oma-orchestrator

name	oma-orchestrator
description	Automated multi-agent orchestrator that spawns CLI subagents in parallel, coordinates via MCP Memory, and monitors progress. Use for orchestration, parallel execution, and automated multi-agent workflows.

Orchestrator - Automated Multi-Agent Coordinator

Scheduling

Goal

Automatically orchestrate multi-agent execution with task decomposition, native/fallback dispatch, memory coordination, progress monitoring, verification, QA cross-review, retry, and result collection.

Intent signature

• User asks to orchestrate, run in parallel, automate multi-agent execution, or coordinate full-stack work end to end.
• Task requires multiple specialist agents and a persistent review/remediation loop.

When to use

• Complex feature requires multiple specialized agents working in parallel
• User wants automated execution without manually spawning agents
• Full-stack implementation spanning backend, frontend, mobile, and QA
• User says "run it automatically", "run in parallel", or similar automation requests

When NOT to use

• Simple single-domain task -> use the specific agent directly
• User wants step-by-step manual control -> use oma-coordination
• Quick bug fixes or minor changes

Expected inputs

• Complex feature or workflow request
• Project config, model/vendor routing, agent types, task constraints, and workspace/session needs
• Acceptance criteria and verification expectations

Expected outputs

• Orchestrator session state, task board, progress files, result files, and final summary
• Specialist agent outputs after mechanical checks, automated verify, and QA cross-review
• Review history and retry/remediation status when loops fail

Dependencies

• .agents/oma-config.yaml, .codex/agents/*.toml, .gemini/agents/*.md, or fallback oma agent:spawn
• Memory provider config, subagent prompt template, scripts, task templates, verify script, and session metrics

Control-flow features

• Branches by vendor/native dispatch availability, priority tiers, agent completion/failure, verification status, QA verdict, retry limits, and clarification debt
• Spawns processes/agents and reads/writes memory/result files
• Blocks termination until persistent workflows complete

Structural Flow

Entry

1. Resolve agent vendor routing and runtime dispatch path.
2. Decompose request into priority-tiered tasks.
3. For each task, classify into one or more domain_tags by matching against the Intent signature block of each installed .agents/skills/oma-*/SKILL.md. Tasks that match no domain confidently inherit the union of their parent feature's tags.
4. Build a per-task exposed_skill_set = skills whose name is in domain_tags. If |exposed_skill_set| < 2 after classification, fall back to the full installed set (flat exposure) and record exposure_fallback: true in the task board.
5. Create session memory and task board with exposed_skill_set and exposure_fallback per task.

Scenes

1. PREPARE: Plan, setup session ID, and initialize memory files.
2. ACT: Spawn agents by priority tier within parallelism limits.
3. VERIFY: Run self-check, oma verify, and QA cross-review loop.
4. RECOVER: Retry failed agents with review history when limits allow.
5. FINALIZE: Collect result files, compile summary, and clean progress files.

Transitions

• If native dispatch is available for current runtime/vendor, use it.
• If vendors differ or native path is unavailable, use fallback spawn.
• If verify or QA fails, feed feedback back to the implementation agent.
• If review loop limits are exceeded, report review history and quality warning.
• If a task's exposed_skill_set excludes a skill that a recovered failure indicates was needed, re-classify the task and re-dispatch with the expanded set rather than retrying against the original narrow set.

Failure and recovery

• Retry failed agents up to configured limits.
• Re-spawn with review history when review loop is exhausted.
• Pause or request re-specification when clarification debt thresholds are exceeded.

Exit

• Success: all tasks complete, verify/review pass, and results are summarized.
• Partial success: failed agents, exhausted review loops, or clarification debt are explicit.

Logical Operations

Actions

Action	SSL primitive	Evidence
Read config and task context	READ	oma config, routing, request
Classify task into domain tags	INFER	task text vs each skill's Intent signature
Compute exposed skill set	SELECT	intersection of domain tags and installed skills
Select dispatch path	SELECT	Native vs fallback
Write session state	WRITE	task board and memory files
Spawn agents	CALL_TOOL	native CLI or oma agent:spawn
Poll progress	READ	progress/result files
Run verification	CALL_TOOL	oma verify, tests, QA
Update retry state	UPDATE_STATE	loop counters and CD metrics
Report final result	NOTIFY	compiled summary

Tools and instruments

• Native CLI subagent dispatch, fallback spawn scripts, memory tools, verify script, QA agent
• Session metrics, prompt templates, task templates

Canonical command path

oma agent:spawn <agent-type> "<task>" <session-id> -w <workspace>
oma verify <agent-type> --workspace <workspace> --json

When native runtime dispatch is available, prefer the runtime-specific native path listed in this skill before falling back to oma agent:spawn.

