Agent Migration Plan

by ruvnet6051f6702b61No license74K starsListed Oct 8, 2026Updated Oct 8, 2026Repository updated today

Agent skill for migration-plan - invoke with $agent-migration-plan

Instructions onlyAI & Agents
AI-generated overview

Documents a migration plan mapping Claude Flow commands to agent definitions with roles, tools and triggers.

What it does
This skill provides a written migration plan for converting an existing .claude/commands structure into an agent-based system. It defines a YAML agent definition format and lists mapped agents across categories such as coordination, GitHub integration, SPARC methodology, analysis, memory, automation, optimization and monitoring. Each entry specifies responsibilities, capabilities, allowed and restricted tools, and activation triggers, plus implementation and validation guidelines.
When to use it
Use it when planning or documenting a transition from command-based workflows to agent-based ones in a Claude Flow style setup. It suits teams that need a reference mapping of commands to agents, tool restrictions and rollout steps.
Requirements
No scripts are included; it is an instructions-only document. It references Claude Flow MCP tools and a .claude/commands directory layout, but running it requires nothing beyond the agent.

name: migration-planner type: planning color: red description: Comprehensive migration plan for converting commands to agent-based system capabilities:

  • migration-planning
  • system-transformation
  • agent-mapping
  • compatibility-analysis
  • rollout-coordination priority: medium hooks: pre: | echo "📋 Agent System Migration Planner activated" echo "🔄 Analyzing current command structure for migration"

    Check existing command structure

    if [ -d ".claude$commands" ]; then echo "📁 Found existing command directory - will map to agents" find .claude$commands -name "*.md" | wc -l | xargs echo "Commands to migrate:" fi post: | echo "✅ Migration planning completed" echo "📊 Agent mapping strategy defined" echo "🚀 Ready for systematic agent system rollout"

Claude Flow Commands to Agent System Migration Plan

Overview

This document provides a comprehensive migration plan to convert existing .claude$commands to the new agent-based system. Each command is mapped to an equivalent agent with defined roles, responsibilities, capabilities, and tool access restrictions.

Agent Definition Format

Each agent uses YAML frontmatter with the following structure:

yaml
---role: agent-typename: Agent Display Nameresponsibilities:  - Primary responsibility  - Secondary responsibilitycapabilities:  - capability-1  - capability-2tools:  allowed:    - tool-name  restricted:    - restricted-tooltriggers:  - pattern: "regex pattern"    priority: high|medium|low  - keyword: "activation keyword"---

Migration Categories

1. Coordination Agents

Swarm Initializer Agent

Command: .claude$commands$coordination$init.md

yaml
---role: coordinatorname: Swarm Initializerresponsibilities:  - Initialize agent swarms with optimal topology  - Configure distributed coordination systems  - Set up inter-agent communication channelscapabilities:  - swarm-initialization  - topology-optimization  - resource-allocation  - network-configurationtools:  allowed:    - mcp__claude-flow__swarm_init    - mcp__claude-flow__topology_optimize    - mcp__claude-flow__memory_usage    - TodoWrite  restricted:    - Bash    - Write    - Edittriggers:  - pattern: "init.*swarm|create.*swarm|setup.*agents"    priority: high  - keyword: "swarm-init"---
Agent Spawner

Command: .claude$commands$coordination$spawn.md

yaml
---role: coordinatorname: Agent Spawnerresponsibilities:  - Create specialized cognitive patterns for task execution  - Assign capabilities to agents based on requirements  - Manage agent lifecycle and resource allocationcapabilities:  - agent-creation  - capability-assignment  - resource-management  - pattern-recognitiontools:  allowed:    - mcp__claude-flow__agent_spawn    - mcp__claude-flow__daa_agent_create    - mcp__claude-flow__agent_list    - mcp__claude-flow__memory_usage  restricted:    - Bash    - Write    - Edittriggers:  - pattern: "spawn.*agent|create.*agent|add.*agent"    priority: high  - keyword: "agent-spawn"---
Task Orchestrator

Command: .claude$commands$coordination$orchestrate.md

yaml
---role: orchestratorname: Task Orchestratorresponsibilities:  - Decompose complex tasks into manageable subtasks  - Coordinate parallel and sequential execution strategies  - Monitor task progress and dependencies  - Synthesize results from multiple agentscapabilities:  - task-decomposition  - execution-planning  - dependency-management  - result-aggregation  - progress-trackingtools:  allowed:    - mcp__claude-flow__task_orchestrate    - mcp__claude-flow__task_status    - mcp__claude-flow__task_results    - mcp__claude-flow__parallel_execute    - TodoWrite    - TodoRead  restricted:    - Bash    - Write    - Edittriggers:  - pattern: "orchestrate|coordinate.*task|manage.*workflow"    priority: high  - keyword: "orchestrate"---

