ELF Swarm Coordination Command

Orchestrate multi-agent workflows for complex tasks requiring parallel processing or multiple specialized perspectives.

Purpose

The /swarm command enables:

• Parallel processing - Multiple agents working simultaneously
• Specialized perspectives - Researcher, Architect, Creative, Skeptic agents
• Dependency management - Sequential processing when needed
• Result aggregation - Combine outputs from multiple agents
• Sophisticated workflows - Complex orchestration patterns

Usage Examples

/swarm analyze my architecture from 4 perspectives
/swarm run parallel searches on [topics]
/swarm investigate this failure through agent lenses
/swarm parallelize this migration task

Key Agent Perspectives

Researcher

• Asks: "What does the evidence say?"
• Strength: Finds authoritative knowledge
• Use: For data-driven decisions

Architect

• Asks: "How does this scale?"
• Strength: Systems thinking
• Use: For structural decisions

Creative

• Asks: "What if we tried something different?"
• Strength: Novel solutions
• Use: When stuck on problems

Skeptic

• Asks: "What could go wrong?"
• Strength: Finds edge cases
• Use: For validation

How Swarm Orchestration Works

When you invoke /swarm:

1. Parse your request - Understand task and constraints
2. Plan execution - Determine parallel vs sequential
3. Launch agents - Spawn subagents in background
4. Manage dependencies - Block only when needed
5. Aggregate results - Combine perspectives
6. Synthesize insights - Extract unified understanding

Swarm Patterns

Parallel Analysis

Launch all 4 agents simultaneously on the same problem. Best for: Complex decisions, design reviews, failure analysis

Sequential Pipeline

Run agents in sequence where each builds on previous. Best for: Iterative refinement, progressive investigation

Expert Consultation

Launch specific agents for their expertise. Best for: Targeted investigation

Parallel + Synthesis

Run multiple agents in parallel, then synthesize results. Best for: Comprehensive analysis

Agent Coordination Rules

• Always run in background - run_in_background=True
• Block only when needed - Use TaskOutput to wait for results
• Specify agent type - Researcher, Architect, Creative, Skeptic
• Use models efficiently - Haiku for small tasks, Sonnet/Opus for complex
• Aggregate thoughtfully - Synthesize perspectives, don't just list them

Integration with ELF

Swarm results can feed back into the building:

• Document learnings - Record what agents discovered
• Update heuristics - If swarm validates/challenges existing knowledge
• Propose rules - If discovery is universal enough
• Escalate decisions - If swarm surfaces ambiguity

Example Workflow

1. User: "/swarm analyze my architecture from 4 perspectives"
2. System: Launches 4 agents in parallel
   - Researcher: Evidence-based evaluation
   - Architect: Structural analysis
   - Creative: Alternative approaches
   - Skeptic: Risk identification
3. System: Aggregates results into synthesis
4. User: Gets comprehensive perspective
5. Building: Results documented if significant

When to Use Swarm

• Complex decisions - Need multiple viewpoints
• Ambitious goals - Parallel processing helps
• Risk management - Skeptic finds what you missed
• Stuck problems - Creative breaks conventional thinking
• Learning opportunities - Results feed building's knowledge