Resource scope

Scope	Resource target
LOCAL_FS	Session, task-board, progress, result, config files
PROCESS	Agent CLI processes and verify scripts
MEMORY	Session state and clarification debt
CODEBASE	Workspaces owned by spawned agents

Preconditions

• Task is decomposable into specialist agent work.
• Runtime/vendor dispatch path or fallback exists.

Effects and side effects

• Spawns agents and writes session/progress/result artifacts.
• May cause code changes through specialist agents.
• May trigger iterative review and retries.

Guardrails

1. Orchestrate per-agent dispatch from the project configuration before spawning any agent.
2. If target_vendor === current_runtime_vendor and the runtime has a verified native path, use native dispatch.
3. Otherwise fall back to oma agent:spawn.
4. Never exceed the configured parallelism or retry limits.
5. Keep session state, task-board state, progress files, and result files aligned throughout the run.
6. Domain gating must be soft: prefer a narrower exposed_skill_set, but fall back to flat exposure when classification confidence is low rather than starving a task of a required specialist.

Current native executor paths:

• Claude Code: Agent tool with .claude/agents/{agent}.md definitions (multiple Agent tool calls in one message run in parallel; results return synchronously — no polling)
• OpenCode: native task tool with subagent_type: {agent-id}; do not use oma agent:spawn for same-session OpenCode work because it will not appear as a native child task
• Codex CLI: codex exec "@agent ..." using .codex/agents/*.toml
• Gemini CLI: gemini -p "@agent ..." using .gemini/agents/*.md

Vendor-specific execution protocols are injected automatically for fallback CLI runs.

Configuration

Setting	Default	Description
MAX_PARALLEL	3	Max concurrent subagents
MAX_RETRIES	2	Retry attempts per failed task
POLL_INTERVAL	30s	Status check interval
MAX_TURNS (impl)	20	Turn limit for backend/frontend/mobile
MAX_TURNS (review)	15	Turn limit for qa/debug
MAX_TURNS (plan)	10	Turn limit for pm

These are skill-level defaults applied by the orchestrating agent; they are not read from config/cli-config.yaml (which carries only vendor CLI and execution settings such as results_dir and timeout).

Memory Configuration

Memory provider and tool names are configurable via .agents/mcp.json (not the repo-root .mcp.json, which is the Claude Code MCP server config):

{
  "memoryConfig": {
    "provider": "file",
    "basePath": ".agents/state/memories",
    "tools": {
      "read": "Read",
      "write": "Write",
      "edit": "Edit"
    }
  }
}

Workflow Phases

PHASE 1 - Plan: Analyze request -> decompose tasks -> generate session ID PHASE 1.5 - Domain gate: For each task, intersect Intent signature matches across installed skills to derive exposed_skill_set. Record exposure_fallback: true when the intersection is too small to be useful and the flat library is used instead. PHASE 2 - Setup: Use memory write tool to create orchestrator-session.md + task-board.md (include exposed_skill_set per task) PHASE 3 - Execute: Spawn agents by priority tier (never exceed MAX_PARALLEL); inject only exposed_skill_set into each subagent's available specialist list PHASE 4 - Monitor: Poll every POLL_INTERVAL; handle completed/failed/crashed agents PHASE 4.5 - Verify: Run mechanical checks for every completed agent; run oma verify {agent-type} only for backend, frontend, mobile, qa, debug, and pm; then run QA cross-review for every completed implementation PHASE 5 - Collect: Read all result-{agent}-{sessionId}.md, compile summary, cleanup progress files

See resources/subagent-prompt-template.md for prompt construction. See resources/memory-schema.md for memory file formats.

Memory File Ownership

File	Owner	Others
orchestrator-session.md	orchestrator	read-only
task-board.md	orchestrator	read-only
progress-{agent}[-{sessionId}].md	that agent	orchestrator reads
result-{agent}[-{sessionId}].md	that agent	orchestrator reads

Agent-to-Agent Review Loop (PHASE 4.5)

After each agent completes, enter an iterative review loop, not a single-pass verification.

Loop Flow

Agent completes work
    ↓
[1] Mechanical Self-Check: lint, type-check, tests, diff scope
    ↓
[2] Verify: For supported types, run `oma verify {agent-type} --workspace {workspace}`
    Unsupported (`db`, `refactor`, `architecture`, `tf-infra`, `docs`) → record SKIP and continue
    ↓ FAIL → Agent receives feedback, fixes, back to [1]
    ↓ PASS
[3] Cross-Review: QA agent reviews the changes
    ↓ FAIL → Agent receives review feedback, fixes, back to [1]
    ↓ PASS
Accept result

Step Details

[1] Mechanical Self-Check (formerly "Self-Review"): Before requesting external review, the implementation agent must:

• Run lint, type-check, and tests in the workspace
• Verify only planned files were modified (diff scope check)
• Fix any mechanical failures (compile errors, test failures)

Quality judgment is NOT performed in this step. Design quality, architecture alignment, and acceptance criteria satisfaction are evaluated exclusively in [3] Cross-Review by the QA agent. Reason: Self-evaluation bias causes agents to consistently overrate their own output (ref: Anthropic harness design research).

