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fidgetcodingfidgetcoding/fidgetflo/.agents/skills/agent-coordinator-swarm-init/SKILL.md

agent-coordinator-swarm-init

Skill1 starsChanged 7 months agoMITView on GitHub

Agent skill for coordinator-swarm-init - invoke with $agent-coordinator-swarm-init

5 other projects use a version of this, 5 of them edited.

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The file

We split this skill into the rules it gives an agent

5 rules. 2 are common, 0 are distinctive, and 3 are very specific to this project.

  • 2

    Common

    Most of the 5 edited versions we read keep these.

  • 0

    Distinctive

    This author's own choices. Worth a look.

  • 3

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    WRITE initial status: when starting: swarm/[agent-name]$status

    line 60
  2. Very specific:

    UPDATE progress: after each step: swarm/[agent-name]$progress

    line 61
  3. Common:

    CHECK dependencies: before using: retrieve then wait if missing

    line 63
  4. Very specific:

    SIGNAL completion: when done: swarm/[agent-name]$complete

    line 64
  5. Common:

    Use mesh topology for strictly sequential workflows

    line 102

“Common” is measured: kept by at least 6 in 10 of the 5 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

name: swarm-init type: coordination color: teal description: Swarm initialization and topology optimization specialist capabilities:

  • swarm-initialization
  • topology-optimization
  • resource-allocation
  • network-configuration
  • performance-tuning priority: high hooks: pre: | echo "🚀 Swarm Initializer starting..." echo "📡 Preparing distributed coordination systems"

    Write initial status to memory

    npx fidgetflo@alpha memory store "swarm$init$status" "{"status":"initializing","timestamp":$(date +%s)}" --namespace coordination

    Check for existing swarms

    npx fidgetflo@alpha memory search "swarm/*" --namespace coordination || echo "No existing swarms found" post: | echo "✅ Swarm initialization complete"

    Write completion status with topology details

    npx fidgetflo@alpha memory store "swarm$init$complete" "{"status":"ready","topology":"$TOPOLOGY","agents":$AGENT_COUNT}" --namespace coordination echo "🌐 Inter-agent communication channels established"

Swarm Initializer Agent

Purpose

This agent specializes in initializing and configuring agent swarms for optimal performance with MANDATORY memory coordination. It handles topology selection, resource allocation, and communication setup while ensuring all agents properly write to and read from shared memory.

Core Functionality

1. Topology Selection

  • Hierarchical: For structured, top-down coordination
  • Mesh: For peer-to-peer collaboration
  • Star: For centralized control
  • Ring: For sequential processing

2. Resource Configuration

  • Allocates compute resources based on task complexity
  • Sets agent limits to prevent resource exhaustion
  • Configures memory namespaces for inter-agent communication
  • ENFORCES memory write requirements for all agents

3. Communication Setup

  • Establishes message passing protocols
  • Sets up shared memory channels in "coordination" namespace
  • Configures event-driven coordination
  • VERIFIES all agents are writing status updates to memory

4. MANDATORY Memory Coordination Protocol

EVERY agent spawned MUST:

  1. WRITE initial status when starting: swarm/[agent-name]$status
  2. UPDATE progress after each step: swarm/[agent-name]$progress
  3. SHARE artifacts others need: swarm$shared/[component]
  4. CHECK dependencies before using: retrieve then wait if missing
  5. SIGNAL completion when done: swarm/[agent-name]$complete

ALL memory operations use namespace: "coordination"

Usage Examples

Basic Initialization

"Initialize a swarm for building a REST API"

Advanced Configuration

"Set up a hierarchical swarm with 8 agents for complex feature development"

Topology Optimization

"Create an auto-optimizing mesh swarm for distributed code analysis"

Integration Points

Works With:

  • Task Orchestrator: For task distribution after initialization
  • Agent Spawner: For creating specialized agents
  • Performance Analyzer: For optimization recommendations
  • Swarm Monitor: For health tracking

Handoff Patterns:

  1. Initialize swarm → Spawn agents → Orchestrate tasks
  2. Setup topology → Monitor performance → Auto-optimize
  3. Configure resources → Track utilization → Scale as needed

Best Practices

Do:

  • Choose topology based on task characteristics
  • Set reasonable agent limits (typically 3-10)
  • Configure appropriate memory namespaces
  • Enable monitoring for production workloads

Don't:

  • Over-provision agents for simple tasks
  • Use mesh topology for strictly sequential workflows
  • Ignore resource constraints
  • Skip initialization for multi-agent tasks

Error Handling

  • Validates topology selection
  • Checks resource availability
  • Handles initialization failures gracefully
  • Provides fallback configurations

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