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Get Started Free →Launch a specialized agent for a subtask. Assigns identity, workspace scope, available commands, and communication protocol. Supports tmux-based (visual) or subprocess (headless) execution backends. The primary way to parallelize work. Triggers on: "spawn", "launch agent", "start worker", "parallelize"
.claude/skills/miosa-osa-spawn/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | -31% | 0% |
| case-03 | ✗→✓ | ▲ Improved | -2% | 0% |
| case-04 | ✗→✓ | ▲ Improved | -44% | 0% |
| case-05 | ✗→✓ | ▲ Improved | -24% | 0% |
| case-11 | ✗→✓ | ▲ Improved | -52% | 0% |
> Launch a specialized agent for a focused subtask.
Create and start an agent runtime dedicated to a specific subtask. Each spawned agent gets its own identity, scoped workspace access, command allowlist, and communication channel back to the orchestrator. Use this to parallelize work, isolate concerns, or assign specialized tasks to purpose-built agents.
bash# Spawn an agent from the library /spawn researcher --task "Find pricing benchmarks for AI SaaS" # Spawn with explicit backend /spawn coder --backend tmux --task "Implement the search endpoint" # Spawn with scoped workspace access /spawn analyst --scope node:money-revenue --task "Generate Q1 revenue report" # Spawn with budget limit /spawn writer --budget 50000 --task "Draft the pitch deck content" # Spawn with custom identity /spawn --name "pricing-researcher" --role "Market Analyst" --task "Compare competitor pricing" # Spawn multiple agents /spawn researcher analyst writer --parallel --tasks-from plan.md
| Flag | Type | Default | Description | |------|------|---------|-------------| | <agent> | positional | required | Agent name from library, or custom with --name | | --task | string | required | The specific task to assign | | --name | string | auto | Custom agent name (overrides library name) | | --role | string | from library | Role description for the agent's identity | | --backend | enum | subprocess | subprocess (headless), tmux (visual), docker (isolated) | | --scope | string | full | Workspace scope: full, node:<name>, read-only, glob pattern | | --commands | string] | all | Allowlisted commands (e.g., search,read,write) | | --budget | int | from workspace | Token budget for this agent | | --timeout | duration | 30m | Maximum runtime before auto-terminate | | --adapter | enum | claude | LLM backend: claude, codex, gemini, ollama | | --parallel | flag | false | Spawn multiple agents simultaneously | | --tasks-from | path | — | Read task assignments from a file | | --quiet | flag | false | Suppress spawn confirmation output |
--name), build identity from flags. Load agent's system prompt, capabilities, and default configuration./budget tracker.subprocess: headless process, output captured to log filetmux: named tmux pane, visible in /boarddocker: isolated container with mounted workspacemarkdown## Agent Spawned | Field | Value | |-------|-------| | ID | agent-pricing-researcher-a1b2 | | Name | pricing-researcher | | Role | Market Analyst | | Backend | subprocess | | Scope | node:money-revenue, node:ai-masters (read-only) | | Budget | 50,000 tokens | | Timeout | 30m | | Status | running | | Inbox | .workspace/agents/pricing-researcher/inbox/ | | Log | .workspace/agents/pricing-researcher/output.log | Task delivered. Monitor with `/board` or send messages with `/inbox send pricing-researcher "..."`.
/budget — Budget allocation and tracking/inbox — Agent communication/heartbeat — Agent monitoring/checkout — Task locking (prevents double-assignment)| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | pass→pass | 5,335 | 4,893 | -8% | 1 | 1 | 0% | 981 | 2,015 | +105% | 0 | 0 | — |
case-02 | fail→pass | 90,238 | 34,540 | -62% | 1 | 1 | 0% | 3,282 | 2,261 | -31% | 0 | 0 | — |
case-03 | fail→pass | 11,747 | 6,586 | -44% | 1 | 1 | 0% | 2,339 | 2,294 | -2% | 0 | 0 | — |
case-04 | fail→pass | 28,727 | 3,393 | -88% | 1 | 1 | 0% | 2,930 | 1,630 | -44% | 0 | 0 | — |
case-05 | fail→pass | 24,920 | 3,330 | -87% | 1 | 1 | 0% | 1,695 | 1,287 | -24% | 0 | 0 | — |
case-06 | pass→pass | 13,583 | 3,614 | -73% | 1 | 1 | 0% | 1,869 | 1,506 | -19% | 0 | 0 | — |
case-11 | fail→pass | 16,878 | 2,653 | -84% | 1 | 1 | 0% | 2,937 | 1,406 | -52% | 0 | 0 | — |
case-07 | fail→pass | 24,647 | 4,370 | -82% | 1 | 1 | 0% | 2,000 | 1,683 | -16% | 0 | 0 | — |
case-08 | pass→pass | 11,686 | 2,589 | -78% | 1 | 1 | 0% | 1,926 | 1,474 | -23% | 0 | 0 | — |
case-09 | fail→pass | 16,491 | 2,202 | -87% | 1 | 1 | 0% | 2,290 | 1,391 | -39% | 0 | 0 | — |
case-10 | fail→pass | 16,084 | 2,224 | -86% | 1 | 1 | 0% | 2,777 | 1,363 | -51% | 0 | 0 | — |
case-12 | pass→pass | 9,303 | 3,076 | -67% | 1 | 1 | 0% | 1,356 | 1,578 | +16% | 0 | 0 | — |
case-13 | fail→pass | 44,448 | 3,364 | -92% | 1 | 1 | 0% | 2,011 | 1,518 | -25% | 0 | 0 | — |
case-14 | fail→pass | 17,220 | 5,717 | -67% | 1 | 1 | 0% | 2,744 | 2,018 | -26% | 0 | 0 | — |
case-15 | fail→pass | 12,228 | 1,875 | -85% | 1 | 1 | 0% | 1,989 | 1,343 | -32% | 0 | 0 | — |
case-16 | fail→pass | 7,060 | 2,221 | -69% | 1 | 1 | 0% | 1,086 | 1,404 | +29% | 0 | 0 | — |
case-17 | fail→pass | 23,037 | 31,236 | +36% | 1 | 1 | 0% | 2,019 | 1,692 | -16% | 0 | 0 | — |
case-18 | fail→pass | 15,699 | 7,603 | -52% | 1 | 1 | 0% | 2,638 | 2,218 | -16% | 0 | 0 | — |
case-19 | fail→pass | 10,669 | 7,191 | -33% | 1 | 1 | 0% | 1,664 | 2,273 | +37% | 0 | 0 | — |
case-20 | fail→pass | 11,795 | 3,191 | -73% | 1 | 1 | 0% | 1,659 | 1,457 | -12% | 0 | 0 | — |
case-21 | fail→pass | 12,154 | 2,592 | -79% | 1 | 1 | 0% | 1,990 | 1,400 | -30% | 0 | 0 | — |
case-22 | fail→pass | 15,133 | 14,303 | -5% | 1 | 1 | 0% | 2,352 | 3,081 | +31% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +82 percentage points is the difference between those two pass rates over the 22 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.