Swarm Execution Skill

This skill orchestrates parallel agent workflows for complex, multi-step tasks. It decomposes work into dependency-mapped waves and executes them with autonomous agents while maintaining quality through automated auditing.

When to Use

Invoke this skill when:

• User runs /swarm command
• Task requires decomposition into multiple parallel sub-tasks
• User mentions "swarm", "parallel execution", "wave-based", or "multi-agent"
• Complex refactoring or migration with many interdependent steps

Roles

The swarm system has five agent roles:

Role	Purpose	When Spawned
Architect	Plan generation, task decomposition, dependency mapping	Start of swarm, re-planning phases
Worker	Execute individual tasks, write reports to files	During wave execution
Reviewer (ultrathink)	MANDATORY wave review, reads ALL reports	End of EVERY wave
Reviewer (opus)	Optional task-scoped review	Complex tasks (optional)
Fixer	Auto-fix HIGH priority issues	When review returns HIGH

Worker Agents

Worker agents are defined in ./agents/ and run in parallel with separate context windows:

Agent	Model	Purpose	Use For
swarm-worker-haiku	haiku	Fast, cheap tasks	Deletions, renames, config, boilerplate
swarm-worker-opus	opus	Standard tasks	Implement, integrate, fix bugs
swarm-worker-ultrathink	opus	Complex tasks	Architecture, algorithms, edge cases

Review Agents

Agent	Model	Purpose	When Used
swarm-reviewer-ultrathink	opus	Wave review (ALL tasks)	MANDATORY end of every wave
swarm-reviewer-opus	opus	Task review (single task)	Optional for complex tasks

Review Output: {priority}|{action}|{review-path} where priority is HIGH/MED/LOW.

Spawning Pattern (Fire-and-Forget)

All agents MUST be spawned with run_in_background: true:

# Workers - include report path in prompt
Task(subagent_type="swarm-worker-haiku",
     prompt="Task 1.1: ... Report: .swarm/reports/plan/wave-1/task-1.1.md",
     run_in_background=true)
→ Returns: agentId (store mapping taskId→agentId)

# DO NOT POLL - wait at end of wave only
TaskOutput(task_id=lastAgentId, block=true)
# Ignore output content - workers wrote to files

ZERO-POLLING: Orchestrator never reads worker output. Communication via files only.

CRITICAL: Orchestrator Context Isolation

The orchestrator (you) NEVER uses Explore or general-purpose agents for assessment.

Why? Because agent output returns to your context, defeating the purpose. Instead:

Need	WRONG	CORRECT
Get TypeScript errors	Task(Explore, "run tsc")	Bash("npx tsc --noEmit")
Read a file	Task(Explore, "read X")	Read("X")
Find files	Task(Explore, "find...")	Glob("pattern")
Search code	Task(Explore, "grep...")	Grep("pattern")

Workers are for IMPLEMENTATION, not exploration. You explore directly, then spawn workers to implement in parallel.

Orchestrator Responsibilities

The main Claude (you) acts as orchestrator:

• Delegates planning to Architect agent
• Launches Worker agents in parallel within waves
• Coordinates Auditor agents after each wave
• Manages the master plan file (sole writer)
• Handles user checkpoints and confirmations
• Decides on escalation and retries

Reference Files

Load these based on current phase:

Phase	Load Reference
Planning	architect-protocol.md
Execution	worker-protocol.md, escalation-rules.md
Validation	auditor-protocol.md
All phases	orchestrator-protocol.md, plan-format.md

Quick Start

1. Receive task (description, file path, or "resume")
2. Spawn Architect (run_in_background: true) → wait with TaskOutput(block: true)
3. Confirm with user (checkpoints, model assignments)
4. Execute waves (ZERO-POLLING):
   • SPAWN all workers with report paths in prompts
   • Store taskId→agentId mapping (NOT output)
   • WAIT at end of wave only: TaskOutput(lastAgentId, block: true)
   • Ignore output content - workers wrote to files
5. MANDATORY wave review: spawn swarm-reviewer-ultrathink
   • Returns: {priority}|{action}|{review-path}
   • HIGH → spawn fixers, wait, re-review
   • MED → note in plan, continue
   • LOW → proceed
6. Optional task review: spawn swarm-reviewer-opus for complex tasks
7. Checkpoint with user at agreed points
8. Update plan with file paths only (not content)
9. Repeat until complete

Plan File Location

Plans are stored in .swarm/{task-slug}.md in project root.
This file is both the plan definition AND execution state (checkboxes).

Mode Behavior

Mode	Behavior
Plan mode	Generate plan only, no execution
Auto-edit	Execute with minimal pauses (major milestones only)
Default	Wave-by-wave checkpoints

Future: GitHub Actions

Design notes for CI/CD migration:

• Tasks are stateless with explicit inputs/outputs
• Plan format supports workflow generation
• Checkpoints map to approval gates