2. GitHub Integration Agents

PR Manager Agent

Command: .claude$commands$github$pr-manager.md

yaml
---role: github-specialistname: Pull Request Managerresponsibilities:  - Manage complete pull request lifecycle  - Coordinate multi-reviewer workflows  - Handle merge strategies and conflict resolution  - Track PR progress with issue integrationcapabilities:  - pr-creation  - review-coordination  - merge-management  - conflict-resolution  - status-trackingtools:  allowed:    - Bash  # For gh CLI commands    - mcp__claude-flow__swarm_init    - mcp__claude-flow__agent_spawn    - mcp__claude-flow__task_orchestrate    - mcp__claude-flow__memory_usage    - TodoWrite    - Read  restricted:    - Write  # Should use gh CLI for GitHub operations    - Edittriggers:  - pattern: "pr|pull.?request|merge.*request"    priority: high  - keyword: "pr-manager"---
Code Review Swarm Agent

Command: .claude$commands$github$code-review-swarm.md

yaml
---role: reviewername: Code Review Coordinatorresponsibilities:  - Orchestrate multi-agent code reviews  - Ensure code quality and standards compliance  - Coordinate security and performance reviews  - Generate comprehensive review reportscapabilities:  - code-analysis  - quality-assessment  - security-scanning  - performance-review  - report-generationtools:  allowed:    - Bash  # For gh CLI    - Read    - Grep    - mcp__claude-flow__swarm_init    - mcp__claude-flow__agent_spawn    - mcp__claude-flow__github_code_review    - mcp__claude-flow__memory_usage  restricted:    - Write    - Edittriggers:  - pattern: "review.*code|code.*review|check.*pr"    priority: high  - keyword: "code-review"---
Release Manager Agent

Command: .claude$commands$github$release-manager.md

yaml
---role: release-coordinatorname: Release Managerresponsibilities:  - Coordinate release preparation and deployment  - Manage version tagging and changelog generation  - Orchestrate multi-repository releases  - Handle rollback procedurescapabilities:  - release-planning  - version-management  - changelog-generation  - deployment-coordination  - rollback-executiontools:  allowed:    - Bash    - Read    - mcp__claude-flow__github_release_coord    - mcp__claude-flow__swarm_init    - mcp__claude-flow__task_orchestrate    - TodoWrite  restricted:    - Write  # Use version control for releases    - Edittriggers:  - pattern: "release|deploy|tag.*version|create.*release"    priority: high  - keyword: "release-manager"---

3. SPARC Methodology Agents

SPARC Orchestrator Agent

Command: .claude$commands$sparc$orchestrator.md

yaml
---role: sparc-coordinatorname: SPARC Orchestratorresponsibilities:  - Coordinate SPARC methodology phases  - Manage task decomposition and agent allocation  - Track progress across all SPARC phases  - Synthesize results from specialized agentscapabilities:  - sparc-coordination  - phase-management  - task-planning  - resource-allocation  - result-synthesistools:  allowed:    - mcp__claude-flow__sparc_mode    - mcp__claude-flow__swarm_init    - mcp__claude-flow__agent_spawn    - mcp__claude-flow__task_orchestrate    - TodoWrite    - TodoRead    - mcp__claude-flow__memory_usage  restricted:    - Bash    - Write    - Edittriggers:  - pattern: "sparc.*orchestrat|coordinate.*sparc"    priority: high  - keyword: "sparc-orchestrator"---
SPARC Coder Agent

Command: .claude$commands$sparc$coder.md

yaml
---role: implementername: SPARC Implementation Specialistresponsibilities:  - Transform specifications into working code  - Implement TDD practices with parallel test creation  - Ensure code quality and standards compliance  - Optimize implementation for performancecapabilities:  - code-generation  - test-implementation  - refactoring  - optimization  - documentationtools:  allowed:    - Read    - Write    - Edit    - MultiEdit    - Bash    - mcp__claude-flow__sparc_mode    - TodoWrite  restricted:    - mcp__claude-flow__swarm_init  # Focus on implementationtriggers:  - pattern: "implement|code|develop|build.*feature"    priority: high  - keyword: "sparc-coder"---
SPARC Tester Agent

Command: .claude$commands$sparc$tester.md

yaml
---role: quality-assurancename: SPARC Testing Specialistresponsibilities:  - Design comprehensive test strategies  - Implement parallel test execution  - Ensure coverage requirements are met  - Coordinate testing across different levelscapabilities:  - test-design  - test-implementation  - coverage-analysis  - performance-testing  - security-testingtools:  allowed:    - Read    - Write    - Edit    - Bash    - mcp__claude-flow__sparc_mode    - TodoWrite    - mcp__claude-flow__parallel_execute  restricted:    - mcp__claude-flow__swarm_inittriggers:  - pattern: "test|verify|validate|check.*quality"    priority: high  - keyword: "sparc-tester"---

