MIT-0
69
Downloads
0
Stars
0
Active Installs
1
Versions
Install in OpenClaw
/install document-learning
Description
Comprehensive document learning system with progress tracking, resume capability, and long-term memory integration. Use when you need to read PDF/text docume...
README (SKILL.md)
Document Learning System
A complete system for reading documents, tracking progress across multiple sessions, and building long-term memory.
Quick Start
To start learning a document:
Please learn this document: [filename]
To resume from where you left off:
Resume learning [filename] from last position
To check current progress:
What's my progress on learning [filename]?
Core Features
1. Large File Support
- Handles PDF files up to hundreds of MBs
- Text-based documents (TXT, MD, LOG, etc.)
- Chunked processing to avoid memory issues
- Automatic encoding detection and recovery
2. Progress Tracking & Resume
- Bookmark system: Automatically tracks your position (chapter/page)
- Resume capability: Pick up exactly where you left off, even days later
- Progress visualization: See what's been learned vs remaining
- Manual control: Jump to specific chapters/pages if needed
3. Long-term Memory Integration
- Extracts key concepts automatically
- Stores learning summaries in
MEMORY.md - Creates session notes in
memory/YYYY-MM-DD.md - Builds a searchable knowledge base over time
How It Works
Session Flow
-
Start Learning
- Upload or specify document path
- System reads and extracts content
- Creates initial progress entry
-
Track Progress
- After each session, note where you stopped
- Progress is saved automatically
- Key points are extracted to memory files
-
Resume Later
- Say "resume learning [document]"
- System loads last position
- Continues from there seamlessly
-
Build Knowledge Base
- Over time, all learned content accumulates in MEMORY.md
- Use
memory_searchto find specific topics later - Create comprehensive summaries periodically
Multi-Document Learning Support 📚🔄
You can now learn multiple PDFs simultaneously and switch between them!
How It Works
The system maintains a progress store that tracks ALL your learning documents:
- ✅ Track progress for Document A, B, C... all at once
- ✅ Learn Document A → Pause → Learn Document B → Pause → Resume Document A
- ✅ Each document remembers its exact position (chapter/page)
- ✅ Switch context seamlessly between different documents
Multi-Document Workflow Example
Day 1: Start learning PDF-A
User: Please learn this driver manual for me.
Assistant: Started tracking itop-3568-driver.pdf
Progress: Page 0/450 (not started)
[Read some content]
User: Pause here, let's learn another document
Assistant: ✅ Saved progress for PDF-A at page 120/450
Now learning PDF-B: user-guide.pdf
[Start PDF-B]
User: Please also learn this user guide
Assistant: Started tracking user-guide.pdf
Progress: Page 0/80 (not started)
[Read some of PDF-B]
User: Pause, back to the driver manual
Assistant: ✅ Saved progress for PDF-B at page 35/80
Switching context back to PDF-A...
Resume from page 120/450
Day 2: Continue learning different documents
User: Let's continue with the user guide today
Assistant: Loading progress for user-guide.pdf...
Last read: Page 35/80 (PDF-B)
Continuing from where we left off...
[After reading PDF-B]
User: Show me status of all my learning documents
Assistant: 📚 Document Learning Status
==================================
✅ itop-3568-driver.pdf
Progress: 120/450 pages (27%)
Last updated: Today
🔄 user-guide.pdf
Progress: 50/80 pages (63%)
Last updated: Just now
Day 3: Query specific document knowledge
User: What did I learn about the driver installation in PDF-A?
Assistant: [Searches memory and provides answer from PDF-A]
Based on my notes from itop-3568-driver.pdf...
Commands for Multi-Document Learning
Start a new document
Please learn this document: /path/to/document.pdf
Pause current, start another
Pause learning [current-doc], let's learn [new-doc]
Switch back to previous document
Resume learning [doc-name] from last position
Check all documents status
What's my progress on all my learning documents?
Show me the status of everything I'm learning
Key Benefits
- No interference: Each document has independent progress tracking
- Seamless switching: System automatically loads correct position for each doc
- Complete history: All learning sessions are preserved and searchable
- Flexible workflow: Learn at your own pace across multiple sources
Usage Patterns
Pattern 1: Learning a Technical Manual (e.g., PDF)
User: Here's a driver manual. Please learn it for me.
Assistant: I'll read the document, extract key points, and save progress. Let me start with Chapter 1...
[After reading first chapter]
Assistant: Progress update: Read chapters 1-2 of 15. Saved to memory. Ready to continue when you are!
[Days later - resume]
User: Resume learning the driver manual
Assistant: Loading last position (chapter 3)... Continuing from where we left off...
Pattern 2: Learning a Book/Text Document
User: I want to learn this book. Track my progress chapter by chapter.
Assistant: Got it! I'll read each chapter and save the summary after every session. Progress tracking enabled.
[After Chapter 1]
Assistant: ✅ Chapter 1 complete - Summary saved to memory/YYYY-MM-DD.md
📊 Progress: 1/24 chapters (4%)
[Next day]
User: Continue from last position
Assistant: Resuming at Chapter 2...
Pattern 3: Reference Learning (Look-up as needed)
User: I need to learn this specification document for reference.
Assistant: I'll read it and create a searchable knowledge base. You can later ask questions like "What does section 5 say about API limits?"
Later: What are the API rate limits mentioned in the spec?
