How book-to-skill Processes SKILL.md Instructions: A Step-by-Step Guide

book-to-skill handles SKILL.md instructions by treating the document as an executable recipe that guides a host agent through 11 sequential steps—from input validation through security scanning to optional GitHub publication.

The book-to-skill project (virgiliojr94/book-to-skill) transforms books into structured AI skills using a unique two-stage architecture. The SKILL.md file sits at the heart of this system, defining exactly how the generator component interprets extracted text and produces the final skill artifacts.

The Two-Stage Architecture

Understanding how SKILL.md instructions are handled requires grasping the project's split design:

Component	Language	Responsibility
Extractor	Python (book_to_skill/cli.py)	Deterministic text extraction from PDF, EPUB, DOCX; produces full_text.txt and metadata.json
Generator	Host-agent driven	Interprets SKILL.md steps to create SKILL.md, chapters/, glossary.md, patterns.md, and cheatsheet.md

As documented in docs/architecture.md, this separation allows the Python side to remain simple and deterministic while the SKILL.md specification evolves independently for different host agents (Copilot CLI, Amp, Claude, Hermes, etc.).

How SKILL.md Instructions Drive the Generator

The SKILL.md file in the repository root contains an ordered workflow that the host agent executes. Here is how each instruction set is processed:

Step 0 — Out-of-Scope Check

The generator first verifies that arguments were supplied. If invoked without parameters, it halts immediately with a usage prompt rather than proceeding with undefined input.

Step 1 — Input Validation

The specification requires at least one supported file, folder, or glob pattern. This check runs before any extraction begins, preventing wasted compute on invalid paths.

Step 1.5 — Content Type Identification

The user is prompted to classify the source as technical or text-heavy. This BOOK_TYPE value directly influences later token budgets, with technical books receiving different allocation strategies than narrative works.

Step 2 — Extraction Orchestration

The generator locates scripts/extract.py, validates the Python environment, and invokes the extractor with the appropriate mode flag. The extraction output lands in a temporary work directory containing the two core artifacts: full_text.txt (sanitized content) and metadata.json (structural metadata).

Step 2.5 — Cost Estimation

Before any generation occurs, the tool reads metadata.json and presents the user with:

• Estimated token cost for the full conversion
• Complete file list from the extraction
• Opportunity to abort before incurring charges

Step 3 — Structure Analysis

The generator examines the first ~8,000 characters of full_text.txt to identify:

• Title and author
• Chapter heading patterns
• Core themes and frameworks

This analysis-only phase can be triggered independently with:

book-to-skill analyze path/to/book.epub

Step 4 — Purpose Selection (Full Conversion Only)

For complete skill generation, the user selects the intended use case:

• Apply frameworks
• Think with mental models
• Reference chapters
• All of the above

This choice sets the DEPTH flag (reference vs study), which combines with BOOK_TYPE to determine per-chapter token budgets.

Step 5 — Skill Name Resolution

The generator either accepts a slug from the command line or proposes names to the user (author-concept or title-based formats). It then selects the appropriate SKILLS_HOME root directory based on the detected host agent. This host-neutral approach—deliberately omitting allowed-tools from the spec—lets each platform supply its own shell and file-access capabilities.

Step 6 — Directory Scaffolding

The skill folder structure is created, including the chapters/ subdirectory that will hold individual chapter summaries.

Step 7 — Chapter-Wise Generation

For each detected chapter, the generator:

• Reads only the relevant slice of full_text.txt (using grep/sed for memory-efficient handling of large books)
• Respects the token budget derived from BOOK_TYPE × DEPTH
• Writes a markdown summary to chapters/{slug}.md

Step 8 — Supporting Files Generation

Three additional artifacts are built from extracted terms and patterns:

• glossary.md — defined terms and concepts
• patterns.md — recurring techniques and structures
• cheatsheet.md — quick-reference decision rules

Each file has size caps to maintain the skill's lightweight profile.

