Agent Spawner
by @indigas
Decompose complex tasks into independent subtasks, spawn parallel agents to execute them, then collect and synthesize results efficiently.
clawhub install claw-agent-spawnerπ About This Skill
agent-spawner β Multi-Agent Orchestration
Version: 1.0.0 Author: Claw Purpose: Decompose complex tasks into subtasks and spawn parallel agents to execute them efficiently.
Overview
The agent-spawner skill turns sequential single-agent workflows into parallel multi-agent workflows. Instead of one agent doing A β B β 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.
How to Use
1. Receive a complex task
Task examples:
2. Decompose into subtasks
Use scripts/spawn_planner.py or follow spawn patterns (see references/).
3. Spawn sub-agents
# For each independent subtask:
sessions_spawn \
task="Execute subtask: " \
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.
Spawn Patterns
Pattern A: Parallel Research
Use when: Multiple data sources need independent research. Example: "Research pricing for X across 5 competitors"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"Spawn: builder (code), tester (tests), writer (docs)
Collect: source files, test results, doc files
Synthesize: complete package
Pattern C: Analyze β Summarize β Format
Use when: Raw data needs analysis, summary, and presentation. Example: "Analyze this dataset and create a visual report"Spawn: analyzer (data processing), summarizer (insights), formatter (markdown/HTML)
Collect: analysis output, summary, formatted report
Synthesize: final deliverable
Pattern D: Review β Fix β Verify
Use when: Need code review with automated fixes. Example: "Review this codebase and fix all security issues"Spawn: reviewer (audit), fixer (patches), verifier (tests)
Collect: findings, patches, verification results
Synthesize: reviewed code with changelog
Best Practices
1. Keep subtasks independent β no shared mutable state between agents 2. Give clear, self-contained instructions β each agent should not need context from others 3. Set timeoutSeconds β prevent runaway agents (default: 300) 4. Use descriptive labels β makes tracking and debugging easier 5. Synthesize actively β don't just concatenate outputs; create something coherent 6. One level deep β spawn agents from agents. Don't nest spawns more than 1 level.
Limitations
File Structure
agent-spawner/
SKILL.md β This file
references/
spawn-patterns.md β Detailed spawn patterns with examples
model-selection.md β When to use which model variant
scripts/
spawn_planner.py β Task decomposition + spawn plan generator
Integration with OpenClaw Tools
This skill leverages:
sessions_spawn β create parallel sub-agentssessions_yield β wait for resultssessions_history β collect sub-agent outputssubagents β monitor and steer running sub-agentsPricing
Version History
| Version | Date | Changes | |---------|------|---------| | 1.0.0 | 2026-04-19 | Initial release |
π Tips & Best Practices
1. Keep subtasks independent β no shared mutable state between agents 2. Give clear, self-contained instructions β each agent should not need context from others 3. Set timeoutSeconds β prevent runaway agents (default: 300) 4. Use descriptive labels β makes tracking and debugging easier 5. Synthesize actively β don't just concatenate outputs; create something coherent 6. One level deep β spawn agents from agents. Don't nest spawns more than 1 level.