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Delivers AI-DLC, Agile, and custom SDLC flows as markdown-based agent systems.","maintainers":[{"name":"ahmetcandev","email":"ahmetcandurukan@gmail.com"}],"readme":"# iris\n\n**AI-native software development with multi-agent orchestration.**\n\niris implements the [AI-Driven Development Lifecycle (AI-DLC)](https://aws.amazon.com/blogs/devops/ai-driven-development-life-cycle/) methodology as a set of markdown-based agents that work with your favorite AI coding tools.\n\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n\n---\n\n## What is AI-DLC?\n\nAI-DLC is a reimagined software development methodology where **AI drives the conversation** and humans validate. Unlike traditional Agile where iterations span weeks, AI-DLC operates in **Bolts** - rapid iterations measured in hours or days.\n\n> \"Traditional development methods were built for human-driven, long-running processes. AI-DLC reimagines the development lifecycle with AI as a central collaborator, enabling rapid cycles measured in hours or days rather than weeks.\"\n\n### AI-DLC vs Traditional Methods\n\n| Aspect | Agile/Scrum | AI-DLC |\n|--------|-------------|--------|\n| Iteration duration | Weeks (Sprints) | Hours/days (Bolts) |\n| Who drives | Human-driven, AI assists | AI-driven, human-validated |\n| Design techniques | Out of scope | Integrated (DDD, TDD, BDD) |\n| Task decomposition | Manual | AI-powered |\n| Phases | Repeating sprints | Rapid three-phase cycles (Inception → Construction → Operations) |\n| Rituals | Daily standups, retrospectives | Mob Elaboration, Mob Construction |\n\n---\n\n## How It Works\n\niris provides four specialized agents that guide you through the entire development lifecycle:\n\n```\n                    ┌─────────────────┐\n                    │  Master Agent   │  Orchestrates & navigates\n                    └────────┬────────┘\n                             │\n        ┌────────────────────┼────────────────────┐\n        ▼                    ▼                    ▼\n┌───────────────┐   ┌───────────────┐   ┌───────────────┐\n│   Inception   │ → │ Construction  │ → │  Operations   │\n│     Agent     │   │     Agent     │   │     Agent     │\n└───────────────┘   └───────────────┘   └───────────────┘\n  Capture intent      Execute bolts      Deploy & monitor\n  Define units        Build & test       Verify & scale\n  Plan stories        Validate stages\n```\n\n### The Three Phases\n\n| Phase | Agent | Purpose | Key Outputs |\n|-------|-------|---------|-------------|\n| **Inception** | Inception Agent | Capture intents, elaborate requirements, decompose into units | User stories, NFRs, Unit definitions, Bolt plans |\n| **Construction** | Construction Agent | Execute bolts through Domain Design → Logical Design → Code Generation → Testing | Domain designs, Logical designs, Code, Tests |\n| **Operations** | Operations Agent | Deploy, verify, and monitor | Deployment units, Monitoring, Runbooks |\n\n---\n\n## Quick Start\n\n### Installation\n\n```bash\nnpx project-iris install\n```\n\nThe installer detects your AI coding tools (Claude Code, Cursor, GitHub Copilot) and sets up:\n- Agent definitions and skills\n- Memory bank structure for context persistence\n- Slash commands for easy agent invocation\n\n### Initialize Your Project\n\n```bash\n# Start the Master Agent\n/iris-master-agent\n\n# Then type:\nproject-init\n```\n\nThis guides you through establishing:\n- **Tech Stack** - Languages, frameworks, databases, infrastructure\n- **Coding Standards** - Formatting, linting, naming, testing strategy\n- **System Architecture** - Architecture style, API design, state management\n- **UX Guide** - Design system, styling, accessibility (optional)\n- **API Conventions** - API style, versioning, response formats (optional)\n\n### Create Your First Intent\n\n```bash\n/iris-inception-agent intent-create\n```\n\nAn **Intent** is your high-level goal:\n- \"User authentication system\"\n- \"Product catalog with search\"\n- \"Payment processing integration\"\n\nThe agent will:\n1. Ask clarifying questions to minimize ambiguity\n2. Elaborate into user stories and NFRs\n3. Define system context\n4. Decompose into loosely-coupled units\n\n### Plan and Execute Bolts\n\n```bash\n# Plan bolts for your stories\n/iris-inception-agent bolt-plan\n\n# Execute a bolt\n/iris-construction-agent bolt-start\n```\n\nEach bolt goes through validated stages:\n1. **Domain Design** - Model business logic using DDD principles\n2. **Logical Design** - Apply patterns and make architecture decisions\n3. **ADR Analysis** - Document significant decisions (optional)\n4. **Code Generation** - Generate production code\n5. **Testing** - Verify correctness with automated tests\n\n**Human validation happens at each stage gate.