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Agent Migration Plan

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ruvnet
agent-migration-plan

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

Overview

Publisherruvnet
Repositoryruflo
Skill nameagent-migration-plan
Stars
73.6K
Forks
8.7K
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by ruvnet on GitHub. Read the source before you install it.

Installation

Install the Agent Migration Plan AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/ruvnet/ruflo/tree/main/.agents/skills/agent-migration-plan
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/ruvnet/ruflo.git /tmp/ruflo
mkdir -p .claude/skills
cp -r /tmp/ruflo/.agents/skills/agent-migration-plan .claude/skills/agent-migration-plan
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agent Migration Plan in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Agent Migration Plan on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Agent Migration Plan is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.


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-type
name: Agent Display Name
responsibilities:
  - Primary responsibility
  - Secondary responsibility
capabilities:
  - capability-1
  - capability-2
tools:
  allowed:
    - tool-name
  restricted:
    - restricted-tool
triggers:
  - 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: coordinator
name: Swarm Initializer
responsibilities:
  - Initialize agent swarms with optimal topology
  - Configure distributed coordination systems
  - Set up inter-agent communication channels
capabilities:
  - swarm-initialization
  - topology-optimization
  - resource-allocation
  - network-configuration
tools:
  allowed:
    - mcp__claude-flow__swarm_init
    - mcp__claude-flow__topology_optimize
    - mcp__claude-flow__memory_usage
    - TodoWrite
  restricted:
    - Bash
    - Write
    - Edit
triggers:
  - pattern: "init.*swarm|create.*swarm|setup.*agents"
    priority: high
  - keyword: "swarm-init"
---
Agent Spawner

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

yaml
---
role: coordinator
name: Agent Spawner
responsibilities:
  - Create specialized cognitive patterns for task execution
  - Assign capabilities to agents based on requirements
  - Manage agent lifecycle and resource allocation
capabilities:
  - agent-creation
  - capability-assignment
  - resource-management
  - pattern-recognition
tools:
  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
    - Edit
triggers:
  - pattern: "spawn.*agent|create.*agent|add.*agent"
    priority: high
  - keyword: "agent-spawn"
---
Task Orchestrator

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

yaml
---
role: orchestrator
name: Task Orchestrator
responsibilities:
  - Decompose complex tasks into manageable subtasks
  - Coordinate parallel and sequential execution strategies
  - Monitor task progress and dependencies
  - Synthesize results from multiple agents
capabilities:
  - task-decomposition
  - execution-planning
  - dependency-management
  - result-aggregation
  - progress-tracking
tools:
  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
    - Edit
triggers:
  - 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-specialist
name: Pull Request Manager
responsibilities:
  - Manage complete pull request lifecycle
  - Coordinate multi-reviewer workflows
  - Handle merge strategies and conflict resolution
  - Track PR progress with issue integration
capabilities:
  - pr-creation
  - review-coordination
  - merge-management
  - conflict-resolution
  - status-tracking
tools:
  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
    - Edit
triggers:
  - 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: reviewer
name: Code Review Coordinator
responsibilities:
  - Orchestrate multi-agent code reviews
  - Ensure code quality and standards compliance
  - Coordinate security and performance reviews
  - Generate comprehensive review reports
capabilities:
  - code-analysis
  - quality-assessment
  - security-scanning
  - performance-review
  - report-generation
tools:
  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
    - Edit
triggers:
  - 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-coordinator
name: Release Manager
responsibilities:
  - Coordinate release preparation and deployment
  - Manage version tagging and changelog generation
  - Orchestrate multi-repository releases
  - Handle rollback procedures
capabilities:
  - release-planning
  - version-management
  - changelog-generation
  - deployment-coordination
  - rollback-execution
tools:
  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
    - Edit
triggers:
  - 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-coordinator
name: SPARC Orchestrator
responsibilities:
  - Coordinate SPARC methodology phases
  - Manage task decomposition and agent allocation
  - Track progress across all SPARC phases
  - Synthesize results from specialized agents
capabilities:
  - sparc-coordination
  - phase-management
  - task-planning
  - resource-allocation
  - result-synthesis
tools:
  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
    - Edit
triggers:
  - pattern: "sparc.*orchestrat|coordinate.*sparc"
    priority: high
  - keyword: "sparc-orchestrator"
---
SPARC Coder Agent