[2] Automated Verify:

oma verify {agent-type} --workspace {workspace} --json

• Run only for backend, frontend, mobile, qa, debug, and pm.
• For db, refactor, architecture, tf-infra, and docs, record that automated verify is unsupported and continue to QA cross-review after the mechanical checks.
• PASS (exit 0): Proceed to cross-review
• FAIL (exit 1): Feed verify output back to the agent as correction context

[3] Cross-Review: Spawn QA agent to review the changes:

• QA agent reads the diff, runs checks, evaluates against acceptance criteria

• If docs/CODE-REVIEW.md exists, QA agent uses it as the review checklist

• QA agent outputs: PASS (with optional nits) or FAIL (with specific issues)
• On FAIL: issues are fed back to the implementation agent for fixing

Loop Limits

Counter	Max	On Exceeded
Self-check + fix cycles	3	Escalate to cross-review regardless
Cross-review rejections	2	Report to user with review history
Total loop iterations	5	Force-complete with quality warning

Review Feedback Format

When feeding review results back to the implementation agent:

## Review Feedback (iteration {n}/{max})
**Reviewer**: {self / verify / qa-agent}
**Verdict**: FAIL
**Issues**:
1. {specific issue with file and line reference}
2. {specific issue}
**Fix instruction**: {what to change}

This replaces single-pass verification. Most "nitpicking" should happen agent-to-agent. Human review is reserved for final approval, not catching lint errors.

Retry Logic (after review loop exhaustion)

Before starting any retry, check the termination conditions (OR, whichever fires first wins):

1. Retry cap: retry count for this agent has reached MAX_RETRIES — do not start another cycle.
2. Session cost cap: if a quota cap is configured (loadQuotaCap() from cli/io/session-cost.ts; no cap → skip), call checkCap(sessionId, cap). On exceeded === true, save the agent's partial results, report early termination due to quota, and do not spawn the next retry or any remaining agents in the tier.

If neither condition fires:

• 1st retry: Re-spawn agent with full review history as context
• 2nd retry: Re-spawn with "Try a different approach" + review history
• After MAX_RETRIES exhausted (cost cap not exceeded): activate the Exploration Loop (see orchestrate.md Step 5): generate 2-3 alternative hypotheses, spawn the same agent type with different hypothesis prompts in parallel separate workspaces, score with Quality Score when available, keep the highest-scoring approach, and record all experiments in the Experiment Ledger.
• Final failure: Report to user with complete review trail, ask whether to continue or abort

Clarification Debt (CD) Monitoring

Track user corrections during session execution. See ../_shared/core/session-metrics.md for full protocol.

Event Classification

When user sends feedback during session:

• clarify (+10): User answering agent's question
• correct (+25): User correcting agent's misunderstanding
• redo (+40): User rejecting work, requesting restart

Threshold Actions

CD Score	Action
CD >= 50	RCA Required: QA agent must add entry to lessons-learned.md
CD >= 80	Session Pause: Request user to re-specify requirements
redo >= 2	Scope Lock: Request explicit allowlist confirmation before continuing

Recording

After each user correction event:

[EDIT]("session-metrics.md", append event to Events table)

At session end, if CD >= 50:

1. Include CD summary in final report
2. Trigger QA agent RCA generation
3. Update lessons-learned.md with prevention measures

References

• Prompt template: resources/subagent-prompt-template.md
• Memory schema: resources/memory-schema.md
• Config: config/cli-config.yaml
• Scripts: scripts/spawn-agent.sh, scripts/parallel-run.sh, scripts/verify.sh
• Task templates: templates/
• Skill-to-agent mapping: ../_shared/core/skill-routing.md
• Verification: scripts/verify.sh <agent-type>
• Session metrics: ../_shared/core/session-metrics.md
• API contract template (SSOT): ../_shared/core/api-contracts/template.md; read generated contracts from .agents/results/api-contracts/ (run artifact) or docs/plans/contracts/ (durable spec)
• Context loading: ../_shared/core/context-loading.md
• Difficulty guide: ../_shared/core/difficulty-guide.md
• Reasoning templates: ../_shared/core/reasoning-templates.md
• Clarification protocol: ../_shared/core/clarification-protocol.md
• Context budget: ../_shared/core/context-budget.md
• Lessons learned: ../_shared/core/lessons-learned.md