What Is the Role of the Spec-Driven Generator in book-to-skill?

The spec-driven generator is the orchestration layer that reads the SKILL.md specification and transforms raw book extractions into structured, on-demand Agent Skills through a series of user-guided, agent-driven steps.

The book-to-skill pipeline splits work between two distinct halves: a deterministic Python extractor that handles document parsing, and the spec-driven generator that handles intelligent transformation. This article examines how the generator functions, what steps it executes, and why this separation of concerns matters for building reliable Agent Skills.

How the Spec-Driven Generator Fits Into the Pipeline

According to the virgiliojr94/book-to-skill source code, the generator serves as the second half of the conversion workflow. After scripts/extract.py produces clean text (full_text.txt) and metadata (metadata.json), the generator takes over by reading the specification file (SKILL.md) and executing its numbered steps.

The docs/architecture.md file describes this two-half design explicitly: the extractor stays pure-Python for reliability, while the generator stays language-agnostic and driven entirely by the markdown spec. Any host agent can interpret the same SKILL.md, making the system portable across different AI platforms.

Step-by-Step Execution Flow

The generator follows a fixed sequence defined in SKILL.md, with flexibility injected through user choices at key decision points.

Step 0–1.5: Content Type Identification

The generator first asks the user to classify the book as technical or text-heavy, storing this as BOOK_TYPE. This variable influences later token budget calculations. This step appears in the Identify content type section of SKILL.md【2†L104-L118】.

Step 2–2.5: Text Extraction Launch

Using BOOK_TYPE, the generator launches scripts/extract.py to process the source document. It then consumes the resulting full_text.txt and metadata.json from the extraction output. See the Extract text from the source documents section【2†L27-L33】.

Step 3–4: Structure Analysis and Purpose Definition

The generator analyzes the extracted content to discover:

• Title and author
• Chapter structure
• Core themes

It then prompts the user for DEPTH—what the skill should do. This answer determines how much detail each chapter summary receives. These steps appear in the Analyze book structure and Ask purpose sections【2†L44-L53】【2†L79-L95】.

Step 6–9: Skill Artifact Generation

The generator creates the complete file hierarchy:

• chapters/ directory with individual summaries
• glossary.md for key terms
• patterns.md for recurring concepts
• cheatsheet.md for quick reference
• Final composed SKILL.md for host agent loading

All output formats and directory structures are dictated by the spec in the Create skill directory structure section onward【2†L33-L41】【2†L56-L64】.

Step 9.5: Security Scanning

Before handoff, the generator runs tools/scan_generated_skill.py to detect hidden instructions or invisible Unicode that may have leaked into generated files. This validation step appears in the Scan the generated skill section【2†L28-L33】.

Step 10–11: Cleanup and Optional Publishing

The generator removes temporary work directories, reports completion, and optionally publishes to GitHub. See Cleanup and report and Publish sections【2†L41-L49】【2†L14-L22】.

Running the Spec-Driven Generator

Basic Conversion

Trigger the full pipeline with a single command:

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

This automatically runs scripts/extract.py followed by all generator steps in SKILL.md, outputting to the host-specific directory (e.g., ~/.copilot/skills/my-awesome-skill/).

Validating an Existing Skill

Run a single generator step in isolation:

python -m tools.validate_skill.py SKILL.md

This executes Step 9.5 from the spec to check a previously generated skill for security concerns.

Updating Existing Skills

The generator supports incremental updates through the Update / Fold-in Workflow:

book-to-skill new/chapter1.pdf existing-skill-slug --mode text

The generator reads the existing skill's SKILL.md, merges new chapter summaries and glossary entries, and updates the master specification.

Key Implementation Files

File	Role
SKILL.md	The specification that drives all generator behavior—step definitions, token budgets, output formats.
docs/architecture.md	Documents the two-half architecture and the generator's orchestration responsibilities.
scripts/extract.py	Thin shim launching the deterministic extractor; invoked by the generator.
tools/validate_skill.py	Implements Step 9.5 security validation.
tools/scan_generated_skill.py	Advisory scanner run before skill finalization.

Why Spec-Driven Design Matters

The spec-driven generator architecture provides three critical advantages:

• Determinism — The same SKILL.md produces consistent outputs across runs
• Portability — Any host agent that parses markdown can execute the workflow
• Maintainability — Business logic lives in the spec, not hardcoded in the generator implementation

This design keeps the conversion reliable while preserving flexibility through user-configurable variables (BOOK_TYPE, DEPTH, skill name, destination host).

Summary

• The spec-driven generator is the second half of book-to-skill, transforming extracted text into structured Agent Skills
• It reads and executes the numbered steps defined in SKILL.md with user input at key decision points
• The generator manages content type detection, token budgeting, file hierarchy creation, security scanning, and optional publishing
• Separation from the deterministic Python extractor keeps the system both reliable and language-agnostic
• Key files include SKILL.md (specification), scripts/extract.py (extraction shim), and tools/scan_generated_skill.py (security validation)

Frequently Asked Questions

What makes the generator "spec-driven"?

The generator executes a fixed workflow defined entirely in SKILL.md, a markdown file containing numbered steps, token budget rules, and output format specifications. This externalized configuration means the same generator implementation can produce different skill structures just by modifying the spec, without code changes.

How does the spec-driven generator handle different book types?

The generator asks users to classify content as technical or text-heavy during Step 0–1.5, storing this as BOOK_TYPE. This variable influences later decisions about extraction parameters, summary depth, and token allocation—allowing the same spec to adapt to different content categories.

Can I run the spec-driven generator without the extractor?

No—the generator depends on scripts/extract.py output (full_text.txt and metadata.json) as its starting input. However, you can re-run the generator on existing extractions or invoke individual validation steps like tools/validate_skill.py without triggering full re-extraction.

Where is the spec-driven generator's security scanning implemented?

Security scanning appears in Step 9.5 of SKILL.md, implemented by tools/scan_generated_skill.py. This step runs automatically before skill finalization to detect hidden instructions, invisible Unicode, or other potentially harmful content that may have propagated from source documents into generated skill files.

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