**\n\n---\n\n## Project Scenarios\n\niris supports both new projects and existing codebases.\n\n### Greenfield (New Projects)\n\nStarting fresh with no existing code:\n\n```bash\n/iris-master-agent\n# Then type: project-init\n```\n\n1. Initialize project with standards (tech stack, coding standards, architecture)\n2. Begin Inception phase immediately\n3. Build from scratch using AI-DLC methodology\n\n### Brownfield (Existing Codebases)\n\nAdding features to an existing codebase requires understanding what's already there:\n\n```bash\n/iris-master-agent\n# Then type: code-elevate\n```\n\n**Code Elevation** analyzes your existing codebase and creates:\n\n- **Static Model** (`memory-bank/elevation/static-model.md`)\n  - Components and their responsibilities\n  - Relationships between components\n  - Key abstractions and patterns used\n\n- **Dynamic Model** (`memory-bank/elevation/dynamic-model.md`)\n  - How components interact for significant use cases\n  - Request/response flows\n  - Event chains and data transformations\n\nAfter code elevation, agents have full context about your existing architecture, enabling them to:\n- Propose changes that fit existing patterns\n- Identify integration points for new features\n- Avoid breaking existing functionality\n\n---\n\n## Key Concepts\n\n### Intent\nA high-level statement of purpose that encapsulates what needs to be achieved - whether a business goal, feature, or technical outcome. It serves as the starting point for AI-driven decomposition.\n\n### Unit\nA cohesive, self-contained work element derived from an Intent. Units are loosely coupled and can be developed independently. Analogous to a Subdomain (DDD) or Epic (Scrum).\n\n### Bolt\nThe smallest iteration in AI-DLC, designed for rapid implementation. Unlike Sprints (weeks), Bolts are **hours to days**. Each bolt encapsulates a well-defined scope of work.\n\n| Type | Best For | Stages |\n|------|----------|--------|\n| **DDD Construction** | Complex business logic, domain modeling | Domain Design → Logical Design → ADR → Code Generation → Testing |\n| **Simple Construction** | UI, integrations, utilities | Plan → Code Generation → Testing |\n\n### Memory Bank\nFile-based storage for all project artifacts. Maintains context across agent sessions and provides traceability between artifacts.\n\n### Standards\nProject decisions that inform AI code generation. Standards ensure consistency across all generated code and documentation.\n\n---\n\n## Project Structure\n\nAfter installation:\n\n```\n.iris/\n├── manifest.yaml              # Installation manifest\n└── aidlc/                     # AI-DLC flow\n    ├── agents/                # Agent definitions\n    ├── skills/                # Agent capabilities\n    ├── templates/             # Artifact templates\n    │   └── standards/         # Standards facilitation guides\n    └── memory-bank.yaml       # Memory bank schema\n\nmemory-bank/                   # Created after project-init\n├── project.yaml               # Project configuration\n│\n├── elevation/                 # Brownfield code analysis (if applicable)\n│   ├── static-model.md        # Components, responsibilities, relationships\n│   └── dynamic-model.md       # Use case interactions\n│\n├── intents/                   # Your captured intents\n│   └── {NNN}-{intent-name}/\n│       ├── requirements.md    # Functional & non-functional requirements\n│       ├── prfaq.md           # Press Release / FAQ\n│       ├── risks.md           # Risk assessment\n│       ├── system-context.md  # System boundaries & actors\n│       ├── units.md           # Unit decomposition overview\n│       └── units/\n│           └── {UUU}-{unit-name}/\n│               ├── unit-brief.md\n│               └── stories/\n│\n├── bolts/                     # Bolt execution records\n│   └── {BBB}-{unit-name}/\n│       ├── bolt.md            # Bolt instance metadata\n│       ├── ddd-01-domain-design.md\n│       ├── ddd-02-logical-design.md\n│       ├── adr-{N}-{slug}.md  # ADR (optional)\n│       └── ddd-03-test-report.md\n│\n├── standards/                 # Project standards\n│   ├── tech-stack.md\n│   ├── coding-standards.md\n│   └── system-architecture.md\n│\n└── operations/                # Deployment context\n    ├── deployment-units/\n    └── playbooks/\n```\n\n---\n\n## Agent Commands\n\n### Master