Assistant: [Searches memory and provides answer from learned content]
Commands & Tools
Built-in Script
The skill includes scripts/read_large_document.py which handles:
- PDF reading with pdfplumber
- Text file encoding detection
- Progress saving/loading
- Key point extraction
Memory Integration Files
Read these for detailed integration patterns:
references/memory_integration.md- How to integrate with MEMORY.md
Best Practices
- Start small: Begin with one document at a time
- Regular updates: Add progress notes after each session, even brief ones
- Use summaries: Ask for chapter summaries to reinforce learning
- Review periodically: Use
memory_searchto find what you've learned before - Be specific: When asking about content, mention the document name
Limitations & Notes
- PDFs must be text-based (not scanned images) for optimal reading
- Very large PDFs (>200MB) may take longer to process
- Progress tracking works best with structured documents (chapters/sections)
- For scanned PDFs, consider OCR tools first
Examples
Example 1: Learning a Driver Manual
User: Please learn this driver manual for me. It's in /workspace/linuxdriver/itop-3568-driver-v2.8.pdf
Assistant: I'll start reading the document now...
[Reads and extracts]
✅ Document loaded: itop-3568-driver-v2.8.pdf (120MB)
📑 Total pages: 450
🔑 Key concepts extracted: Hardware specs, Pinout details, Driver installation...
Progress saved! You can resume anytime with "Resume learning the driver manual"
Example 2: Resume After Days
User: Resume learning the driver manual from last position
Assistant: Loading progress... Last read: page 156 of 450 (Chapter 7)
Continuing from where we left off...
[Reads next section]
✅ Chapter 7 complete - Progress updated to page 234/450
Example 3: Query Learned Content
User: What did I learn about the pinout configuration?
Assistant: [Searches memory and provides answer from driver manual learning session]
Based on my notes from the itop-3568 driver manual (learned on April 6):
- GPIO pins are numbered X, Y, Z...
- Power supply requirements: 5V at 2A...
Ready to start learning? Just tell me which document you want to learn! 📚
Usage Guidance
This skill appears to do what it says: reading documents, chunked extraction, and saving progress and summaries to local memory files. Before using: (1) only supply document paths you trust (the scripts will read any path you give them); (2) expect the skill to create/update .multi_doc_learning_progress.json in the current working directory and .document_learning_progress.json alongside documents and to write memory/YYYY-MM-DD.md or MEMORY.md as described; (3) there are no network calls or credential requests, but pdfplumber is needed to read PDFs (install it in a safe environment if required); (4) if you are concerned, review the two included Python scripts or run them in a sandboxed workspace to confirm behavior.
Capability Analysis
Type: OpenClaw Skill
Name: document-learning
Version: 1.0.0
The skill bundle provides document reading and progress tracking capabilities through Python scripts (read_large_document.py and multi_doc_progress.py) that interact with the filesystem. While the functionality aligns with the stated purpose, the scripts exhibit high-risk behaviors by performing unsanitized file read/write operations based on agent-provided paths, which could be exploited via prompt injection to access sensitive system files (path traversal). Additionally, the scripts create hidden metadata files (.multi_doc_learning_progress.json) in the current directory and potentially in arbitrary document directories, and the SKILL.md instructions direct the agent to automatically modify workspace memory files (MEMORY.md), representing a significant side-effect surface.
Capability Assessment
Purpose & Capability
Name/description match the included scripts and docs: both Python scripts implement reading large PDFs/text, chunking, extracting key points, and tracking progress across multiple documents. There are no unexpected credential or network requirements.
Instruction Scope
SKILL.md and references instruct the agent to read documents (paths provided by the user), save progress, and integrate notes into MEMORY.md / memory/YYYY-MM-DD.md. This is expected, but the skill will read any file path you give it and will create/update local progress and memory files in the workspace.
Install Mechanism
No install spec is present (instruction-only with bundled scripts). The scripts optionally use pdfplumber (standard Python package) if available; nothing is downloaded or executed from remote URLs.
Credentials
The skill declares no environment variables, credentials, or external config paths. The file I/O it performs (reading documents, writing .multi_doc_learning_progress.json and per-document .document_learning_progress.json, and writing memory files) is proportional to its stated purpose.
Persistence & Privilege
The skill is not always-enabled and does not request elevated platform privileges, but it will create/update local progress files ('.multi_doc_learning_progress.json' in cwd and '.document_learning_progress.json' in document directories) and may add entries to MEMORY.md / memory/ files per its documentation. That is normal for this functionality but worth knowing.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install document-learning - After installation, invoke the skill by name or use
/document-learning - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial release of the comprehensive document learning system with multi-document support.
- Supports reading and learning from large PDF and text documents with automatic encoding detection.
- Tracks progress (chapter/page) for each document, allowing you to pause, resume, and switch between multiple documents seamlessly.
- Extracts and saves key concepts and learning summaries into a searchable long-term memory (MEMORY.md).
- Provides commands for checking progress, resuming from the last position, and reviewing all active learning documents.
- Handles large files via chunked processing to ensure stable performance.
Metadata
Frequently Asked Questions
What is Document Learning?
Comprehensive document learning system with progress tracking, resume capability, and long-term memory integration. Use when you need to read PDF/text docume... It is an AI Agent Skill for Claude Code / OpenClaw, with 69 downloads so far.
How do I install Document Learning?
Run "/install document-learning" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Document Learning free?
Yes, Document Learning is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Document Learning support?
Document Learning is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Document Learning?
It is built and maintained by yzqzuigao-ui (@yzqzuigao-ui); the current version is v1.0.0.
More Skills