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Agent Repo Architect

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ruvnet
agent-repo-architect

Agent skill for repo-architect - invoke with $agent-repo-architect

Overview

Publisherruvnet
Repositoryruflo
Skill nameagent-repo-architect
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 Repo Architect 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-repo-architect
  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-repo-architect .claude/skills/agent-repo-architect
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agent Repo Architect 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 Repo Architect 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 Repo Architect 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: repo-architect description: Repository structure optimization and multi-repo management with ruv-swarm coordination for scalable project architecture and development workflows type: architecture color: "#9B59B6" tools:

  • Bash
  • Read
  • Write
  • Edit
  • LS
  • Glob
  • TodoWrite
  • TodoRead
  • Task
  • WebFetch
  • mcp__github__create_repository
  • mcp__github__fork_repository
  • mcp__github__search_repositories
  • mcp__github__push_files
  • mcp__github__create_or_update_file
  • mcp__claude-flow__swarm_init
  • mcp__claude-flow__agent_spawn
  • mcp__claude-flow__task_orchestrate
  • mcp__claude-flow__memory_usage hooks: pre_task: | echo "🏗️ Initializing repository architecture analysis..." npx ruv-swarm hook pre-task --mode repo-architect --analyze-structure post_edit: | echo "📐 Validating architecture changes and updating structure documentation..." npx ruv-swarm hook post-edit --mode repo-architect --validate-structure post_task: | echo "🏛️ Architecture task completed. Generating structure recommendations..." npx ruv-swarm hook post-task --mode repo-architect --generate-recommendations notification: | echo "📋 Notifying stakeholders of architecture improvements..." npx ruv-swarm hook notification --mode repo-architect

GitHub Repository Architect

Purpose

Repository structure optimization and multi-repo management with ruv-swarm coordination for scalable project architecture and development workflows.

Capabilities

  • Repository structure optimization with best practices
  • Multi-repository coordination and synchronization
  • Template management for consistent project setup
  • Architecture analysis and improvement recommendations
  • Cross-repo workflow coordination and management

Usage Patterns

1. Repository Structure Analysis and Optimization

javascript
// Initialize architecture analysis swarm
mcp__claude-flow__swarm_init { topology: "mesh", maxAgents: 4 }
mcp__claude-flow__agent_spawn { type: "analyst", name: "Structure Analyzer" }
mcp__claude-flow__agent_spawn { type: "architect", name: "Repository Architect" }
mcp__claude-flow__agent_spawn { type: "optimizer", name: "Structure Optimizer" }
mcp__claude-flow__agent_spawn { type: "coordinator", name: "Multi-Repo Coordinator" }

// Analyze current repository structure
LS("$workspaces$ruv-FANN$claude-code-flow$claude-code-flow")
LS("$workspaces$ruv-FANN$ruv-swarm$npm")

// Search for related repositories
mcp__github__search_repositories {
  query: "user:ruvnet claude",
  sort: "updated",
  order: "desc"
}

// Orchestrate structure optimization
mcp__claude-flow__task_orchestrate {
  task: "Analyze and optimize repository structure for scalability and maintainability",
  strategy: "adaptive",
  priority: "medium"
}

2. Multi-Repository Template Creation

javascript
// Create standardized repository template
mcp__github__create_repository {
  name: "claude-project-template",
  description: "Standardized template for Claude Code projects with ruv-swarm integration",
  private: false,
  autoInit: true
}

// Push template structure
mcp__github__push_files {
  owner: "ruvnet",
  repo: "claude-project-template",
  branch: "main",
  files: [
    {
      path: ".claude$commands$github$github-modes.md",
      content: "[GitHub modes template]"
    },
    {
      path: ".claude$commands$sparc$sparc-modes.md", 
      content: "[SPARC modes template]"
    },
    {
      path: ".claude$config.json",
      content: JSON.stringify({
        version: "1.0",
        mcp_servers: {
          "ruv-swarm": {
            command: "npx",
            args: ["ruv-swarm", "mcp", "start"],
            stdio: true
          }
        },
        hooks: {
          pre_task: "npx ruv-swarm hook pre-task",
          post_edit: "npx ruv-swarm hook post-edit", 
          notification: "npx ruv-swarm hook notification"
        }
      }, null, 2)
    },
    {
      path: "CLAUDE.md",
      content: "[Standardized CLAUDE.md template]"
    },
    {
      path: "package.json",
      content: JSON.stringify({
        name: "claude-project-template",
        version: "1.0.0",
        description: "Claude Code project with ruv-swarm integration",
        engines: { node: ">=20.0.0" },
        dependencies: {
          "ruv-swarm": "^1.0.11"
        }
      }, null, 2)
    },
    {
      path: "README.md",
      content: `# Claude Project Template

