# AI Factory > **Stop configuring. Start building.** You want to build with AI, but setting up the right context, prompts, and workflows takes time. AI Factory handles all of that so you can focus on what matters — shipping quality code. **One command. Full AI-powered development environment.** ```bash ai-factory init ``` --- ## Why AI Factory? - **Zero configuration** — installs relevant skills, configures integrations - **Best practices built-in** — logging, commits, code review, all following industry standards - **Spec-driven development** — AI follows plans, not random exploration. Predictable, resumable, reviewable - **Community skills** — leverage [skills.sh](https://skills.sh) ecosystem or generate custom skills - **Stack-agnostic** — works with any language, framework, or platform - **Multi-agent support** — Claude Code, Cursor, Windsurf, Roo Code, Kilo Code, Antigravity, OpenCode, Warp, Zencoder, Codex CLI, Codex app, GitHub Copilot, Gemini CLI, Junie, Qwen Code, or [any agent](docs/getting-started.md#supported-agents) --- ## Installation ### Using npm ```bash npm install -g ai-factory ``` ### Using mise ```bash mise use -g npm:ai-factory ``` ## Quick Start ```bash # In your project directory (interactive wizard) ai-factory init # Or non-interactive with flags ai-factory init --agents claude,codex --mcp playwright,github ``` This will: - Ask which AI agent you use (or use `--agents` flag) - Install relevant skills (or use `--skills` flag) - Configure MCP servers (or use `--mcp` flag) Then open your AI agent and start working: ``` /aif ``` Codex CLI and Codex app use `$aif` style invocations after installation. Need CLI flags, update/upgrade details, or extension commands? See [Getting Started](docs/getting-started.md). Need slash-command reference? See [Core Skills](docs/skills.md). ## Example Workflow ```bash # Explore options and requirements before planning (optional) /aif-explore Add user authentication with OAuth # Need a strictly verified answer before changing anything? /aif-grounded Does this repo already support OAuth providers? # Plan a feature — creates branch, analyzes codebase, builds step-by-step plan /aif-plan Add user authentication with OAuth # Optionally refine the plan with deeper analysis /aif-improve # Execute the plan — implements tasks one by one, commits at checkpoints /aif-implement # Create a knowledge reference from docs AI doesn't know about /aif-reference https://docs.example.com/api-reference --name example-api # Fix a bug — AI learns from every fix and gets smarter over time /aif-fix TypeError: Cannot read property 'name' of undefined # Set up CI pipeline — GitHub Actions or GitLab CI with linting, SA, tests /aif-ci github # Generate project documentation — README + docs/ with topics /aif-docs ``` See the full [Development Workflow](docs/workflow.md) with diagram and decision table. --- ## Skills only via skills.sh If you only need the `aif-*` skills without full AI-Factory setup, use: ```bash npx skills add lee-to/ai-factory --skill '*' ``` > **Disclaimer:** This installs only the skill files. CLI commands (`ai-factory init/update/upgrade`), MCP auto-configuration, agent transformers, and the interactive wizard are **not** available. For the full experience, use the [npm installation](#installation) above. --- ## Documentation | Guide | Description | |-------|-------------| | [Getting Started](docs/getting-started.md) | What is AI Factory, supported agents, CLI commands | | [Development Workflow](docs/workflow.md) | Workflow diagram, when to use `explore` vs `grounded`, spec-driven approach | | [Research and System Analysis](docs/research.md) | Adaptive ultra research bundles with C4, ADR, dependency, and traceability views | | [Reflex Loop](docs/loop.md) | Iterative generate → evaluate → critique → refine workflow | | [Subagents](docs/subagents.md) | Bundled Claude subagents and the baseline Codex native agent-file bundle, including managed Codex config | | [Core Skills](docs/skills.md) | All slash commands — explore, grounded, plan, fix, implement, evolve, docs, and more | | [Quality Gates](docs/quality-gates.md) | Machine-readable `aif-gate-result` summaries for verify, review, security, and rules gates | | [Skill Evolution](docs/evolve.md) | How /aif-fix patches feed into /aif-evolve to generate smarter skill rules | | [Plan Files](docs/plan-files.md) | Plan files, self-improvement patches, skill acquisition | | [Security](docs/security.md) | Two-level security scanning for external skills | | [Extensions](docs/extensions.md) | Writing and installing extensions — commands, injections, MCP, agents | | [Configuration](docs/configuration.md) | `.ai-factory.json`, MCP servers, project structure, best practices | | [Config Reference](docs/config-reference.md) | Full `config.yaml` key reference and skill read/write matrix | ## Links - [Official Website](https://aif.cutcode.dev) - AI Factory website - [aif-handoff](https://github.com/lee-to/aif-handoff) - Autonomous Kanban board built on AI Factory - If AI Factory feels too simple for your goals, try [HLV](https://github.com/lee-to/hlv) - [skills.sh](https://skills.sh) - Skill marketplace - [Agent Skills Spec](https://agentskills.io) - Skill specification - [Claude Code](https://claude.ai/code) - Anthropic's AI coding agent - [Cursor](https://cursor.com) - AI-powered code editor - [OpenCode](https://opencode.ai) - Open-source AI coding agent - [Roo Code](https://roocode.com) - AI coding agent for VS Code - [Kilo Code](https://kilo.ai) - Open-source agentic coding platform - [Windsurf](https://windsurf.com) - AI-powered code editor by Codeium - [Warp](https://www.warp.dev) - Intelligent terminal with AI agent - [Zencoder](https://zencoder.ai) - AI coding agent for VS Code and JetBrains - [Codex CLI](https://github.com/openai/codex) - OpenAI's coding agent - [Codex app](https://openai.com/codex/) - OpenAI's coding agent app - [Gemini CLI](https://github.com/google-gemini/gemini-cli) - Google's coding agent - [Antigravity](https://antigravity.dev) - AI coding agent - [Junie](https://www.jetbrains.com/junie/) - JetBrains' AI coding agent ## License MIT