ai-dev-governance-kit

AI development governance starter kit for engineering teams using Claude Code and similar coding assistants.

This repository provides a practical foundation for teams that want to adopt AI-assisted software development in a way that is consistent, reviewable, and scalable. It includes sample project context, reusable rules, shared commands, rollout guidance, safety boundaries, onboarding materials, and example workflow integrations.

Why this exists

AI coding tools create the most value when teams move beyond isolated individual usage and establish shared operating patterns.

This starter kit is designed to help teams:

• create shared context for AI-assisted development
• improve consistency across engineers and repos
• make review expectations more explicit
• define clear safety and workflow boundaries
• support engineers, tech leads, PMs, and QA with role-appropriate guidance
• roll out AI-assisted development in a way that can scale over time

The goal is simple: help teams turn AI-assisted development into a maintainable team capability rather than a collection of personal habits.

Who this is for

This repo is for:

• engineering teams adopting Claude Code or similar coding assistants
• tech leads creating shared standards for AI-assisted development
• platform or DevOps teams responsible for rollout and governance
• PM and QA partners who need role-appropriate workflows around AI-assisted delivery
• organizations that want a practical, adaptable starting point for team adoption

What is included

• a sample .claude/ directory structure
• a baseline CLAUDE.md for shared project context
• example rule files for code quality, git conventions, and security boundaries
• sample reusable commands for review and scaffolding workflows
• governance and rollout guidance for engineering teams
• example managed settings and CI workflow patterns
• role-based guidance for engineers, tech leads, PMs, and QA
• lightweight training and onboarding materials

Repository structure

ai-dev-governance-kit/
├── README.md
├── LICENSE
├── .gitignore
├── .claude/
│   ├── CLAUDE.md
│   ├── commands/
│   │   ├── review.md
│   │   └── scaffold.md
│   └── rules/
│       ├── code-quality.md
│       ├── git-conventions.md
│       └── security-boundaries.md
├── docs/
│   ├── governance-model.md
│   ├── repo-strategy.md
│   ├── safety-boundaries.md
│   └── adoption-metrics.md
├── examples/
│   ├── managed-settings.json
│   └── github-actions/
│       └── claude-review.yml
├── guides/
│   ├── for-engineers.md
│   ├── for-tech-leads.md
│   └── for-pm-qa.md
└── training/
    ├── workshop-outline.md
    ├── readiness-checklist.md
    └── quick-reference.md

Design principles

1. Shared context beats personal prompting habits

The goal is not to rely on every engineer being “good at prompting.” The goal is to put the important context, constraints, and standards into version-controlled team assets.

2. AI should amplify engineering judgment

Assistants can help with exploration, drafting, refactoring, review support, and repetitive tasks. They should strengthen good engineering practice, not replace review, ownership, or accountability.

3. Governance should be practical and visible

If there are boundaries around generated code, sensitive files, review expectations, testing requirements, or deployment workflows, those should be written down and easy for the team to find.

4. Teams need both shared standards and local flexibility

Some rules should apply everywhere. Others should live near the codebase or repo that actually needs them.

5. Adoption is a systems capability

Successful rollout depends on onboarding, role clarity, repo strategy, review policy, safety constraints, and change management—not just installing a CLI.

Recommended rollout path

Phase 1: Standardize

• create a baseline .claude/ structure
• define a shared CLAUDE.md
• write initial rules for quality, git workflow, and boundaries
• identify files or code paths that require stricter handling

Phase 2: Pilot

• choose one or two willing teams or repos
• test the starter structure in real work
• refine rules based on actual usage patterns and edge cases
• gather feedback from engineers and reviewers

Phase 3: Integrate

• connect the workflow to pull requests and CI where appropriate
• align AI-assisted work with code review expectations
• document team-specific conventions
• add role-specific guidance for PM and QA

Phase 4: Scale

• expand to additional repos or teams
• separate shared standards from repo-specific rules
• introduce lightweight governance ownership
• keep the system updated as the codebase and team evolve

Suggested use cases

• standardizing Claude Code usage across a software team
• bootstrapping a shared .claude/ directory for a new project
• piloting AI-assisted review workflows while keeping engineering controls intact
• documenting safe boundaries for security-sensitive or highly governed environments
• onboarding engineers, PMs, and QA into a coordinated AI-assisted workflow

What this repo is not

This repo is not:

• a promise that AI can replace engineering leadership
• a complete enterprise governance framework
• a one-size-fits-all policy set
• a substitute for code review, testing, or security practices
• a dump of prompts without process or ownership

How to use this starter kit

1. Copy the .claude/ directory into a test repo.
2. Adapt CLAUDE.md to your actual project context.
3. Review the sample rules and remove anything that does not fit.
4. Add repo-specific safety or workflow constraints.
5. Pilot with a small team before wider rollout.
6. Use the docs, guides, and training files to support adoption.

Roadmap

Planned improvements:

• additional example commands
• more role-based guidance
• richer GitHub Actions examples
• onboarding templates for larger organizations
• sample metrics for adoption and quality tracking
• optional architecture diagrams and rollout visuals

Contributing

Contributions are welcome if they improve practical team adoption of AI-assisted development. Preference goes to changes that are concrete, reusable, and grounded in real engineering workflows.

License

MIT