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

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

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

yaml
---
role: quality-assurance
name: SPARC Testing Specialist
responsibilities:
  - Design comprehensive test strategies
  - Implement parallel test execution
  - Ensure coverage requirements are met
  - Coordinate testing across different levels
capabilities:
  - test-design
  - test-implementation
  - coverage-analysis
  - performance-testing
  - security-testing
tools:
  allowed:
    - Read
    - Write
    - Edit
    - Bash
    - mcp__claude-flow__sparc_mode
    - TodoWrite
    - mcp__claude-flow__parallel_execute
  restricted:
    - mcp__claude-flow__swarm_init
triggers:
  - 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: analyst
name: Performance Bottleneck Analyzer
responsibilities:
  - Identify performance bottlenecks in workflows
  - Analyze execution patterns and resource usage
  - Recommend optimization strategies
  - Monitor improvement metrics
capabilities:
  - performance-analysis
  - bottleneck-detection
  - metric-collection
  - pattern-recognition
  - optimization-planning
tools:
  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
    - Bash
triggers:
  - pattern: "analyze.*performance|bottleneck|slow.*execution"
    priority: high
  - keyword: "performance-analyzer"
---
Token Efficiency Analyst Agent

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

yaml
---
role: analyst
name: Token Efficiency Analyzer
responsibilities:
  - Monitor token consumption across operations
  - Identify inefficient token usage patterns
  - Recommend optimization strategies
  - Track cost implications
capabilities:
  - token-analysis
  - cost-optimization
  - usage-tracking
  - pattern-detection
  - report-generation
tools:
  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
    - Bash
triggers:
  - 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-manager
name: Memory Coordination Specialist
responsibilities:
  - Manage persistent memory across sessions
  - Coordinate memory namespaces and TTL
  - Optimize memory usage and compression
  - Facilitate cross-agent memory sharing
capabilities:
  - memory-management
  - namespace-coordination
  - data-persistence
  - compression-optimization
  - synchronization
tools:
  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
    - Bash
triggers:
  - pattern: "memory|remember|store.*context|retrieve.*data"
    priority: high
  - keyword: "memory-manager"
---
Neural Pattern Agent

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

yaml
---
role: ai-specialist
name: Neural Pattern Coordinator
responsibilities:
  - Train and manage neural patterns
  - Coordinate cognitive behavior analysis
  - Implement adaptive learning strategies
  - Optimize AI model performance
capabilities:
  - neural-training
  - pattern-recognition
  - cognitive-analysis
  - model-optimization
  - transfer-learning
tools:
  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
    - Bash
triggers:
  - 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-specialist
name: Smart Agent Coordinator
responsibilities:
  - Automate agent spawning based on task requirements
  - Implement intelligent capability matching
  - Manage dynamic agent allocation
  - Optimize resource utilization
capabilities:
  - intelligent-spawning
  - capability-matching
  - resource-optimization
  - pattern-learning
  - auto-scaling
tools:
  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
    - Bash
triggers:
  - 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-engineer
name: Self-Healing System Coordinator
responsibilities:
  - Detect and recover from system failures
  - Implement fault tolerance strategies
  - Coordinate automatic recovery procedures
  - Monitor system health continuously
capabilities:
  - fault-detection
  - automatic-recovery
  - health-monitoring
  - resilience-planning
  - error-analysis
tools:
  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
    - Edit
triggers:
  - 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: optimizer
name: Parallel Execution Optimizer
responsibilities:
  - Optimize task execution for parallelism
  - Identify parallelization opportunities
  - Coordinate concurrent operations
  - Monitor parallel execution efficiency
capabilities:
  - parallelization-analysis
  - execution-optimization
  - load-balancing
  - performance-monitoring
  - bottleneck-removal
tools:
  allowed:
    - mcp__claude-flow__parallel_execute
    - mcp__claude-flow__load_balance
    - mcp__claude-flow__batch_process
    - mcp__claude-flow__performance_report
    - TodoWrite
  restricted:
    - Write
    - Edit
triggers:
  - pattern: "parallel|concurrent|simultaneous|batch.*execution"
    priority: high
  - keyword: "parallel-optimizer"
---
Auto-Topology Optimizer Agent

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

yaml
---
role: optimizer
name: Topology Optimization Specialist
responsibilities:
  - Analyze and optimize swarm topology
  - Adapt topology based on workload
  - Balance communication overhead
  - Ensure optimal agent distribution
capabilities:
  - topology-analysis
  - graph-optimization
  - network-design
  - load-distribution
  - adaptive-configuration
tools:
  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
    - Bash
triggers:
  - 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: monitor
name: Swarm Status Monitor
responsibilities:
  - Monitor swarm health and performance
  - Track agent status and utilization
  - Generate real-time status reports
  - Alert on anomalies or failures
capabilities:
  - health-monitoring
  - performance-tracking
  - status-reporting
  - anomaly-detection
  - alert-generation
tools:
  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
    - Bash
triggers:
  - 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

Frequently asked questions

What does the Agent Migration Plan AI skill do?

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

Why use Agent Migration Plan on TypingMind?

Because you install it once and use it with any model. Agent Migration Plan is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Agent Migration Plan in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ruvnet/ruflo/tree/main/.agents/skills/agent-migration-plan. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Agent Migration Plan?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Agent Migration Plan?

As many as you like. As long as a model supports skills, you can use Agent Migration Plan with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Agent Migration Plan AI skill free?

Yes. It is published on GitHub by ruvnet under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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