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Claude Code in Production: 65 Automation Patterns for Hooks, Sub-Agents & CI/CD
Stop configuring Claude Code by hand. Build systems that enforce quality, cut costs, and ship code automatically.
Most teams use Claude Code like a search one question at a time, no memory between sessions. Claude Code in Production shows what's possible when you treat it as an automation platform instead of a chatbot.
This book delivers 65 production-tested patterns across every layer of the Claude Code stack — each with a real problem, working code, actual output, and clear trade-offs.
▪ Part 1 — Design Foundations (Patterns 01–10)
Your CLAUDE.md file is the configuration layer everything else depends on. This part covers how to structure rules that survive team growth, how to embed dynamic runtime context (git branch, open issues, environment variables) directly into Skills, and how to build a modular rules system that different team members can extend without breaking each other's workflows.
▪ Part 2 — The Complete 17-Event Catalog (Patterns 11–22)
Hooks are the most powerful and least understood feature in Claude Code. This part documents every available event — PreToolUse, PostToolUse, Stop, SubagentStop, and more — with a working pattern for each. Patterns cover automated quality gates, commit hygiene enforcement, terminology checking, and state persistence across sessions.
▪ Part 3 — Design and Implementation (Patterns 23–31)
Multi-agent pipelines unlock true one agent reviews code while another runs tests while a third checks documentation. This part covers when to delegate versus when to keep work in the main agent, how to pass context without hitting token limits, how to prevent agent loops, and three complete pipeline architectures you can adapt for your own projects.
▪ Part 4 — CI/CD Integration (Patterns 32–39)
Running Claude Code in headless mode inside GitHub Actions and GitLab CI enables automated PR reviewers, test-generation agents that run on every push, and documentation agents that keep your README in sync with your code. Covers secrets management, timeout handling, and cost controls.
▪ Part 5 — Permissions and Security (Patterns 40–47)
Production deployments require hard limits on what Claude Code can touch. This part covers allowedTools and deniedTools configuration, read-only agent modes for untrusted codebases, prompt injection prevention for agents that process external content, and audit trails for AI-assisted operations in regulated environments.
▪ Part 6 — Cost Optimization (Patterns 48–56)
Token costs compound quickly at team scale. The patterns here show how to cut consumption by 40–70% through prompt caching, intelligent model routing (Haiku for routine tasks, Opus for complex analysis), context compression, and output format optimization. Each pattern includes before/after token counts from real workflows.
▪ Part 7 — Production Workflows (Patterns 57–65)
Original frameworks developed for this the 7-Stage Development Workflow for reproducible sessions, the Hallucination Detection Protocol for validating AI-generated content, the Confidence Check System that surfaces uncertainty before it becomes a bug, and the Autonomous Merge Pipeline that takes a PR from review to merge with safety gates at every step.
Most teams use Claude Code like a search one question at a time, no memory between sessions. Claude Code in Production shows what's possible when you treat it as an automation platform instead of a chatbot.
This book delivers 65 production-tested patterns across every layer of the Claude Code stack — each with a real problem, working code, actual output, and clear trade-offs.
▪ Part 1 — Design Foundations (Patterns 01–10)
Your CLAUDE.md file is the configuration layer everything else depends on. This part covers how to structure rules that survive team growth, how to embed dynamic runtime context (git branch, open issues, environment variables) directly into Skills, and how to build a modular rules system that different team members can extend without breaking each other's workflows.
▪ Part 2 — The Complete 17-Event Catalog (Patterns 11–22)
Hooks are the most powerful and least understood feature in Claude Code. This part documents every available event — PreToolUse, PostToolUse, Stop, SubagentStop, and more — with a working pattern for each. Patterns cover automated quality gates, commit hygiene enforcement, terminology checking, and state persistence across sessions.
▪ Part 3 — Design and Implementation (Patterns 23–31)
Multi-agent pipelines unlock true one agent reviews code while another runs tests while a third checks documentation. This part covers when to delegate versus when to keep work in the main agent, how to pass context without hitting token limits, how to prevent agent loops, and three complete pipeline architectures you can adapt for your own projects.
▪ Part 4 — CI/CD Integration (Patterns 32–39)
Running Claude Code in headless mode inside GitHub Actions and GitLab CI enables automated PR reviewers, test-generation agents that run on every push, and documentation agents that keep your README in sync with your code. Covers secrets management, timeout handling, and cost controls.
▪ Part 5 — Permissions and Security (Patterns 40–47)
Production deployments require hard limits on what Claude Code can touch. This part covers allowedTools and deniedTools configuration, read-only agent modes for untrusted codebases, prompt injection prevention for agents that process external content, and audit trails for AI-assisted operations in regulated environments.
▪ Part 6 — Cost Optimization (Patterns 48–56)
Token costs compound quickly at team scale. The patterns here show how to cut consumption by 40–70% through prompt caching, intelligent model routing (Haiku for routine tasks, Opus for complex analysis), context compression, and output format optimization. Each pattern includes before/after token counts from real workflows.
▪ Part 7 — Production Workflows (Patterns 57–65)
Original frameworks developed for this the 7-Stage Development Workflow for reproducible sessions, the Hallucination Detection Protocol for validating AI-generated content, the Confidence Check System that surfaces uncertainty before it becomes a bug, and the Autonomous Merge Pipeline that takes a PR from review to merge with safety gates at every step.
278 pages, Kindle Edition
Published February 27, 2026
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