agent-coordinator-swarm-init
- Repo stars 54,444
- Author updated Live
- Author repo ruflo
- Domain
- AI
- Compatible agents
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- Trust score
- 88 / 100 · community maintained
- Author / version / license
- @ruvnet · no license declared
- Token usage
- Lean
- Setup complexity
- Guided setup
- External API key
- Not required
- Operating systems
- macOS · Linux · Windows
- Runtime requirements
- No special requirements
- Permissions
-
- Read-only
- Write / modify
- Shell exec
- Network behavior
- Local-only
- Install commands
- 26 variants
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools,默认拥有全部工具权限。
---
name: agent-coordinator-swarm-init
description: Agent skill for coordinator-swarm-init - invoke with $agent-coordinator-swarm-init name: swarm-i…
category: ai
runtime: no special runtime
---
# agent-coordinator-swarm-init output preview
## PART A: Task fit
- Use case: Agent skill for coordinator-swarm-init - invoke with $agent-coordinator-swarm-init name: swarm-init type: coordination description: Swarm initialization and topology optimization specialist echo "🚀 Swarm Initializer starting..." runs entirely locally. Works with Claude Code, Cursor, Cline and 23 more..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Purpose / Core Functionality / 1. Topology Selection” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “Agent skill for coordinator-swarm-init - invoke with $agent-coordinator-swarm-init name: swarm-init type: coordination description: Swarm initialization and topology optimization specialist echo "🚀 Swarm Initializer starting..." runs entirely locally. Works with Claude Code, Cursor, Cline and 23 more.”.
- **02** When the source has headings, the agent prioritizes “Purpose / Core Functionality / 1. Topology Selection” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
## Running Rules
- read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options. The source does not require a stable slash command. After installation, invoke the skill by name and describe the task.
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files, run shell commands.
Start with a small task and check whether the result follows “Purpose / Core Functionality / 1. Topology Selection”. Inspect diffs, logs, previews, or tests before expanding scope.
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: agent-coordinator-swarm-init
description: Agent skill for coordinator-swarm-init - invoke with $agent-coordinator-swarm-init name: swarm-i…
category: ai
source: ruvnet/ruflo
---
# agent-coordinator-swarm-init
## When to use
- Agent skill for coordinator-swarm-init - invoke with $agent-coordinator-swarm-init name: swarm-init type: coordination…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “Purpose / Core Functionality / 1. Topology Selection” and keep inference separate from source facts.
- read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies. The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "agent-coordinator-swarm-init" {
input -> user goal + target files + boundaries + acceptance criteria
context -> Purpose / Core Functionality / 1. Topology Selection
rules -> SKILL.md triggers / order / output contract
runtime -> no special runtime | read files, write/modify files, run shell commands | mostly runs locally
guardrails -> usually needs no extra API key + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
} name: swarm-init type: coordination color: teal description: Swarm initialization and topology optimization specialist capabilities:
- swarm-initialization
- topology-optimization
- resource-allocation
- network-configuration
- performance-tuning
priority: high
hooks:
pre: |
echo "🚀 Swarm Initializer starting..."
echo "📡 Preparing distributed coordination systems"
Write initial status to memory
npx claude-flow@alpha memory store "swarm$init$status" "{"status":"initializing","timestamp":$(date +%s)}" --namespace coordinationCheck for existing swarms
npx claude-flow@alpha memory search "swarm/*" --namespace coordination || echo "No existing swarms found" post: | echo "✅ Swarm initialization complete"Write completion status with topology details
npx claude-flow@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:
- WRITE initial status when starting:
swarm/[agent-name]$status - UPDATE progress after each step:
swarm/[agent-name]$progress - SHARE artifacts others need:
swarm$shared/[component] - CHECK dependencies before using: retrieve then wait if missing
- SIGNAL completion when done:
swarm/[agent-name]$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:
- Initialize swarm → Spawn agents → Orchestrate tasks
- Setup topology → Monitor performance → Auto-optimize
- 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
Decide Fit First
Design Intent
How To Use It
Boundaries And Review