Step 9 — Master SKILL.md Assembly

The core skill file is constructed with specific sections:

• Concise description
• How to Use section
• Core Frameworks index (~2,000 tokens)
• Chapter index
• Topic index

The file is deliberately capped at ~4,000 tokens; excess content is truncated from the end to preserve front-loaded critical information.

Step 9.5 — Security Scan

Before displaying or publishing, the generator invokes tools/scan_generated_skill.py to check for:

• Hidden Unicode characters
• Model-control tags
• Other potentially unsafe artifacts

This advisory scan protects both the user and downstream consumers of the skill.

Step 10 — Cleanup and Reporting

The temporary work directory is removed, and a success report lists all generated artifacts with final token estimates.

Step 11 — Optional Publishing

If enabled and visibility checks pass, the skill folder is initialized as a Git repository and pushed to GitHub, making it installable via:

npx skills add <user>/<repo>

Update and Fold-In Workflow

When the target skill folder already exists (detected by presence of SKILL.md), the generator branches at Step 5 into an update pathway. It:

• Reads existing skill files
• Merges new chapters without duplicating existing ones
• Updates supporting files with new terms and patterns
• Regenerates the master SKILL.md while preserving previous content

Trigger this with:

book-to-skill path/to/new/chapter.pdf existing-skill-slug

Design Principles Enforced by SKILL.md

The specification embeds four key constraints that shape every skill:

Principle	Implementation in SKILL.md
Extract structure, not summaries	Only named frameworks, decision rules, and anti-patterns are retained; narrative summaries are discarded
On-demand chapters	SKILL.md stays small; full chapter content loads only when explicitly queried
Front-loaded core content	Most important frameworks appear first; automatic truncation removes from the end
Host-neutral tooling	No allowed-tools restrictions; each host supplies required capabilities

Complete Workflow Example

Running a full conversion executes steps 0 through 10:

book-to-skill path/to/book.pdf my-skill-slug

This single command triggers extraction, cost estimation, structure analysis, chapter generation, supporting file creation, security scanning, and cleanup—entirely as specified in the SKILL.md instruction set.

Key Implementation Files

File	Role in SKILL.md Processing
SKILL.md	Master specification defining steps 0–11
scripts/extract.py	Shim launching the deterministic extractor
book_to_skill/cli.py	Python extraction pipeline entry point
tools/scan_generated_skill.py	Security scanner invoked at Step 9.5
tools/validate_skill.py	CI validator for host-specific compliance
docs/architecture.md	Pipeline overview and host root selection

Summary

• SKILL.md is executable documentation — the host agent follows its numbered steps as a runtime script
• Token budgets are dynamic — calculated from BOOK_TYPE × DEPTH to balance completeness against cost
• Security is built-in — mandatory scanning at Step 9.5 checks for hidden threats before publication
• Updates are first-class — the same specification handles both greenfield skills and incremental additions
• Host neutrality enables portability — the spec avoids tool-lock-in, letting Copilot, Claude, Amp, and Hermes each provide appropriate capabilities

Frequently Asked Questions

What happens if I run book-to-skill without arguments?

The generator stops at Step 0 with a usage prompt. The SKILL.md specification explicitly defines this as an out-of-scope check to prevent execution with undefined input.

Can I preview what a book contains before generating a full skill?

Yes. The analyze subcommand runs through Step 3 only, returning an extraction report that lists detected frameworks, principles, techniques, and suggested skill names without incurring generation costs.

How does book-to-skill prevent generated skills from becoming too large?

Three mechanisms enforce size constraints: dynamic token budgets based on content type and depth, per-file caps on supporting files, and truncation from the end of SKILL.md at ~4,000 tokens. The specification prioritizes front-loaded content so truncation removes less critical material.

Is it safe to publish generated skills to public repositories?

The Step 9.5 security scan (tools/scan_generated_skill.py) checks for hidden Unicode, model-control tags, and other unsafe artifacts before any display or publication. However, users should still review generated content, as the scan is advisory rather than exhaustive.

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