# Integration Strategy: wshobson/agents + claude-force

## 🎯 Executive Summary

**wshobson/agents** is a plugin marketplace with 63 focused plugins, 85 agents, 47 skills, and 44 tools using a modular, progressive disclosure architecture.

**claude-force** is a production-ready orchestration system with 19 agents, 11 skills, 10 workflows, formal contracts, governance, and MCP server.

This document outlines **10 strategic integration ideas** to combine the best of both systems.

---

## 📊 System Comparison

| Feature | wshobson/agents | claude-force | Integration Opportunity |
|---------|-----------------|--------------|-------------------------|
| **Architecture** | Plugin marketplace (install what you need) | All-in-one system | Hybrid: Core + marketplace |
| **Agents** | 85 agents (domain-specific) | 19 agents (comprehensive) | Merge + deduplicate |
| **Skills** | 47 skills (progressive disclosure) | 11 skills (comprehensive) | Import + categorize |
| **Model Strategy** | Hybrid (Haiku + Sonnet) | Single model | Adopt hybrid orchestration |
| **Governance** | None mentioned | 6-layer validation system | Add to marketplace plugins |
| **Contracts** | None mentioned | Formal contracts for all agents | Add contracts to imported agents |
| **MCP Server** | Not mentioned | Full implementation | Expose marketplace via MCP |
| **Workflows** | Per-plugin | 10 system workflows | Combine both approaches |

---

## 💡 Integration Ideas

### 1. 🚀 Quick Start / Project Initialization (Your Idea)

**Concept**: Users provide a project description, and claude-force suggests and initializes the optimal .claude template.

**Implementation**:

```python
# claude_force/quick_start.py

class QuickStartOrchestrator:
    """Intelligent project initialization with template suggestion."""

    def __init__(self):
        self.prompt_engineer = PromptEngineer()
        self.claude_code_expert = ClaudeCodeExpert()
        self.templates = self._load_templates()

    async def initialize_project(
        self,
        description: str,
        project_type: Optional[str] = None,
        tech_stack: Optional[List[str]] = None
    ) -> ProjectTemplate:
        """
        Analyze project description and initialize optimal .claude template.

        Steps:
        1. Analyze description using prompt-engineer
        2. Match to templates using semantic similarity
        3. Suggest agents, workflows, and skills
        4. Generate .claude/ directory structure
        5. Create customized agent definitions
        6. Set up governance and hooks
        """

        # Step 1: Analyze project requirements
        analysis = await self.prompt_engineer.analyze_project(description)

        # Step 2: Semantic matching to templates
        templates = await self._match_templates(
            description=description,
            analysis=analysis,
            tech_stack=tech_stack or []
        )

        # Step 3: Get user confirmation
        selected_template = await self._interactive_selection(templates)

        # Step 4: Generate .claude/ structure
        config = await self.claude_code_expert.generate_config(
            template=selected_template,
            project_analysis=analysis
        )

        # Step 5: Initialize directory
        self._create_claude_directory(config)

        return config

# CLI Command
@app.command()
def init(
    description: str = typer.Option(..., "--description", "-d"),
    interactive: bool = typer.Option(True, "--interactive", "-i"),
    template: Optional[str] = typer.Option(None, "--template", "-t")
):
    """
    Initialize .claude/ directory with intelligent template selection.

    Examples:
        claude-force init -d "Building a SaaS app with React, FastAPI, and PostgreSQL"
        claude-force init -d "ML model training pipeline for NLP tasks" -t ml-project
        claude-force init -d "E-commerce platform" --interactive
    """
    orchestrator = QuickStartOrchestrator()
    config = orchestrator.initialize_project(description, template)

    console.print(f"✅ Initialized {config.name} with {len(config.agents)} agents")
    console.print(f"📁 Created .claude/ directory with:")
    console.print(f"   - {len(config.agents)} agents")
    console.print(f"   - {len(config.workflows)} workflows")
    console.print(f"   - {len(config.skills)} skills")
    console.print(f"   - {len(config.hooks)} hooks")
```

