agent-spawner — Multi-Agent Orchestration#

Version: 1.0.0

Author: Claw

Purpose: Decompose complex tasks into subtasks and spawn parallel agents to execute them efficiently.

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Overview#

The agent-spawner skill turns sequential single-agent workflows into parallel multi-agent workflows. Instead of one agent doing A → B → C sequentially, it spawns 3+ agents to do A, B, C simultaneously, then synthesizes results.

Efficiency gain: 2-4x faster execution for multi-part tasks.

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How to Use#

1. Receive a complex task#

Task examples:

• "Research the AI automation market in Czech Republic"
• "Compare these 5 projects: X, Y, Z, A, B"
• "Build a report on solar panel ROI for residential use"

2. Decompose into subtasks#

Use scripts/spawn_planner.py or follow spawn patterns (see references/).

3. Spawn sub-agents#

bash

# For each independent subtask:
sessions_spawn \
  task="Execute subtask: <description>" \
  label="subtask-1" \
  mode="run" \
  runtime="subagent"

4. Yield and collect#

Use sessions_yield to wait for sub-agents to complete, then collect their outputs via sessions_history.

5. Synthesize results#

Combine sub-agent outputs into a coherent final deliverable. Resolve conflicts, merge findings, add context only you possess.

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Spawn Patterns#

Pattern A: Parallel Research#

Use when: Multiple data sources need independent research. Example: "Research pricing for X across 5 competitors"

text

Spawn: competitor-A-price, competitor-B-price, competitor-C-price...
Collect: price data from each
Synthesize: comparison table

Pattern B: Build + Test + Document#

Use when: Need code, tests, and docs simultaneously. Example: "Build a Python CLI tool with tests and documentation"

text

Spawn: builder (code), tester (tests), writer (docs)
Collect: source files, test results, doc files
Synthesize: complete package

Pattern C: Analyze → Summarize → Format#

Use when: Raw data needs analysis, summary, and presentation. Example: "Analyze this dataset and create a visual report"

text

Spawn: analyzer (data processing), summarizer (insights), formatter (markdown/HTML)
Collect: analysis output, summary, formatted report
Synthesize: final deliverable

Pattern D: Review → Fix → Verify#

Use when: Need code review with automated fixes. Example: "Review this codebase and fix all security issues"

text

Spawn: reviewer (audit), fixer (patches), verifier (tests)
Collect: findings, patches, verification results
Synthesize: reviewed code with changelog

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Best Practices#

1. Keep subtasks independent — no shared mutable state between agents
2. Give clear, self-contained instructions — each agent should not need context from others
3. Set timeoutSeconds — prevent runaway agents (default: 300)
4. Use descriptive labels — makes tracking and debugging easier
5. Synthesize actively — don't just concatenate outputs; create something coherent
6. One level deep — spawn agents from agents. Don't nest spawns more than 1 level.

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Limitations#

• Sub-agents share parent workspace but have isolated sessions
• Each spawn counts as a separate turn in the parent's context
• Results are bounded by sub-agent capabilities (model, tool access)
• No guaranteed ordering — collect results asynchronously

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File Structure#

text

agent-spawner/
  SKILL.md                    — This file
  references/
    spawn-patterns.md         — Detailed spawn patterns with examples
    model-selection.md        — When to use which model variant
  scripts/
    spawn_planner.py          — Task decomposition + spawn plan generator

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Integration with OpenClaw Tools#

This skill leverages:

• sessions_spawn — create parallel sub-agents
• sessions_yield — wait for results
• sessions_history — collect sub-agent outputs
• subagents — monitor and steer running sub-agents

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Pricing#

• Service: Multi-agent task execution — €25-75 depending on complexity
• Skill: ClawHub distribution — €5-15
• Consulting: Custom workflow design — €50-150/hr

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Version History#

VersionDateChanges1.0.02026-04-19Initial release