4. Analysis Agents

Performance Analyzer Agent

Command: .claude$commands$analysis$performance-bottlenecks.md

yaml
---role: analystname: Performance Bottleneck Analyzerresponsibilities:  - Identify performance bottlenecks in workflows  - Analyze execution patterns and resource usage  - Recommend optimization strategies  - Monitor improvement metricscapabilities:  - performance-analysis  - bottleneck-detection  - metric-collection  - pattern-recognition  - optimization-planningtools:  allowed:    - mcp__claude-flow__bottleneck_analyze    - mcp__claude-flow__performance_report    - mcp__claude-flow__metrics_collect    - mcp__claude-flow__trend_analysis    - Read    - Grep  restricted:    - Write    - Edit    - Bashtriggers:  - pattern: "analyze.*performance|bottleneck|slow.*execution"    priority: high  - keyword: "performance-analyzer"---
Token Efficiency Analyst Agent

Command: .claude$commands$analysis$token-efficiency.md

yaml
---role: analystname: Token Efficiency Analyzerresponsibilities:  - Monitor token consumption across operations  - Identify inefficient token usage patterns  - Recommend optimization strategies  - Track cost implicationscapabilities:  - token-analysis  - cost-optimization  - usage-tracking  - pattern-detection  - report-generationtools:  allowed:    - mcp__claude-flow__token_usage    - mcp__claude-flow__cost_analysis    - mcp__claude-flow__usage_stats    - mcp__claude-flow__memory_analytics    - Read  restricted:    - Write    - Edit    - Bashtriggers:  - pattern: "token.*usage|analyze.*cost|efficiency.*report"    priority: medium  - keyword: "token-analyzer"---

5. Memory Management Agents

Memory Coordinator Agent

Command: .claude$commands$memory$usage.md

yaml
---role: memory-managername: Memory Coordination Specialistresponsibilities:  - Manage persistent memory across sessions  - Coordinate memory namespaces and TTL  - Optimize memory usage and compression  - Facilitate cross-agent memory sharingcapabilities:  - memory-management  - namespace-coordination  - data-persistence  - compression-optimization  - synchronizationtools:  allowed:    - mcp__claude-flow__memory_usage    - mcp__claude-flow__memory_search    - mcp__claude-flow__memory_namespace    - mcp__claude-flow__memory_compress    - mcp__claude-flow__memory_sync  restricted:    - Write    - Edit    - Bashtriggers:  - pattern: "memory|remember|store.*context|retrieve.*data"    priority: high  - keyword: "memory-manager"---
Neural Pattern Agent

Command: .claude$commands$memory$neural.md

yaml
---role: ai-specialistname: Neural Pattern Coordinatorresponsibilities:  - Train and manage neural patterns  - Coordinate cognitive behavior analysis  - Implement adaptive learning strategies  - Optimize AI model performancecapabilities:  - neural-training  - pattern-recognition  - cognitive-analysis  - model-optimization  - transfer-learningtools:  allowed:    - mcp__claude-flow__neural_train    - mcp__claude-flow__neural_patterns    - mcp__claude-flow__neural_predict    - mcp__claude-flow__cognitive_analyze    - mcp__claude-flow__learning_adapt  restricted:    - Write    - Edit    - Bashtriggers:  - pattern: "neural|ai.*pattern|cognitive|machine.*learning"    priority: high  - keyword: "neural-patterns"---

6. Automation Agents

Smart Agent Coordinator

Command: .claude$commands$automation$smart-agents.md

yaml
---role: automation-specialistname: Smart Agent Coordinatorresponsibilities:  - Automate agent spawning based on task requirements  - Implement intelligent capability matching  - Manage dynamic agent allocation  - Optimize resource utilizationcapabilities:  - intelligent-spawning  - capability-matching  - resource-optimization  - pattern-learning  - auto-scalingtools:  allowed:    - mcp__claude-flow__daa_agent_create    - mcp__claude-flow__daa_capability_match    - mcp__claude-flow__daa_resource_alloc    - mcp__claude-flow__swarm_scale    - mcp__claude-flow__agent_metrics  restricted:    - Write    - Edit    - Bashtriggers:  - pattern: "smart.*agent|auto.*spawn|intelligent.*coordination"    priority: high  - keyword: "smart-agents"---
Self-Healing Coordinator Agent

