What Is the Role of SKILL.md in book-to-skill? Generator Spec Explained

SKILL.md serves as the generator specification that drives the agent-driven second half of the book-to-skill pipeline, defining workflows, operating modes, token budgets, and output file structures for converting extracted book content into fully-featured Agent Skills.

The book-to-skill repository (virgiliojr94/book-to-skill) implements a two-stage architecture: a deterministic Python extractor (scripts/extract.py → book_to_skill/) prepares clean text and metadata, then an agent-driven generator follows the instructions encoded in SKILL.md to transform that raw extraction into a complete, loadable skill. This separation keeps extraction logic stable while allowing generation behavior to evolve through a spec-driven, reviewable document.

Core Responsibilities Defined in SKILL.md

SKILL.md acts as the single source of truth for the generator. According to the source code in virgiliojr94/book-to-skill, it specifies six critical areas:

1. Workflow Definition

The document enumerates a ten-step process that the generator executes sequentially. Starting from an out-of-scope check and input validation, it proceeds through content analysis, user interaction phases, file generation, and concludes with security scanning and cleanup. Step 9.5 invokes tools/scan_generated_skill.py for advisory security checks, while Step 10 handles final cleanup.

2. Mode Selection

SKILL.md defines four operating modes with clear triggers:

• Full Conversion — complete book-to-skill transformation
• Analyze-Only — structure extraction without generation
• Generate-from-Prior-Analysis — skips extraction, uses cached analysis
• Update/Fold-in — incremental updates to existing skills

This mode system lets users re-run the pipeline at different stages without redundant processing.

3. User Interaction Protocol

Steps 0–4 establish a REPL-style probing sequence where the generator queries the user for:

• Content type classification
• Target purpose for the skill
• Desired depth level per chapter

These inputs shape how much detail each chapter summary receives, directly influencing the final skill's granularity.

4. Token Budget Control

Step 7 implements a BOOK_TYPE × DEPTH matrix that calculates per-chapter token allocations. This budget system ensures generated skills stay within cost limits while maintaining quality. The matrix scales allocation based on book complexity and user-selected depth preference.

5. File Generation Structure

SKILL.md prescribes exact output formats:

• Individual chapter files with standardized naming (ch01-intro.md, etc.)
• Glossary for domain terminology
• Patterns document for recurring techniques
• Cheatsheet for quick reference
• Master SKILL.md with front-matter, indexes, and core framework section (capped at ≤ 4,000 tokens)

6. Security and Validation

The specification mandates two quality gates before completion:

• Step 9.5: Security scan via tools/scan_generated_skill.py
• Step 10: Cleanup routine ensuring generated skills are safe to load in downstream agents

This validation chain prevents malformed or unsafe skills from entering the user's skill directory.

How SKILL.md Fits the Architecture

The repository's design, documented in docs/architecture.md, maintains strict separation between stages:

Stage	Component	Driven By	Output
Extraction	scripts/extract.py, book_to_skill/ package	Deterministic Python code	Clean text, metadata JSON
Generation	Agent runtime	SKILL.md spec	Complete Agent Skill directory

This split enables hot-swappable generation logic — updating SKILL.md immediately changes behavior without modifying the extractor's core parsing and sanitization code in book_to_skill/.

Running the Pipeline

To execute the extractor and trigger the generator that reads SKILL.md:

# Locate the extractor shim across possible host paths

SCRIPT_PATH=$(for f in \
  "$HOME/.copilot/skills/book-to-skill/scripts/extract.py" \
  "$HOME/.agents/skills/book-to-skill/scripts/extract.py" \
  "$HOME/.claude/skills/book-to-skill/scripts/extract.py" \
  ".github/skills/book-to-skill/scripts/extract.py"; do
    [ -f "$f" ] && echo "$f" && break
done)

# Run extraction; generator follows SKILL.md internally

python3 "$SCRIPT_PATH" path/to/book.pdf

Generated Skill Structure

After successful execution, the output directory follows the layout defined in SKILL.md:

$SKILLS_HOME/my-skill/
├── SKILL.md          # Core frameworks & indexes (≤ 4k tokens)

├── chapters/
│   ├── ch01-intro.md
│   └── ...
├── glossary.md
├── patterns.md
└── cheatsheet.md

Key Source Files

• SKILL.md — Generator specification source
• docs/architecture.md — Extractor/generator split diagram source
• scripts/extract.py — Extraction shim source
• tools/scan_generated_skill.py — Security scanner invoked at Step 9.5 source
• book_to_skill/ — Core extractor package source

Summary

• SKILL.md is the generator spec, not the extractor — it governs how agent-driven generation behaves after deterministic extraction completes.
• It defines ten sequential steps from validation through security scanning, with four selectable operating modes for different use cases.
• Token budgeting via the BOOK_TYPE × DEPTH matrix controls costs while preserving quality.
• The specification mandates specific output structures: chapter files, glossary, patterns, cheatsheet, and a master SKILL.md capped at 4,000 tokens.
• Security validation through tools/scan_generated_skill.py is hardcoded at Step 9.5.
• This architecture enables spec-driven evolution — modify SKILL.md to change generation without touching book_to_skill/ extraction code.

Frequently Asked Questions

What happens if I modify SKILL.md?

Changes take effect immediately on the next generator run. The extractor phase remains unaffected since it operates from hardcoded Python logic in book_to_skill/. This design lets you experiment with generation strategies, adjust token budgets, or add new operating modes without risking extraction stability.

Can I run the generator without the extraction step?

Yes. SKILL.md defines the Generate-from-Prior-Analysis mode specifically for this workflow. If you've cached analysis output from a previous run, the generator skips the extractor entirely and proceeds directly to skill construction using stored metadata.

How does book-to-skill prevent overspending on API tokens?

Step 7's BOOK_TYPE × DEPTH matrix calculates per-chapter budgets before any generation begins. The generator requests user confirmation at Step 2.5 (Pre-flight cost estimate) when projections exceed thresholds, and the hard ceiling on master SKILL.md length (≤ 4,000 tokens) caps final output size regardless of input complexity.

Where does the final generated skill live?

Output location depends on your configured SKILLS_HOME environment variable. The generator creates a subdirectory matching your skill name, populating it with the structured file layout defined in SKILL.md Steps 6–9. Standard paths include $HOME/.copilot/skills/, $HOME/.agents/skills/, or $HOME/.claude/skills/ depending on your agent runtime.

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