maestro.yaml

# Maestro AI Diamond Chain workflow definition
name: my-project
description: Squad-based development using AI Diamond Chain methodology

# Supported tools - agent runtimes to scaffold and use
# Supported values: opencode, amp
tools:
  - opencode

# Squad definition - maps roles to agent configurations
agents:
  architect:
    role: architect
    phase: discovery

  researcher:
    role: researcher
    phase: discovery

  ux-designer:
    role: ux-designer
    phase: discovery

  frontend-engineer:
    role: frontend-engineer
    phase: build

  backend-engineer:
    role: backend-engineer
    phase: build

  devops-sre:
    role: devops-sre
    phase: build

  qa-engineer:
    role: qa-engineer
    phase: quality

  code-reviewer:
    role: code-reviewer
    phase: quality

# AI Diamond Chain Configuration
# Alternating Discovery and Implementation diamonds with Learn & Feed loop
diamond_chain:
  mode: infinite # or "fixed:N" for limited iterations
  learning_accumulation: true # Compound learnings across diamonds

# Diamond Definitions
diamonds:
  # Discovery Diamond: "What should we build?"
  discovery:
    description: Understand the problem and decide what to build

    # Phase 1: Diverge (Explore)
    diverge:
      parallel: true
      agents: [researcher, ux-designer, architect]
      activities:
        researcher:
          - market_analysis
          - competitive_review
          - trend_identification
        ux-designer:
          - user_research
          - needs_exploration
          - persona_development
        architect:
          - tech_hypotheses
          - feasibility_exploration
          - constraint_analysis
      duration_target: "2-3 days" # AI-accelerated

    # Phase 2: Converge (Define)
    converge:
      agent: maestro
      outputs:
        - discovery-decision-gate.md
      criteria:
        - problem_validated
        - scope_defined
        - success_criteria_set
        - market_opportunity_sized

    decision_gate:
      options: [GO, NO-GO, REFINE]
      auto_trigger_next: false # Human approval required
      confidence_threshold: 70 # Minimum for GO decision
      refinement_triggers:
        - known_unknowns_count_exceeds: 2
        - conflicting_findings
        - low_confidence_critical_items
        - missing_dependencies

  # Implementation Diamond: "How do we build it?"
  implementation:
    description: Build and ship the solution

    # Phase 1: Diverge (Explore)
    diverge:
      parallel: true
      agents: [architect, backend-engineer, frontend-engineer, devops-sre]
      activities:
        architect:
          - architecture_options
          - tradeoff_analysis
          - pattern_exploration
        backend-engineer:
          - api_prototyping
          - db_design_options
        frontend-engineer:
          - ui_pattern_exploration
          - component_prototyping
        devops-sre:
          - infrastructure_options
          - deployment_strategies
      duration_target: "1-2 days" # AI-accelerated

    # Phase 2: Converge (Deliver)
    converge:
      parallel: true
      build_agents: [backend-engineer, frontend-engineer, devops-sre]
      quality_agents: [qa-engineer, code-reviewer]
      outputs:
        - implementation-decision-gate.md
        - build-report.md
        - quality-gate-report.md
      criteria:
        - solution_works
        - quality_met
        - performance_acceptable
        - security_scan_passed

    decision_gate:
      options: [SHIP, NO-SHIP, FIX]
      auto_trigger_next: true # Auto-trigger Learn & Feed
      quality_threshold: 80 # Minimum quality score for SHIP
      fix_max_retries: 3

  # Learn & Feed: The Chain Link
  learn_and_feed:
    description: Capture learnings to feed next Discovery Diamond
    enabled: true
    auto_trigger_after: implementation # Auto-run after ship
    agents: [researcher, ux-designer, architect]
    activities:
      researcher:
        - usage_analytics
        - market_response_analysis
      ux-designer:
        - user_feedback_synthesis
        - satisfaction_analysis
      architect:
        - technical_learnings
        - performance_assessment
    outputs:
      - learned-report.md
    queue_for_next_discovery: true

# Legacy workflow configuration (maintained for backward compatibility)
workflow:
  discovery:
    parallel: true
    confidence_threshold: 70
    max_refinement_rounds: 3

  synthesis:
    approval: true
    refinement_on:
      - low_confidence_critical_items
      - conflicting_findings
      - known_unknowns_count_exceeds: 2
      - missing_dependencies

  build:
    parallel: true
    continuous_feedback:
      enabled: true
      trigger_on:
        - module_complete
        - checkpoint_reached
        - pr_merged
    modules:
      - name: backend-core
        agent: backend-engineer
        checkpoint: "API contracts defined"
        qa_entry_point: true
      - name: infrastructure
        agent: devops-sre
        checkpoint: "Infrastructure as code complete"
        qa_entry_point: true
      - name: frontend-shell
        agent: frontend-engineer
        depends_on: [backend-core]
        checkpoint: "UI components render with mock data"
        qa_entry_point: true
    feedback_queue:
      enabled: true
      file: "feedback-queue.md"
      auto_pause_on_critical: true

  quality:
    parallel: true
    on_failure: fix_and_retry
    max_retries: 3
    validations:
      usecase_coverage:
        required: true
        min_coverage_percent: 80
      workflow_testing:
        required: true
        test_happy_path: true
        test_error_paths: true
        test_edge_cases: true
      edge_case_coverage:
        required: true
        categories:
          - boundary_conditions
          - empty_null_states
          - concurrent_access
          - failure_modes
          - security_edge_cases