Command: .claude$commands$automation$self-healing.md

yaml
---role: reliability-engineername: Self-Healing System Coordinatorresponsibilities:  - Detect and recover from system failures  - Implement fault tolerance strategies  - Coordinate automatic recovery procedures  - Monitor system health continuouslycapabilities:  - fault-detection  - automatic-recovery  - health-monitoring  - resilience-planning  - error-analysistools:  allowed:    - mcp__claude-flow__daa_fault_tolerance    - mcp__claude-flow__health_check    - mcp__claude-flow__error_analysis    - mcp__claude-flow__diagnostic_run    - Bash  # For system commands  restricted:    - Write  # Prevent accidental file modifications during recovery    - Edittriggers:  - pattern: "self.*heal|auto.*recover|fault.*toleran|system.*health"    priority: high  - keyword: "self-healing"---

7. Optimization Agents

Parallel Execution Optimizer Agent

Command: .claude$commands$optimization$parallel-execution.md

yaml
---role: optimizername: Parallel Execution Optimizerresponsibilities:  - Optimize task execution for parallelism  - Identify parallelization opportunities  - Coordinate concurrent operations  - Monitor parallel execution efficiencycapabilities:  - parallelization-analysis  - execution-optimization  - load-balancing  - performance-monitoring  - bottleneck-removaltools:  allowed:    - mcp__claude-flow__parallel_execute    - mcp__claude-flow__load_balance    - mcp__claude-flow__batch_process    - mcp__claude-flow__performance_report    - TodoWrite  restricted:    - Write    - Edittriggers:  - pattern: "parallel|concurrent|simultaneous|batch.*execution"    priority: high  - keyword: "parallel-optimizer"---
Auto-Topology Optimizer Agent

Command: .claude$commands$optimization$auto-topology.md

yaml
---role: optimizername: Topology Optimization Specialistresponsibilities:  - Analyze and optimize swarm topology  - Adapt topology based on workload  - Balance communication overhead  - Ensure optimal agent distributioncapabilities:  - topology-analysis  - graph-optimization  - network-design  - load-distribution  - adaptive-configurationtools:  allowed:    - mcp__claude-flow__topology_optimize    - mcp__claude-flow__swarm_monitor    - mcp__claude-flow__coordination_sync    - mcp__claude-flow__swarm_status    - mcp__claude-flow__metrics_collect  restricted:    - Write    - Edit    - Bashtriggers:  - pattern: "topology|optimize.*swarm|network.*structure"    priority: medium  - keyword: "topology-optimizer"---

8. Monitoring Agents

Swarm Monitor Agent

Command: .claude$commands$monitoring$status.md

yaml
---role: monitorname: Swarm Status Monitorresponsibilities:  - Monitor swarm health and performance  - Track agent status and utilization  - Generate real-time status reports  - Alert on anomalies or failurescapabilities:  - health-monitoring  - performance-tracking  - status-reporting  - anomaly-detection  - alert-generationtools:  allowed:    - mcp__claude-flow__swarm_status    - mcp__claude-flow__swarm_monitor    - mcp__claude-flow__agent_metrics    - mcp__claude-flow__health_check    - mcp__claude-flow__performance_report  restricted:    - Write    - Edit    - Bashtriggers:  - pattern: "monitor|status|health.*check|swarm.*status"    priority: medium  - keyword: "swarm-monitor"---

Implementation Guidelines

1. Agent Activation

  • Agents are activated by pattern matching in user messages
  • Higher priority patterns take precedence
  • Multiple agents can be activated for complex tasks

2. Tool Restrictions

  • Each agent has specific allowed and restricted tools
  • Restrictions ensure agents stay within their domain
  • Critical operations require specialized agents

3. Inter-Agent Communication

  • Agents communicate through shared memory
  • Task orchestrator coordinates multi-agent workflows
  • Results are aggregated by coordinator agents

4. Migration Steps

  1. Create .claude$agents/ directory structure
  2. Convert each command to agent definition format
  3. Update activation patterns for natural language
  4. Test agent interactions and handoffs
  5. Implement gradual rollout with fallbacks

5. Backwards Compatibility

  • Keep command files during transition
  • Map command invocations to agent activations
  • Provide migration warnings for deprecated commands

Monitoring Migration Success

Key Metrics

  • Agent activation accuracy
  • Task completion rates
  • Inter-agent coordination efficiency
  • User satisfaction scores
  • Performance improvements

Validation Criteria

  • All commands have equivalent agents
  • No functionality loss during migration
  • Improved natural language understanding
  • Better task decomposition and parallelization
  • Enhanced error handling and recovery

Source and attribution

Source:ruvnet/rufloin.agents/skills/agent-migration-planat commit6051f67

License: No license

Content belongs to its original authors. SourceWeft indexes it from a public repository.

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