Understanding the Two Main Components of Book-to-Skill

Book-to-Skill consists of two core components: a deterministic Python extractor that parses documents into structured data, and a spec-driven generator that transforms that data into a reusable Agent Skill.

The virgiliojr94/book-to-skill repository implements a clean separation between data extraction and skill generation. This architecture allows users to process any book or document format and produce LLM-ready outputs without mixing parsing concerns with presentation logic.

The Deterministic Python Extractor

The first component handles all document ingestion and normalization. It is implemented as a deterministic Python pipeline that converts raw files—PDFs, EPUBs, DOCX, HTML, and others—into a canonical JSON representation.

The extraction logic resides in two locations:

• [scripts/extract.py](https://github.com/virgiliojr94/book-to-skill/blob/master/scripts/extract.py) – The CLI entry point that orchestrates the extraction workflow
• book_to_skill/ – The core package containing parsers, text sanitizers, and format handlers

This separation ensures that parsing remains reproducible and testable. The same input document always produces identical structured output, which is critical for downstream processing.

Run the extractor from the command line:

python -m book_to_skill.cli extract path/to/book.pdf --out extracted.json

The --out parameter specifies where the canonical JSON representation is saved. This file serves as the bridge to the second component.

The Spec-Driven Generator

The second component transforms extracted data into the final Agent Skill. Unlike the extractor, which operates on fixed parsing rules, the generator follows an external specification defined in [SKILL.md](https://github.com/virgiliojr94/book-to-skill/blob/master/SKILL.md).

This design provides flexibility:

• Modify output structure without touching Python code
• Version skill formats independently of the extraction engine
• Support multiple target skill formats through different spec files

Generate a skill from extracted data:

python -m book_to_skill.cli generate extracted.json --spec SKILL.md --out skill.md

The generator reads the JSON produced by the extractor, applies the rules and templates from SKILL.md, and emits a markdown file formatted for agent consumption.

How the Components Work Together

The two components form a linear pipeline:

1. Extract – Document → extracted.json (deterministic, format-agnostic)
2. Generate – extracted.json + SKILL.md → skill.md (spec-driven, customizable)

This architecture mirrors classical ETL patterns: extraction handles messy real-world inputs, while generation applies business logic through configuration rather than code.

Development teams benefit from this split:

• Engineers focus on parser accuracy and performance in book_to_skill/
• Prompt engineers or domain experts refine SKILL.md to control output quality
• Operations cache or version extracted.json to avoid re-parsing unchanged documents

Command-Line Interface Reference

Both components expose unified CLI commands under book_to_skill.cli:

Command	Purpose	Primary Files
extract	Parse document to structured JSON	scripts/extract.py, book_to_skill/*
generate	Render JSON to Agent Skill via spec	SKILL.md

Add --help to any command for parameter details:

python -m book_to_skill.cli --help
python -m book_to_skill.cli extract --help
python -m book_to_skill.cli generate --help

Summary

• Deterministic Python extractor – Parses documents into canonical JSON using the book_to_skill package and scripts/extract.py
• Spec-driven generator – Produces Agent Skills by applying SKILL.md templates to extracted data
• Clean separation – Parsing logic and output formatting remain independent, enabling parallel development and flexible deployment

Frequently Asked Questions

What file formats does the Book-to-Skill extractor support?

The extractor handles PDF, EPUB, DOCX, HTML, and other common document formats through the parsing utilities in the book_to_skill/ package. Specific format support depends on the underlying libraries integrated into the extraction pipeline.

Can I customize the output format without modifying Python code?

Yes. The spec-driven generator reads rules from [SKILL.md](https://github.com/virgiliojr94/book-to-skill/blob/master/SKILL.md), allowing you to change output structure, field mappings, and formatting by editing this specification file. No changes to book_to_skill/ or scripts/extract.py are required.

Is the extraction process deterministic and repeatable?

Yes. The Python extractor is designed to produce identical JSON output for identical inputs. This determinism enables caching, regression testing, and reproducible build pipelines when processing large document collections.

Where can I find the CLI implementation for both components?

The unified command-line interface is accessed through python -m book_to_skill.cli, which exposes extract and generate subcommands. The CLI orchestration logic resides in [scripts/extract.py](https://github.com/virgiliojr94/book-to-skill/blob/master/scripts/extract.py) and supporting modules within the book_to_skill/ package.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}