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AI Coding Project Toolkit

A structured framework for AI agents to build software autonomously — with verification, guardrails, and human oversight built in.

View Landing Page · Example Project Output

What Is This?

A framework that turns ad-hoc AI prompting into a repeatable workflow with automatic verification. Three phases:

  1. Specify — Guided Q&A produces plans/greenfield/PRODUCT_SPEC.md and plans/greenfield/TECHNICAL_SPEC.md
  2. Plan — Generator creates plans/greenfield/EXECUTION_PLAN.md, plans/PLAN_STATUS.md, a scoped plans/greenfield/AGENTS.md, and a durable root AGENTS.md
  3. Execute — AI agents work task-by-task with automatic verification after each one

What makes execution robust?

  • Code verification — Multi-agent system checks each task against its acceptance criteria
  • TDD enforcement — Verifies tests exist, were written first, and have meaningful assertions
  • Security scanning — Dependency audits, secrets detection, and static analysis at checkpoints
  • Auto-advance — Phases chain automatically when no human intervention is needed
  • Stuck detection — Agents escalate to humans instead of spinning on failures
  • Cross-model review — Optional second-opinion review using OpenAI Codex CLI

Prerequisites

  • Claude Code — Anthropic's CLI for Claude (primary interface)
  • Git — Required for the branching and commit workflow

Codex CLI users: see Codex CLI Setup. Not using Claude Code? See Manual Setup.

Cross-Agent Runtime (Optional)

If you switch between Claude Code and Codex across multiple laptops, bootstrap shared machine-level skills and MCPs from this toolkit repo:

./scripts/bootstrap-agent-runtime.sh

This sets up:

  • ~/.claude/skills/ (Claude personal skills)
  • ~/.agents/skills/ (Codex user skills)
  • ~/.codex/skills -> ~/.agents/skills compatibility symlink (when ~/.codex/skills is not already in use)
  • MCP servers for both agents from config/mcp/servers.json

Safety defaults:

  • Existing non-toolkit skills are preserved (not overwritten)
  • Existing MCPs are discovered and normalized via add-mcp list + add-mcp sync (use --no-normalize-existing for manifest-only MCP apply)

Quick Start

# 1. Clone the toolkit and set up your project (one-time)
git clone https://github.com/benjaminshoemaker/ai_coding_project_base.git
cd ai_coding_project_base
/setup ~/Projects/my-new-app

# 2. Generate specs, plan, and execute (from your project directory)
cd ~/Projects/my-new-app
/product-spec
/technical-spec
/generate-plan
cd plans/greenfield
/fresh-start

/fresh-start loads context and auto-advances through phase-prep → phase-start → phase-checkpoint for each phase, stopping only when human input is needed. To resume later, run /go — it detects where you left off.

For feature development in existing projects, see Feature Workflow.

Workflow Overview

┌─────────────────────────────────────────────────────────────────────────┐
│                         SPECIFICATION PHASE                             │
├─────────────────────────────────────────────────────────────────────────┤
│                                                                         │
│   Your Idea                                                             │
│       ↓                                                                 │
│   /product-spec  ───────────→  plans/greenfield/PRODUCT_SPEC.md        │
│       ↓                                                                 │
│   /technical-spec  ─────────→  plans/greenfield/TECHNICAL_SPEC.md      │
│       ↓                                                                 │
│   [Auto-Verify] ─────────────→  Check context preservation & quality    │
│                                                                         │
└─────────────────────────────────────────────────────────────────────────┘
                                    ↓
┌─────────────────────────────────────────────────────────────────────────┐
│                           PLANNING PHASE                                │
├─────────────────────────────────────────────────────────────────────────┤
│                                                                         │
│   /generate-plan  ──────────→  plans/greenfield/EXECUTION_PLAN.md      │
│                                 plans/PLAN_STATUS.md                   │
│                                 AGENTS.md + plans/greenfield/AGENTS.md │
│       ↓                                                                 │
│   [Auto-Verify] ─────────────→  Check context preservation & quality    │
│                                                                         │
└─────────────────────────────────────────────────────────────────────────┘
                                    ↓
┌─────────────────────────────────────────────────────────────────────────┐
│                          EXECUTION PHASE                                │
├─────────────────────────────────────────────────────────────────────────┤
│                                                                         │
│   cd plans/greenfield                                               │
│       ↓                                                                 │
│   /fresh-start  ────────────→  Orient to scoped plan, load context      │
│       ↓                                                                 │
│   /phase-start N  ──────────→  Execute phase (branch + commits)         │
│       ↓                                                                 │
│   /phase-checkpoint N  ─────→  Verify, test, security scan              │
│                                                                         │
│   Phase 1 → Checkpoint → Phase 2 → Checkpoint → Phase 3 → ...          │
│                                                                         │
└─────────────────────────────────────────────────────────────────────────┘

Key Commands

Command Description
/product-spec Generate product specification
/technical-spec Generate technical specification
/generate-plan Generate the greenfield execution plan plus root and scoped AGENTS files
/go Resume execution from wherever you left off
/fresh-start Orient to project, load context, begin execution
/phase-start N Execute phase N (creates branch, commits per task)
/phase-checkpoint N Verify phase: tests, lint, security, then production checks
/verify-task X.Y.Z Verify a specific task's acceptance criteria
/create-pr Create GitHub PR with automatic Codex review

See Command Reference for the full list including feature, setup, verification, and recovery commands.

What You Get

your-project/
├── AGENTS.md                # Durable project-wide workflow rules
├── CLAUDE.md                # Root Claude shim
├── LEARNINGS.md             # Discovered patterns (created as you work)
├── DEFERRED.md              # Deferred requirements (captured during Q&A)
├── plans/
│   ├── PLAN_STATUS.md       # Single current-plan pointer and plan history
│   ├── archive/             # Superseded/rejected/abandoned greenfield snapshots
│   └── greenfield/
│       ├── PRODUCT_SPEC.md      # What you're building
│       ├── TECHNICAL_SPEC.md    # How it's built
│       ├── EXECUTION_PLAN.md    # Tasks with acceptance criteria
│       ├── AGENTS.md            # Greenfield execution guidance
│       └── CLAUDE.md            # Scoped Claude shim
├── features/
│   └── archive/             # Superseded/rejected/abandoned feature snapshots
├── .claude/
│   ├── verification-config.json
│   └── toolkit-version.json # Tracks toolkit sync state (global skill resolution)
└── [your code]

Skills resolve globally from ~/.claude/skills/ (managed by ./scripts/bootstrap-agent-runtime.sh) rather than long-lived per-project copies. These documents persist across sessions, enabling any AI agent to pick up where another left off. plans/PLAN_STATUS.md tells Claude Code, Codex, and other agents which plan is current so historical specs and abandoned experiments stay available without becoming accidental requirements.

Documentation

Contributing

See CONTRIBUTING.md for guidelines.

npm run lint      # Check markdown
npm run lint:fix  # Auto-fix

License

MIT

About

A structured prompt framework for building software products with AI coding assistants. This toolkit guides you through product specification, technical design, and implementation planning—producing documents that AI agents can execute against.

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