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description Coordinate multiple agents for a complex multi-domain project using PM planning, parallel agent spawning, and QA review

MANDATORY RULES — VIOLATION IS FORBIDDEN

  • Response language follows language setting in .agents/oma-config.yaml if configured.
  • NEVER skip steps. Execute from Step 0 in order. Explicitly report completion of each step to the user before proceeding to the next.
  • You MUST use MCP tools throughout the entire workflow. This is NOT optional.
    • Use code analysis tools (get_symbols_overview, find_symbol, find_referencing_symbols, search_for_pattern) for code exploration.
    • Use memory tools (read/write/edit) for progress tracking.
    • Memory path: configurable via memoryConfig.basePath (default: .serena/memories)
    • Tool names: configurable via memoryConfig.tools in mcp.json
    • Do NOT use raw file reads or grep as substitutes. MCP tools are the primary interface for code and memory operations.
  • Read the oma-coordination skill BEFORE starting. Read .agents/skills/oma-coordination/SKILL.md and follow its Core Rules.
  • Follow the context-loading guide. Read .agents/skills/_shared/core/context-loading.md and load only task-relevant resources.

Vendor Detection

Before starting, determine your runtime environment by following .agents/skills/_shared/core/vendor-detection.md. The detected runtime vendor and each agent's target vendor determine how agents are spawned (Step 4) and monitored (Step 5).

Step 0: Preparation (DO NOT SKIP)

  1. Read .agents/skills/oma-coordination/SKILL.md and confirm Core Rules.
  2. Read .agents/skills/_shared/core/context-loading.md for resource loading strategy.
  3. Read .agents/skills/_shared/runtime/memory-protocol.md for memory protocol.
  4. Record session start using memory write tool:
    • Create session-work.md in the memory base path
    • Include: session start time, user request summary.

Step 1: Analyze Requirements

Analyze the user's request and identify involved domains (frontend, backend, mobile, QA).

  • Single domain: suggest using the specific agent directly.
  • Multiple domains: proceed to Step 2.
  • Use MCP code analysis tools (get_symbols_overview or search_for_pattern) to understand the existing codebase structure relevant to the request.
  • Report analysis results to the user.

Step 2: Run PM Agent for Task Decomposition

// turbo Activate PM Agent to:

  1. Analyze requirements.
  2. Define API contracts.
  3. Create a prioritized task breakdown.
  4. Save plan to .agents/results/plan-{sessionId}.json.
  5. Use memory write tool to record plan completion.

Step 3: Review Plan with User

Present the PM Agent's task breakdown to the user:

  • Priorities (P0, P1, P2)
  • Agent assignments
  • Dependencies
  • You MUST get user confirmation before proceeding to Step 4. Do NOT proceed without confirmation.

Step 4: Spawn Agents by Priority Tier

// turbo Spawn agents for each task by priority tier (P0 first, then P1, etc.). Spawn all same-priority tasks in parallel. Assign separate workspaces to avoid file conflicts.

Per-Agent Dispatch

Resolve the target vendor for each agent from .agents/oma-config.yaml. Use native subagents only when target_vendor === current_runtime_vendor and that runtime supports the vendor's role-subagent path. Otherwise use oma agent:spawn for that agent.

If Claude Code and target vendor is Claude

Use the Agent tool to spawn subagents:

  • Agent(subagent_type="backend-engineer", prompt="Implement backend tasks per plan.", run_in_background=true)
  • Agent(subagent_type="frontend-engineer", prompt="Implement frontend tasks per plan.", run_in_background=true)
  • Multiple Agent tool calls in the same message = true parallel execution
  • Agent definitions: .claude/agents/{agent}.md

If Codex CLI and target vendor is Codex

Spawn native Codex custom agents using .codex/agents/{agent}.toml when available. Native CLI executor path: codex exec "@{agent} ..." using the generated agent file. Pass each agent its task description, API contracts, and relevant context. If native dispatch is not verified in the current runtime, fall back to oma agent:spawn.

If Gemini CLI and target vendor is Gemini

Use native Gemini subagents when available, otherwise fall back to oma agent:spawn. Native CLI executor path: gemini -p "@{agent} ..." using .gemini/agents/{agent}.md.

If target vendor differs from current runtime, or native dispatch is unavailable

oma agent:spawn backend "task description" session-id -w ./backend &
oma agent:spawn frontend "task description" session-id -w ./frontend &
wait

Step 5: Monitor Agent Progress

  • Use memory read tool to poll progress-{agent}.md files
  • Use MCP code analysis tools (find_symbol and search_for_pattern) to verify API contract alignment between agents
  • Use memory edit tool to record monitoring results

Step 6: Run QA Agent Review

After all implementation agents complete, spawn QA Agent to review all deliverables:

  • Security (OWASP Top 10)
  • Performance
  • Accessibility (WCAG 2.1 AA)
  • Code quality

Step 6.1: Measure Quality Score (Conditional)

If automated measurement is available:

  1. Load quality-score.md (conditional, per context-loading.md)
  2. Measure Quality Score based on QA findings
  3. Record as baseline in Experiment Ledger via memory tools

Step 7: Address Issues and Iterate

If QA finds CRITICAL or HIGH issues:

  1. Re-spawn the responsible agent with QA findings.
  2. If Quality Score is active: measure after fix, apply Keep/Discard rule, record in Experiment Ledger.
  3. Repeat Steps 5-7.
  4. If same issue persists after 2 fix attempts: Activate Exploration Loop (load exploration-loop.md per context-loading.md):
    • Generate 2-3 alternative approaches via Exploration Decision template
    • Re-spawn the same agent type with different hypothesis prompts (separate workspaces)
    • QA scores each result
    • Best result adopted, others discarded
    • All experiments recorded in Experiment Ledger
  5. Continue until all critical issues are resolved.
  6. Use memory write tool to record final results.
  7. If Quality Score was measured: generate Experiment Ledger summary and auto-generate lessons from discarded experiments.