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document-learning文档学习

Agent Skill

document-learning 用于记录任务执行中的错误、用户纠正、经验和能力缺口,适合在 OpenClaw 中希望让 Agent 持续沉淀问题、修正和最佳实践时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

1,925

周安装

81

GitHub Stars

公开资料未说明

下载量

674
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:document-learning(文档学习)
来源仓库:https://github.com/yzqzuigao-ui/document-learning
安装命令:
openclaw skills install document-learning
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install document-learning

简介

记录文档阅读进度与长期记忆集成。

  • 支持 PDF/文本文档分段学习与恢复功能。
  • 持续积累知识缺口与纠正历史。适用宿主包括 OpenClaw,接入前应确认版本、权限和运行环境要求。
  • 依赖本地存储,跨会话需手动备份记忆数据。
  • document-learning 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

name
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 documents, track your learning progress (chapter/page bookmarks), resume from where you left off in future sessions, and automatically store learned knowledge into MEMORY.md for permanent retention. Supports large file handling and chunked processing.

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

  1. Start Learning

- Upload or specify document path - System reads and extracts content - Creates initial progress entry

  1. Track Progress

- After each session, note where you stopped - Progress is saved automatically - Key points are extracted to memory files

  1. Resume Later

- Say "resume learning [document]" - System loads last position - Continues from there seamlessly

  1. Build Knowledge Base

- Over time, all learned content accumulates in MEMORY.md - Use memory_search to 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

  1. No interference: Each document has independent progress tracking
  2. Seamless switching: System automatically loads correct position for each doc
  3. Complete history: All learning sessions are preserved and searchable
  4. 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:

Best Practices

  1. Start small: Begin with one document at a time
  2. Regular updates: Add progress notes after each session, even brief ones
  3. Use summaries: Ask for chapter summaries to reinforce learning
  4. Review periodically: Use memory_search to find what you've learned before
  5. 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! 📚

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

77.93%
按下载量换算525

安全审计

VirusTotal

可疑

ClawScan

通过

Static analysis

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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