An AI-powered reading assistant that transforms books and EPUB files into structured study guides complete with kid-friendly analogies and temporal validity checks.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install book-learning-assistant
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install book-learning-assistant using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Book Learning Assistant?
The Book Learning Assistant is an intelligent study companion designed to accelerate comprehension and retention of non-fiction literature. Operating as a featured utility within the Openclaw Skills ecosystem, this tool helps readers bypass superficial skimming by offering structured overviews and focused, chapter-by-chapter deep dives. It operates on a zero-fabrication policy, ensuring that all synthesized ideas are strictly derived from search results or directly parsed from local EPUB files.
What sets this skill apart is its dual-mode execution and cognitive approach. By translating complex conceptual frameworks into analogies that a primary school student can grasp, it makes dense material accessible. Furthermore, it incorporates an active temporal assessment, cross-referencing book theories against modern 2026 research to flag outdated concepts, offering readers an historically accurate and highly relevant learning experience.
Book Learning Assistant Use Cases
- EPUB-First Study Sessions: You have an EPUB file of a dense textbook or business book and want an interactive, chapter-by-chapter study path without manual note-taking.
- Quick Book Evaluation: You are researching a topic and want to evaluate if a book is worth buying by analyzing its core thesis, cognitive tools, and directory structure via web-search.
- Updating Legacy Knowledge: Reading an older conceptual book (e.g., in psychology or economics) and wanting to instantly know which ideas have been debunked or updated by modern research.
- Teaching and Curriculum Design: Teachers who need to translate complex technical concepts into clear, primary-school-level analogies for educational content.
How Book Learning Assistant Works
- Trigger Phase: The user triggers the skill by providing either book details (Name, Author, Edition, Publisher) for an online web search, or uploading an EPUB e-book directly.
- Direct EPUB Parsing: If an EPUB is uploaded, the agent unzips the file, reads XML metadata (
container.xml, OPF, Manifest, and NCX Spine) to resolve chapter maps, extracts the raw HTML, decodes the text (recovering UTF-8/GBK), and strips structural noise. - Web Search Enrichment: If no EPUB is present, the agent executes targeted search queries to extract authentic chapter outlines and reviews from credible academic, official, or community sources.
- Structured Stage 1 Overview: The agent generates a comprehensive book overview detailing the chapter-by-chapter framework, core thesis, and standalone cognitive tools.
- Stage 2 Focused Learning: Once the user selects a chapter, the agent deep-dives into the content, delivering original logical reasoning, key definitions, three distinct level-appropriate examples, and a critical 2026 temporal validity check.
Book Learning Assistant Setup
To get started with the Book Learning Assistant on your local environment, install the necessary Python dependencies for EPUB and document parsing:
pip install beautifulsoup4 lxml
Ensure that you have your API keys configured for your AI orchestrator to support search and file-handling workflows. Copy the skill definition into your project's skills directory to enable it.
Book Learning Assistant Data Schema & Taxonomy
The Book Learning Assistant structures book schemas and output metadata dynamically. Below is the taxonomy used for processing and structuring book metadata:
EPUB Internal Structure Mapping
| Directory / File | Type | Purpose |
|---|---|---|
META-INF/container.xml |
XML | Root entry point pointing to the OPF content description |
content.opf |
XML | Package document listing manifest items and the spine reading order |
toc.ncx / toc.xhtml |
XML/HTML | Table of contents layout mapping title tags to internal HTML segments |
text/*.html |
HTML | Document body containing styled chapter text |
Output Architecture (Markdown Notes)
Each learning session is organized into structural files or notes (ideal for Obsidian integration) formatted with the following structure:
- Overview Header: Book Metadata (Title, Author, Edition, Publisher, Source File/Search Source).
- Chapter Directory Layout: Sequential chapter and subsection map using hierarchical bullets.
- Core Concept Nodes: Detailed conceptual definitions, kid-friendly analogies, and temporal validity flags (
✅ No Obsoletionor⚠️ Outdated/Updatedwith citation strength annotations).
Book Learning Assistant Advanced Features
- Automated EPUB Parsing Engine: Low-level parsing using python's
zipfileand XML parsers skips search loops, mapping chapter nodes with absolute text accuracy directly from raw.epubfiles. - Dynamic 2026 Temporal Check: Built-in verification logic automatically reviews and flags outdated material in fast-evolving fields like finance, technology, psychology, and geopolitics.
- Structured Note Generation: Outputs are perfectly formatted with nested, folding Markdown trees (
├and└formatting) for instant importing into PKM tools like Obsidian. - Tri-Level Analogy System: Generates three customized analogies for every key point, ranging from primary school concrete representations (e.g., dividing snacks, grocery shopping) to advanced cross-disciplinary applications.
SKILL.md
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