DOI: 10.5281/zenodo.17781760
title: Practitioner's Playbook for Claude Code: Configuration for Token-Efficient AI Engineering v3.0.0

description: Professional developers using Claude's Pro tier ($20-40/mo) face hard token limits. Traditional chat-based workflows burn through budgets with multi-turn conversations, often exceeding 5-10 turns for simple refactoring tasks.

v3.0.0 introduces a complete skills-based architecture transforming the playbook from a static knowledge base into an executable workflow system.

Core Innovation: Executable Workflows

Six specialized workflows that compress common refactoring tasks:- triage: Technical debt analysis with scoring algorithm (~2K tokens)- extract: Surgical function extraction with validation (~5K tokens)- modernize: Pattern updates (async/await, Result<T,E>, functional composition) (~4K tokens)- qnew: Session initialization with context loading (~2K tokens)- qplan: Pre-implementation validation against architectural patterns (~3K tokens)- qcode: Full implementation with comprehensive verification - max 15 files (~8-12K tokens)- catchup: Context restoration after /clear - critical for budget management (~1-2K tokens)

Architecture Framework:- Velocity vs. Vision Distinction: Clear separation between autocomplete and agentic reasoning- Modern TypeScript Patterns: Feature-based modules, Result monads, functional composition- Session Reset Protocol: Mandatory /clear + catchup every 5-7 prompts for optimal token efficiency- Progress Tracking: REFACTOR_PROGRESS.md template for multi-session work- Validation Gates: Type-check, unit tests, and lint must pass before proceeding

Usage: Upload the .claude/ directory to a Claude Project. The skill system auto-activates when you invoke workflows. CLAUDE.md establishes the token-efficient persona and refactoring constitution.

Target Audience: Professional software engineers with Claude Pro subscription (10-40 prompts per 5 hours), TypeScript/JavaScript codebases requiring refactoring, need for predictable token costs and budget planning, focus on systematic technical debt reduction.

Empirical Results: Tested on real-world refactoring - Decomposed 1200-line god objects into feature modules, 67% reduction in conversation turns, Maintained 100% test pass rate throughout, Zero API surface breakage, Predictable token costs per operation.

Philosophy: This playbook represents a public contribution to democratizing AI-assisted development. By sharing empirically-tested workflows and token economics, we enable developers worldwide to maximize value from Claude Pro subscriptions while maintaining code quality and architectural discipline
description: What's New in v3.0.0:

Breaking Changes:- Renamed knowledge base structure to .claude/skills/ for Claude Code compatibility- Workflows now invoked via: claude skills refactoring <workflow-name>- CLAUDE.md now acts as constitution rather than bootloader

New Features:- 6 executable workflow files with step-by-step execution protocols- Comprehensive skill documentation (SKILL.md) with learning curve guidance- Field-tested best practices section with common pitfalls- Budget tracking integration with /cost checkpoints- Emergency stop protocol for failed validations- UX test scenario generation in qcode workflow

Enhanced Patterns:- Complete modern architecture reference with code examples- TypeScript style guide with contemporary patterns- Configuration-driven logic extraction patterns- Dependency injection via factory functions- Migration strategy for monolith-to-features transition- Feature-based module organization (Manager/Endpoint/Database layers).

 
description: This is a major version release (2.0.0 → 3.0.0) due to breaking architectural changes. The shift from a static knowledge base to an executable skills-based system represents a fundamental change in how users interact with the playbook.

Field-tested on production codebases with documented 2/3 reduction in AI conversation turns and predictable token economics.

## Long-term Vision

The Claude Code Playbook aims to become the standard toolkit for token-efficient AI-assisted development across all languages and frameworks. We envision a future where:

- Developers worldwide have access to proven workflows- Token economics are transparent and predictable- Best practices are shared openly- AI assistants amplify developer productivity sustainably- Budget constraints don't limit innovation

This is a community-driven project. Your contributions, feedback,