**Templates to Include** (from both repos):

```yaml
# templates/templates.yaml

templates:

  # Full-stack templates
  - id: fullstack-web
    name: "Full-Stack Web Application"
    description: "Complete web app with frontend, backend, database"
    agents: [frontend-architect, backend-architect, database-architect, ui-components-expert, python-expert]
    workflows: [full-stack-feature, frontend-only, backend-only]
    skills: [api-design, test-generation, code-review]
    keywords: [web, fullstack, react, api, postgres, mongodb]

  # AI/ML templates
  - id: ml-project
    name: "Machine Learning Project"
    description: "ML model training, evaluation, and deployment"
    agents: [ai-engineer, data-engineer, python-expert, code-reviewer]
    workflows: [ai-ml-development, data-pipeline]
    skills: [test-generation, git-workflow]
    keywords: [ml, ai, pytorch, tensorflow, training, deployment]

  - id: llm-app
    name: "LLM-Powered Application"
    description: "RAG, chatbots, semantic search with LLMs"
    agents: [prompt-engineer, ai-engineer, backend-architect, security-specialist]
    workflows: [llm-integration]
    skills: [api-design, test-generation]
    keywords: [llm, rag, chatbot, openai, claude, embeddings]

  # Data templates
  - id: data-pipeline
    name: "Data Engineering Pipeline"
    description: "ETL, data warehousing, analytics"
    agents: [data-engineer, database-architect, python-expert]
    workflows: [data-pipeline]
    skills: [test-generation, git-workflow]
    keywords: [etl, airflow, spark, bigquery, snowflake, data]

  # Backend templates
  - id: api-service
    name: "REST API Service"
    description: "Backend API with database and authentication"
    agents: [backend-architect, database-architect, security-specialist, python-expert]
    workflows: [backend-only]
    skills: [api-design, test-generation, dockerfile]
    keywords: [api, rest, graphql, backend, microservice]

  # Frontend templates
  - id: frontend-spa
    name: "Frontend Single-Page Application"
    description: "React/Vue/Angular SPA with state management"
    agents: [frontend-architect, ui-components-expert, frontend-developer]
    workflows: [frontend-only]
    skills: [test-generation, git-workflow]
    keywords: [frontend, react, vue, angular, spa, ui]

  # Mobile templates
  - id: mobile-app
    name: "Mobile Application"
    description: "React Native or Flutter mobile app"
    agents: [mobile-app-expert, ui-components-expert, backend-architect]
    workflows: [mobile-feature]
    skills: [test-generation, api-design]
    keywords: [mobile, react-native, flutter, ios, android]

  # DevOps templates
  - id: infrastructure
    name: "Infrastructure & DevOps"
    description: "Docker, Kubernetes, CI/CD, cloud deployment"
    agents: [devops-architect, google-cloud-expert, deployment-integration-expert]
    workflows: [infrastructure]
    skills: [dockerfile, git-workflow]
    keywords: [devops, kubernetes, docker, terraform, aws, gcp]

  # Claude Code templates
  - id: claude-code-system
    name: "Claude Code Multi-Agent System"
    description: "Build custom agents, workflows, and governance"
    agents: [claude-code-expert, python-expert, document-writer-expert]
    workflows: [claude-code-system]
    skills: [create-agent, create-skill]
    keywords: [claude-code, agents, orchestration, governance]
```

**Usage Flow**:

```bash
# Interactive mode (recommended)
$ claude-force init -d "Building a customer support chatbot with RAG"

🤖 Analyzing project description...

📋 Suggested Templates:
  1. ⭐ LLM-Powered Application (95% match)
     - Agents: prompt-engineer, ai-engineer, backend-architect, security-specialist
     - Workflows: llm-integration
     - Best for: RAG systems, chatbots, semantic search

  2. Full-Stack Web Application (72% match)
     - Agents: frontend-architect, backend-architect, ui-components-expert
     - Workflows: full-stack-feature
     - Best for: Complete web applications

  3. REST API Service (68% match)
     - Agents: backend-architect, database-architect, security-specialist
     - Workflows: backend-only
     - Best for: Backend services

Select template [1]: 1

✅ Selected: LLM-Powered Application

🎯 Customizing for your project...
  - Adding vector database support (detected: RAG)
  - Including customer support domain knowledge
  - Setting up prompt evaluation workflow

📁 Creating .claude/ directory:
  ✅ Created claude.json
  ✅ Added 4 agents (prompt-engineer, ai-engineer, backend-architect, security-specialist)
  ✅ Added 1 workflow (llm-integration)
  ✅ Added 3 skills (api-design, test-generation, git-workflow)
  ✅ Set up governance hooks
  ✅ Created task.md template

🚀 Next steps:
  1. Edit .claude/task.md with your first task
  2. Run: claude-force run workflow llm-integration
  3. Review output in .claude/work.md

💡 Tip: Use 'claude-force info <agent>' to learn about each agent
```

**Benefits**:
- ✅ Reduces setup time from hours to minutes
- ✅ Ensures best practices from the start
- ✅ Intelligent template matching
- ✅ Customized for specific project needs
- ✅ Includes all necessary agents, workflows, and skills

