SSO MCP Server

--- description: Execute the implementation planning workflow using the plan template to generate design artifacts. handoffs: - label: Create Tasks agent: rainbow.taskify prompt: Break the plan into tasks send: true - label: Create Checklist agent: rainbow.checklist prompt: Create a checklist for the following domain... --- ## User Input ```text $ARGUMENTS ``` You **MUST** consider the user input before proceeding (if not empty). ## Outline **IMPORTANT**: Automatically generate a 'docs:' prefixed git commit message (e.g., 'docs: add implementation plan for feature-name') and commit design.md, research.md, data-model.md, and contracts/ upon completion. 1. **Setup**: Run `.rainbow/scripts/bash/setup-design.sh --json` from repo root and parse JSON for FEATURE_SPEC, IMPL_PLAN, SPECS_DIR, BRANCH. For single quotes in args like "I'm Groot", use escape syntax: e.g 'I'\''m Groot' (or double-quote if possible: "I'm Groot"). 2. **Load context**: Read FEATURE_SPEC, `memory/ground-rules.md`, and `docs/architecture.md` (if it exists). Load IMPL_PLAN template (already copied). 3. **Execute plan workflow**: Follow the structure in IMPL_PLAN template to: - Fill Technical Context (mark unknowns as "NEEDS CLARIFICATION") - Fill Ground-rules Check section from ground-rules - Align with architecture decisions from architecture.md (if available) - Evaluate gates (ERROR if violations unjustified) - Phase 0: Generate research.md (resolve all NEEDS CLARIFICATION) - Phase 1: Generate data-model.md, contracts/, quickstart.md - Phase 1: Update agent context by running the agent script - Re-evaluate Ground-rules Check post-design 4. **Stop and report**: Command ends after Phase 2 planning. Report branch, IMPL_PLAN path, and generated artifacts. ## Phases ### Phase 0: Outline & Research 1. **Extract unknowns from Technical Context** above: - For each NEEDS CLARIFICATION → research task - For each dependency → best practices task - For each integration → patterns task - Review architecture.md (if exists) for relevant architectural decisions and patterns 2. **Generate and dispatch research agents**: ```text For each unknown in Technical Context: Task: "Research {unknown} for {feature context}" For each technology choice: Task: "Find best practices for {tech} in {domain}" If architecture.md exists: Review: Architectural patterns, ADRs, and quality strategies relevant to this feature ``` 3. **Consolidate findings** in `research.md` using format: - Decision: [what was chosen] - Rationale: [why chosen] - Alternatives considered: [what else evaluated] - Architecture alignment: [how decision aligns with architecture.md, if applicable] **Output**: research.md with all NEEDS CLARIFICATION resolved ### Phase 1: Design & Contracts **Prerequisites:** `research.md` complete 1. **Extract entities from feature spec** → `data-model.md`: - Entity name, fields, relationships - Validation rules from requirements - State transitions if applicable - Align with data models and component designs from architecture.md (if available) 2. **Generate API contracts** from functional requirements: - For each user action → endpoint - Use standard REST/GraphQL patterns - Follow API design patterns from architecture.md (if available) - Output OpenAPI/GraphQL schema to `/contracts/` 3. **Agent context update**: - Run `.rainbow/scripts/bash/update-agent-context.sh copilot` - These scripts detect which AI agent is in use - Update the appropriate agent-specific context file - Add only new technology from current plan - Preserve manual additions between markers **Output**: data-model.md, /contracts/*, quickstart.md, agent-specific file ## Key rules - Use absolute paths - ERROR on gate failures or unresolved clarifications - If `docs/architecture.md` exists, ensure feature design aligns with architectural decisions, patterns, and quality strategies - Reference relevant ADRs (Architecture Decision Records) from architecture.md when making design choices - Maintain consistency with the technology stack and deployment architecture defined in architecture.md