Agent\n```bash\n/iris-master-agent\n```\n| Command | Purpose |\n|---------|---------|\n| `project-init` | Initialize project with standards |\n| `code-elevate` | Analyze existing codebase (brownfield) |\n| `analyze-context` | View current project state |\n| `route-request` | Get directed to the right agent |\n| `explain-flow` | Learn about AI-DLC methodology |\n| `answer-question` | Get help with any iris question |\n\n### Inception Agent\n```bash\n/iris-inception-agent\n```\n| Command | Purpose |\n|---------|---------|\n| `intent-create` | Create a new intent with PRFAQ |\n| `intent-list` | List all intents |\n| `requirements` | Gather requirements with measurements |\n| `risks` | Assess risks with mitigations |\n| `context` | Define system context |\n| `units` | Decompose into units |\n| `story-create` | Create stories for a unit |\n| `bolt-plan` | Plan bolts for stories |\n| `review` | Review inception artifacts |\n\n### Construction Agent\n```bash\n/iris-construction-agent\n```\n| Command | Purpose |\n|---------|---------|\n| `bolt-start` | Start/continue executing a bolt |\n| `bolt-status` | Check bolt progress |\n| `bolt-list` | List all bolts |\n| `bolt-replan` | Replan bolts if needed |\n\n### Operations Agent\n```bash\n/iris-operations-agent\n```\n| Command | Purpose |\n|---------|---------|\n| `build` | Build deployment artifacts |\n| `deploy` | Deploy to environment |\n| `verify` | Verify deployment |\n| `monitor` | Set up monitoring and SLOs |\n| `rollback` | Rollback to previous version |\n\n---\n\n## Why iris?\n\n### AI-Native, Not AI-Retrofitted\nBuilt from the ground up for AI-driven development. AI-DLC is a reimagination based on first principles, not a retrofit of existing methods.\n\n### Human Oversight as Loss Function\nValidation at each stage catches errors early before they cascade downstream. Each validation transforms artifacts into rich context for subsequent stages.\n\n### Design Techniques Built-In\nDDD, TDD, and BDD are integral to the methodology - not optional add-ons. This addresses the \"whitespace\" in Agile that has led to quality issues.\n\n### Tool Agnostic\nWorks with Claude Code, Cursor, GitHub Copilot, and other AI coding assistants. Markdown-based agents work anywhere.\n\n### Context Engineering\nSpecs and Memory Bank provide structured context for AI agents. Agents reload context each session - no more lost knowledge.\n\n---\n\n## Supported Tools\n\n| Tool | Status | Installation |\n|------|--------|--------------|\n| **Claude Code** | Full support | Slash commands in `.claude/commands/` |\n| **Cursor** | Full support | Rules in `.cursor/rules/` (`.mdc` format) |\n| **GitHub Copilot** | Full support | Agents in `.github/agents/` (`.agent.md` format) |\n| **Google Antigravity** | Full support | Agents in `.agent/agents/` |\n\n---\n\n## FAQ\n\n**Q: Agents don't seem to remember previous context?**\nEach agent invocation starts fresh. Agents read context from the Memory Bank at startup. Ensure artifacts are saved after each step.\n\n**Q: How do I reset project state?**\nClear the `memory-bank/` directory to reset all artifacts. To remove iris entirely, delete the `.iris/` directory and tool-specific command files.\n\n**Q: Can I use iris with existing Agile workflows?**\nAI-DLC is designed as a reimagination, not a retrofit. However, familiar concepts (user stories, acceptance criteria) are retained to ease transition.\n\n**Q: What project types is this suited for?**\niris is designed for building complex systems that demand architectural complexity, trade-off management, and scalability. Simpler systems may be better suited for low-code/no-code approaches.\n\n---\n\n## Resources\n\n- [AI-DLC Specification (AWS)](https://aws.amazon.com/blogs/devops/ai-driven-development-life-cycle/)\n\n---\n\n## Analytics & Privacy\n\niris collects **anonymous** usage analytics during installation only. This helps us understand adoption and improve the product.\n\n**What we collect**: OS, shell type, selected IDEs, installation success/failure, approximate location (country level).\n\n**What we don't collect**: No usernames, no file paths, no project contents, no IP addresses stored.\n\n### Opt-Out\n\n```bash\n# Option 1: Environment variable\nIRIS_TELEMETRY_DISABLED=1 npx project-iris@latest install\n\n# Option 2: DO_NOT_TRACK standard\nDO_NOT_TRACK=1 npx project-iris@latest install\n```\n\nAnalytics are automatically disabled in CI environments.\n\nSee [PRIVACY.md](PRIVACY.md) for full details.\n\n---\n\n## License\n\nMIT License - see [LICENSE](LICENSE) for details.\n\n\n","readmeFilename":"README.md"}