---

### 2. 🏪 Plugin Marketplace for claude-force

**Concept**: Create a plugin system where users can install specific agent packs from both repos.

**Implementation**:

```python
# claude_force/marketplace.py

class MarketplaceManager:
    """Manage plugin installation from multiple sources."""

    OFFICIAL_REPOS = {
        "claude-force": "khanh-vu/claude-force",
        "wshobson": "wshobson/agents"
    }

    def __init__(self):
        self.installed_plugins = self._load_installed()
        self.available_plugins = self._fetch_marketplace()

    def list_available(self, category: Optional[str] = None):
        """List all available plugins from all sources."""
        plugins = []

        # From claude-force (built-in)
        plugins.extend(self._get_builtin_plugins())

        # From wshobson/agents (external)
        plugins.extend(self._get_external_plugins("wshobson"))

        if category:
            plugins = [p for p in plugins if p.category == category]

        return plugins

    def install_plugin(
        self,
        plugin_id: str,
        source: str = "auto"
    ):
        """
        Install plugin and its dependencies.

        Examples:
            install_plugin("llm-application-dev", source="wshobson")
            install_plugin("ai-ml-complete", source="claude-force")
        """
        plugin = self._resolve_plugin(plugin_id, source)

        # Download agents, skills, workflows
        agents = self._download_agents(plugin)
        skills = self._download_skills(plugin)
        workflows = self._download_workflows(plugin)

        # Integrate into .claude/
        self._integrate_agents(agents)
        self._integrate_skills(skills)
        self._integrate_workflows(workflows)

        # Update claude.json
        self._update_config(plugin)

        # Add contracts if missing (claude-force style)
        self._generate_contracts(agents)

        return InstallationResult(
            plugin=plugin,
            agents_added=len(agents),
            skills_added=len(skills),
            workflows_added=len(workflows)
        )

# CLI commands
@marketplace.command()
def search(query: str):
    """Search marketplace for plugins."""
    manager = MarketplaceManager()
    results = manager.search(query)

    for result in results:
        console.print(f"📦 {result.name} ({result.source})")
        console.print(f"   {result.description}")
        console.print(f"   Agents: {len(result.agents)}, Skills: {len(result.skills)}")
        console.print()

@marketplace.command()
def install(plugin_id: str, source: str = "auto"):
    """Install a plugin from the marketplace."""
    manager = MarketplaceManager()
    result = manager.install_plugin(plugin_id, source)

    console.print(f"✅ Installed {result.plugin.name}")
    console.print(f"   Added: {result.agents_added} agents, {result.skills_added} skills")
```

**Plugin Categories**:

```yaml
# .claude/marketplace/registry.yaml

categories:

  - name: "Development"
    plugins:
      - id: "python-complete"
        source: "wshobson"
        agents: [python-developer, python-senior, uv-package-manager]
        skills: [async-patterns, testing, packaging]

      - id: "frontend-complete"
        source: "claude-force"
        agents: [frontend-architect, ui-components-expert, frontend-developer]
        skills: [test-generation]

  - name: "AI & ML"
    plugins:
      - id: "llm-application-dev"
        source: "wshobson"
        agents: [ai-engineer, prompt-engineer]
        skills: [prompt-engineering]

      - id: "ai-ml-complete"
        source: "claude-force"
        agents: [ai-engineer, prompt-engineer, data-engineer]
        workflows: [ai-ml-development, llm-integration]
        skills: [create-agent, create-skill]

  - name: "Data Engineering"
    plugins:
      - id: "data-pipeline-complete"
        source: "both"  # Combines both repos
        agents: [data-engineer, backend-architect]  # claude-force
        skills: [etl-patterns, data-quality]  # wshobson

  - name: "Infrastructure"
    plugins:
      - id: "kubernetes-ops"
        source: "wshobson"
        agents: [kubernetes-engineer]
        skills: [k8s-manifests, helm-charts, gitops]

      - id: "devops-complete"
        source: "claude-force"
        agents: [devops-architect, google-cloud-expert]
        skills: [dockerfile, git-workflow]
```

**Usage**:

```bash
# Search for plugins
$ claude-force marketplace search "kubernetes"

📦 kubernetes-ops (wshobson/agents)
   Kubernetes deployment, Helm charts, and GitOps workflows
   Agents: 3, Skills: 5

📦 devops-complete (claude-force)
   Complete DevOps with Docker, Kubernetes, and cloud
   Agents: 2, Skills: 2, Workflows: 1

# List all available
$ claude-force marketplace list --category "AI & ML"

# Install plugin
$ claude-force marketplace install llm-application-dev --source wshobson

📦 Installing llm-application-dev from wshobson/agents...
  ✅ Downloaded 2 agents (ai-engineer, prompt-engineer)
  ✅ Downloaded 1 skill (prompt-engineering)
  ✅ Generated contracts for agents
  ✅ Updated .claude/claude.json

✅ Installation complete!

💡 Try: claude-force run agent prompt-engineer --task "Your task"
```