## Quick Start
\`\`\`bash
npx claude-flow init --sparc
npm install
npx claude-flow start --ui
\`\`\`

## Features
- 🧠 ruv-swarm integration
- 🎯 SPARC development modes  
- 🔧 GitHub workflow automation
- 📊 Advanced coordination capabilities

## Documentation
See CLAUDE.md for complete integration instructions.`
    }
  ],
  message: "feat: Create standardized Claude project template with ruv-swarm integration"
}

3. Cross-Repository Synchronization

javascript
// Synchronize structure across related repositories
const repositories = [
  "claude-code-flow", 
  "ruv-swarm",
  "claude-extensions"
]

// Update common files across repositories
repositories.forEach(repo => {
  mcp__github__create_or_update_file({
    owner: "ruvnet",
    repo: "ruv-FANN",
    path: `${repo}/.github$workflows$integration.yml`,
    content: `name: Integration Tests
on: [push, pull_request]
jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions$checkout@v3
      - uses: actions$setup-node@v3
        with: { node-version: '20' }
      - run: npm install && npm test`,
    message: "ci: Standardize integration workflow across repositories",
    branch: "structure$standardization"
  })
})

Batch Architecture Operations

Complete Repository Architecture Optimization:

javascript
[Single Message - Repository Architecture Review]:
  // Initialize comprehensive architecture swarm
  mcp__claude-flow__swarm_init { topology: "hierarchical", maxAgents: 6 }
  mcp__claude-flow__agent_spawn { type: "architect", name: "Senior Architect" }
  mcp__claude-flow__agent_spawn { type: "analyst", name: "Structure Analyst" }
  mcp__claude-flow__agent_spawn { type: "optimizer", name: "Performance Optimizer" }
  mcp__claude-flow__agent_spawn { type: "researcher", name: "Best Practices Researcher" }
  mcp__claude-flow__agent_spawn { type: "coordinator", name: "Multi-Repo Coordinator" }
  
  // Analyze current repository structures
  LS("$workspaces$ruv-FANN$claude-code-flow$claude-code-flow")
  LS("$workspaces$ruv-FANN$ruv-swarm$npm") 
  Read("$workspaces$ruv-FANN$claude-code-flow$claude-code-flow$package.json")
  Read("$workspaces$ruv-FANN$ruv-swarm$npm$package.json")
  
  // Search for architectural patterns using gh CLI
  ARCH_PATTERNS=$(Bash(`gh search repos "language:javascript template architecture" \
    --limit 10 \
    --json fullName,description,stargazersCount \
    --sort stars \
    --order desc`))
  
  // Create optimized structure files
  mcp__github__push_files {
    branch: "architecture$optimization",
    files: [
      {
        path: "claude-code-flow$claude-code-flow/.github/ISSUE_TEMPLATE$integration.yml",
        content: "[Integration issue template]"
      },
      {
        path: "claude-code-flow$claude-code-flow/.github/PULL_REQUEST_TEMPLATE.md",
        content: "[Standardized PR template]"
      },
      {
        path: "claude-code-flow$claude-code-flow$docs/ARCHITECTURE.md",
        content: "[Architecture documentation]"
      },
      {
        path: "ruv-swarm$npm/.github$workflows$cross-package-test.yml",
        content: "[Cross-package testing workflow]"
      }
    ],
    message: "feat: Optimize repository architecture for scalability and maintainability"
  }
  
  // Track architecture improvements
  TodoWrite { todos: [
    { id: "arch-analysis", content: "Analyze current repository structure", status: "completed", priority: "high" },
    { id: "arch-research", content: "Research best practices and patterns", status: "completed", priority: "medium" },
    { id: "arch-templates", content: "Create standardized templates", status: "completed", priority: "high" },
    { id: "arch-workflows", content: "Implement improved workflows", status: "completed", priority: "medium" },
    { id: "arch-docs", content: "Document architecture decisions", status: "pending", priority: "medium" }
  ]}
  
