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orchestrator
Automated multi-agent orchestrator that spawns CLI subagents in parallel, coordinates via MCP Memory, and monitors progress
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
メニュー
Automated multi-agent orchestrator that spawns CLI subagents in parallel, coordinates via MCP Memory, and monitors progress
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
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| name | orchestrator |
| description | Automated multi-agent orchestrator that spawns CLI subagents in parallel, coordinates via MCP Memory, and monitors progress |
This skill orchestrates CLI subagents via gemini -p "..." --approval-mode=yolo. It uses MCP Memory tools as a shared state bus. Each subagent runs as an independent process.
| 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 |
Memory provider and tool names are configurable via mcp.json:
{
"memoryConfig": {
"provider": "serena",
"basePath": ".serena/memories",
"tools": {
"read": "read_memory",
"write": "write_memory",
"edit": "edit_memory"
}
}
}
PHASE 1 - Plan: Analyze request -> decompose tasks -> generate session ID
PHASE 2 - Setup: Use memory write tool to create orchestrator-session.md + task-board.md
PHASE 3 - Execute: Spawn agents by priority tier (never exceed MAX_PARALLEL)
PHASE 4 - Monitor: Poll every POLL_INTERVAL; handle completed/failed/crashed agents
PHASE 4.5 - Verify: Run oh-my-ag verify {agent-type} per completed agent
PHASE 5 - Collect: Read all result-{agent}.md, compile summary, cleanup progress files
See resources/subagent-prompt-template.md for prompt construction.
See resources/memory-schema.md for memory file formats.
| File | Owner | Others |
|---|---|---|
orchestrator-session.md | orchestrator | read-only |
task-board.md | orchestrator | read-only |
progress-{agent}.md | that agent | orchestrator reads |
result-{agent}.md | that agent | orchestrator reads |
After each agent completes, run automated verification before accepting the result:
oh-my-ag verify {agent-type} --workspace {workspace}
# or with JSON output for programmatic use:
oh-my-ag verify {agent-type} --workspace {workspace} --json
Track user corrections during session execution. See ../_shared/session-metrics.md for full protocol.
When user sends feedback during session:
| 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 |
After each user correction event:
[EDIT]("session-metrics.md", append event to Events table)
At session end, if CD >= 50:
lessons-learned.md with prevention measuresSee ../_shared/memory-protocol.md.
resources/subagent-prompt-template.mdresources/memory-schema.mdconfig/cli-config.yamlscripts/spawn-agent.sh, scripts/parallel-run.shtemplates/../_shared/skill-routing.mdoh-my-ag verify <agent-type>../_shared/session-metrics.md../_shared/api-contracts/../_shared/context-loading.md../_shared/difficulty-guide.md../_shared/reasoning-templates.md../_shared/clarification-protocol.md../_shared/context-budget.md../_shared/lessons-learned.md