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VDD Framework

Vision-Driven Development — A framework for AI-autonomous software development with enforced safety boundaries.

"Don't just document rules — enforce them technically."

日本語版はこちら

What is VDD?

VDD Framework provides a complete methodology and tooling for AI-autonomous development, built around two core ideas:

  1. Vision-Driven Development (VDD): Keep your project aligned with its vision through structured decision-making, daily scoring, and debate-driven validation
  2. Release-Driven Development (RDD): Ship value in small, safe, rollback-friendly releases where AI handles implementation, testing, and review autonomously

The framework enforces safety through git worktree isolation, hook-based guardrails, and a clear authority matrix between humans and AI.

Human decides WHAT and WHY → AI decides HOW and executes
     (Vision + QA)              (Implementation + Review)

Design Philosophy: Autonomy Through Boundaries

High autonomy and well-defined boundaries are two sides of the same coin.

The conventional approach to AI safety is a whitelist — telling the AI exactly what it can do, step by step. This framework takes the opposite approach: define what is forbidden, and everything else is permitted. By explicitly specifying the small set of actions that must never happen (force-push to main, editing files outside a worktree, merging without review), the AI gains maximum freedom to make its own decisions within those boundaries.

This works because the boundaries are technically enforced, not just documented. When an AI agent knows that a hook will physically block a dangerous operation, it doesn't need to second-guess every action — it can act decisively, knowing the guardrails will catch genuine mistakes. The branch strategy reinforces this: release/* branches in isolated worktrees give the AI a sandbox where it can work with full autonomy, while main and develop remain protected by structural rules.

The result: the clearer the boundaries, the more autonomous the agent becomes. Not despite the constraints, but because of them.

Architecture

graph TB
    subgraph VDD ["VDD (Strategy)"]
        V[VISION.md] --> D[DECISIONS.md]
        D --> DS[DAILY_SCORE.md]
    end

    subgraph RDD ["RDD (Execution)"]
        P0[Phase 0: Requirements] --> P1[Phase 1: Design Dialogue]
        P1 --> P2[Phase 2: Autonomous Implementation]
        P2 --> P3[Phase 3: Self-Review + Independent Review]
        P3 --> P4[Phase 4: Merge + QA]
    end

    subgraph Safety ["Safety Layer"]
        H1[worktree-guard L5]
        H2[commit-guard L5]
        H3[review-enforcement L4]
        H4[subagent-rules L3]
    end

    VDD -->|"Strategic Alignment"| RDD
    Safety -->|"Enforce Boundaries"| RDD
Loading

Quick Start

# 1. Clone the framework
git clone https://github.com/shuhei0866/vdd-framework.git

# 2. Navigate to your project
cd your-project

# 3. Run the setup wizard
bash path/to/vdd-framework/scripts/init.sh

The wizard will:

  • Detect your project type (Node.js, Python, Rust, Go)
  • Ask you to choose an adoption level (L1-L5)
  • Copy and configure the appropriate files
  • Set up git hooks and Claude Code integration

Adoption Levels

Start small, grow incrementally. Each level builds on the previous one.

Level Name What You Get Value
L1 Safe Development worktree-guard + commit-guard + git-worktrees skill AI won't destroy uncommitted work
L2 Structured Releases L1 + RDD workflow + release specs + conversation logger Structured release process
L3 Quality-Enforced L2 + agents + TDD + skills + review enforcement AI autonomously implements, tests, and reviews
L4 Vision-Aligned L3 + VDD artifacts + decision authority matrix Vision-driven strategic development
L5 Full Autonomous L4 + cloud execution + debate partner + multi-AI review Fully autonomous pipeline

Recommendation: Start with L1 to experience the safety model, then progress to L3 when ready for autonomous AI development.

Enforcement Levels

Rules aren't just documented — they're technically enforced at four levels:

Level Label Mechanism Bypassable?
L5 Hook Enforced (deny) Hook blocks the action No
L4 Hook Warning (ask/block) Hook requires confirmation With user approval
L3 Context Injection Auto-injected into agent context Agent sees it, but no hard block
L2 Prompt-only Rule Written in CLAUDE.md only Yes (no enforcement)

Branch Strategy

A simplified GitFlow with three layers:

main (production)     ← Human promotes (release feedback meeting)
  ↑
develop (integration) ← AI merges after independent review approval
  ↑
release/* (work)      ← AI implements in isolated git worktrees

Features

Safety & Isolation

  • Git worktree isolation — All code changes happen in worktrees, never in the main working tree
  • Commit guards — Blocks force-push to main, --no-verify, and other dangerous operations
  • PR guards — Prevents unauthorized PR operations in cloud environments

Autonomous Development

  • TDD-first — Test before implementation, enforced via context injection
  • Self-review pipeline — AI runs quality checks, self-evaluation, and independent code review
  • Multi-AI review — Different AI perspectives for comprehensive review (e.g., Claude + Codex)
  • Subagent orchestration — Parallel implementation with task decomposition

Vision Alignment

  • VISION.md — Living document defining project direction and values
  • DECISIONS.md — Decision ledger with context and rationale
  • DAILY_SCORE.md — Subjective daily score tracking development satisfaction
  • Debate partner — External AI validates vision alignment before implementation

Specialized Agents

  • code-reviewer — Independent code review with accumulated project knowledge
  • implementer — TDD-driven implementation specialist
  • release-manager — Release creation and versioning

10 Built-in Skills

Skill Purpose
git-worktrees Create and manage isolated worktrees
tdd Parallel TDD execution with subagents
review-now Independent code review on local changes
review-pr PR code review with GitHub comment posting
release-ready Pre-release self-evaluation checklist
release GitHub release creation from commit history
task-decompose Break large tasks into parallel subtasks
dig Structured requirement clarification
security-balance 4-axis security trade-off evaluation
env-secrets Safe handling of environment variables and secrets

Documentation

Detailed documentation is available in both English and Japanese:

Examples

See reference implementations in examples/:

  • minimal — Minimal Level 1 setup (just safety guardrails)

For a full-featured example, explore template/ which contains all hooks, agents, skills, and VDD artifacts for levels L1-L5.

Requirements

  • git 2.20+ (for worktree support)
  • Claude Code CLI
  • jq (for hook scripts)
  • gh CLI (for GitHub integration, L2+)

Contributing

See CONTRIBUTING.md for guidelines.

License

MIT


Built with the belief that AI should have maximum autonomy within enforced boundaries.

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Vision-Driven Development / Release-Driven Development framework for AI-autonomous software development with enforced guardrails.

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