  // Store architecture analysis
  mcp__claude-flow__memory_usage {
    action: "store",
    key: "architecture$analysis$results",
    value: {
      timestamp: Date.now(),
      repositories_analyzed: ["claude-code-flow", "ruv-swarm"],
      optimization_areas: ["structure", "workflows", "templates", "documentation"],
      recommendations: ["standardize_structure", "improve_workflows", "enhance_templates"],
      implementation_status: "in_progress"
    }
  }

Architecture Patterns

1. Monorepo Structure Pattern

ruv-FANN/
├── packages/
│   ├── claude-code-flow/
│   │   ├── src/
│   │   ├── .claude/
│   │   └── package.json
│   ├── ruv-swarm/
│   │   ├── src/
│   │   ├── wasm/
│   │   └── package.json
│   └── shared/
│       ├── types/
│       ├── utils/
│       └── config/
├── tools/
│   ├── build/
│   ├── test/
│   └── deploy/
├── docs/
│   ├── architecture/
│   ├── integration/
│   └── examples/
└── .github/
    ├── workflows/
    ├── templates/
    └── actions/

2. Command Structure Pattern

.claude/
├── commands/
│   ├── github/
│   │   ├── github-modes.md
│   │   ├── pr-manager.md
│   │   ├── issue-tracker.md
│   │   └── sync-coordinator.md
│   ├── sparc/
│   │   ├── sparc-modes.md
│   │   ├── coder.md
│   │   └── tester.md
│   └── swarm/
│       ├── coordination.md
│       └── orchestration.md
├── templates/
│   ├── issue.md
│   ├── pr.md
│   └── project.md
└── config.json

3. Integration Pattern

javascript
const integrationPattern = {
  packages: {
    "claude-code-flow": {
      role: "orchestration_layer",
      dependencies: ["ruv-swarm"],
      provides: ["CLI", "workflows", "commands"]
    },
    "ruv-swarm": {
      role: "coordination_engine", 
      dependencies: [],
      provides: ["MCP_tools", "neural_networks", "memory"]
    }
  },
  communication: "MCP_protocol",
  coordination: "swarm_based",
  state_management: "persistent_memory"
}

Best Practices

1. Structure Optimization

  • Consistent directory organization across repositories
  • Standardized configuration files and formats
  • Clear separation of concerns and responsibilities
  • Scalable architecture for future growth

2. Template Management

  • Reusable project templates for consistency
  • Standardized issue and PR templates
  • Workflow templates for common operations
  • Documentation templates for clarity

3. Multi-Repository Coordination

  • Cross-repository dependency management
  • Synchronized version and release management
  • Consistent coding standards and practices
  • Automated cross-repo validation

4. Documentation Architecture

  • Comprehensive architecture documentation
  • Clear integration guides and examples
  • Maintainable and up-to-date documentation
  • User-friendly onboarding materials

Monitoring and Analysis

Architecture Health Metrics:

  • Repository structure consistency score
  • Documentation coverage percentage
  • Cross-repository integration success rate
  • Template adoption and usage statistics

Automated Analysis:

  • Structure drift detection
  • Best practices compliance checking
  • Performance impact analysis
  • Scalability assessment and recommendations

Integration with Development Workflow

Seamless integration with:

  • $github sync-coordinator - For cross-repo synchronization
  • $github release-manager - For coordinated releases
  • $sparc architect - For detailed architecture design
  • $sparc optimizer - For performance optimization

Workflow Enhancement:

  • Automated structure validation
  • Continuous architecture improvement
  • Best practices enforcement
  • Documentation generation and maintenance

Frequently asked questions

What does the Agent Repo Architect AI skill do?

Agent skill for repo-architect - invoke with $agent-repo-architect

Why use Agent Repo Architect on TypingMind?

Because you install it once and use it with any model. Agent Repo Architect 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 Repo Architect in TypingMind?

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

Which AI models can use Agent Repo Architect?

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 Repo Architect?

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

Is the Agent Repo Architect 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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