agency-example-workflow-book-chapter

name	agency-example-workflow-book-chapter
description	The Agency examples specialist
metadata	{"origin":"TheAgency","division":"examples"}

Workflow Example: Book Chapter Development

A focused single-agent workflow for turning rough source material into a strategic first-person chapter draft with explicit revision loops.

When to Use This

Use this workflow when an author has voice notes, fragments, or strategic notes, but not yet a clean chapter draft. The goal is not generic ghostwriting. The goal is to produce a chapter that strengthens category positioning, preserves the author's voice, and exposes open editorial decisions clearly.

Agent Used

Agent	Role
Book Co-Author	Converts source material into a versioned chapter draft with editorial notes and next-step questions

Example Activation

Activate Book Co-Author.

Book goal: Build authority around practical AI adoption for Mittelstand companies.
Target audience: Owners and operational leaders of 20-200 person businesses.
Chapter topic: Why most AI projects fail before implementation starts.
Desired draft maturity: First substantial draft.

Raw material:
- Voice memo: "The real failure happens in expectation setting, not tooling."
- Notes: Leaders buy software before defining the operational bottleneck.
- Story fragment: We nearly rolled out the wrong automation in a cabinetmaking workflow because the actual problem was quoting delays, not production throughput.
- Positioning angle: Practical realism over hype.

Produce:
1. Chapter objective and strategic role in the book
2. Any clarification questions you need
3. Chapter 2 - Version 1 - ready for review
4. Editorial notes on assumptions and proof gaps
5. Specific next-step revision requests

Expected Output Shape

The Book Co-Author should respond in five parts:

1. Target Outcome
2. Chapter Draft
3. Editorial Notes
4. Feedback Loop
5. Next Step

Quality Bar

• The draft stays in first-person voice
• The chapter has one clear promise and internal logic
• Claims are tied to source material or flagged as assumptions
• Generic motivational language is removed
• The output ends with explicit revision questions, not a vague handoff