---

### 3. 🔀 Hybrid Model Orchestration

**Concept**: Adopt wshobson's hybrid model strategy (Haiku for deterministic, Sonnet for complex) in claude-force.

**Implementation**:

```python
# claude_force/orchestrator.py (enhanced)

class HybridOrchestrator(AgentOrchestrator):
    """
    Hybrid model orchestration inspired by wshobson/agents.

    Strategy:
    - Haiku: Fast, deterministic tasks (formatting, linting, simple transforms)
    - Sonnet: Complex reasoning (architecture, design, review)
    - Opus: Critical decisions (security, production changes)
    """

    # Model classification for agents
    MODEL_STRATEGY = {
        # Haiku agents (fast, deterministic)
        "haiku": [
            "document-writer-expert",  # Formatting
            "api-documenter",          # Template-based docs
            "deployment-integration-expert",  # Config generation
        ],

        # Sonnet agents (complex reasoning)
        "sonnet": [
            "frontend-architect",
            "backend-architect",
            "database-architect",
            "ai-engineer",
            "prompt-engineer",
            "data-engineer",
            "code-reviewer",
            "security-specialist",
            "bug-investigator",
            "claude-code-expert",
        ],

        # Opus agents (critical decisions)
        "opus": [
            # Can be specified per-task for critical operations
        ]
    }

    def select_model_for_agent(
        self,
        agent_name: str,
        task_complexity: str = "auto"
    ) -> str:
        """
        Select optimal model based on agent and task.

        Args:
            agent_name: Name of the agent
            task_complexity: auto | simple | complex | critical

        Returns:
            Model name (claude-3-haiku, claude-3-5-sonnet, claude-opus)
        """
        if task_complexity == "auto":
            # Automatic selection based on agent classification
            for model, agents in self.MODEL_STRATEGY.items():
                if agent_name in agents:
                    return self._model_name(model)

            # Default to Sonnet for unknown agents
            return "claude-3-5-sonnet-20241022"

        elif task_complexity == "simple":
            return "claude-3-haiku-20240307"

        elif task_complexity == "complex":
            return "claude-3-5-sonnet-20241022"

        elif task_complexity == "critical":
            return "claude-opus-4-20250514"

    def run_agent(
        self,
        agent_name: str,
        task: str,
        model: Optional[str] = None,
        auto_select_model: bool = True
    ) -> AgentResult:
        """Run agent with hybrid model selection."""

        if auto_select_model and model is None:
            # Analyze task complexity
            complexity = self._analyze_task_complexity(task, agent_name)
            model = self.select_model_for_agent(agent_name, complexity)

            logger.info(f"Auto-selected {model} for {agent_name} (complexity: {complexity})")

        return super().run_agent(agent_name, task, model=model)

    def _analyze_task_complexity(self, task: str, agent_name: str) -> str:
        """
        Analyze task complexity to determine appropriate model.

        Heuristics:
        - Simple: < 100 tokens, clear instructions, template-based
        - Complex: > 100 tokens, requires reasoning, multiple steps
        - Critical: Production changes, security, data migration
        """
        task_lower = task.lower()

        # Critical indicators
        critical_keywords = [
            "production", "delete", "drop", "migrate",
            "security audit", "vulnerability", "compliance"
        ]
        if any(kw in task_lower for kw in critical_keywords):
            return "critical"

        # Simple indicators
        simple_keywords = [
            "format", "lint", "document", "generate docs",
            "create readme", "add comments"
        ]
        if any(kw in task_lower for kw in simple_keywords):
            return "simple"

        # Complex indicators (default)
        return "complex"

# Configuration
# .claude/config.yaml
hybrid_orchestration:
  enabled: true
  auto_select_model: true

  model_pricing:  # Cost optimization
    haiku: 0.00025    # per 1K input tokens
    sonnet: 0.003     # per 1K input tokens
    opus: 0.015       # per 1K input tokens

  cost_threshold: 1.00  # Maximum cost per task in USD
  prefer_cheaper: true  # Prefer cheaper models when quality is equivalent
```

**CLI Support**:

```bash
# Automatic model selection
$ claude-force run agent document-writer-expert --task "Generate README"
# Auto-selects: claude-3-haiku (simple, deterministic)

$ claude-force run agent ai-engineer --task "Design RAG architecture"
# Auto-selects: claude-3-5-sonnet (complex reasoning)

# Manual override
$ claude-force run agent code-reviewer --task "Review auth.py" --model opus
# Uses: claude-opus-4 (critical security review)

# Show cost estimate
$ claude-force run agent frontend-architect --task "Design app" --estimate-cost
📊 Cost Estimate:
   Model: claude-3-5-sonnet-20241022
   Estimated tokens: 5,000 input + 3,000 output
   Estimated cost: $0.024

Proceed? [Y/n]:
```

**Benefits**:
- ✅ 60-80% cost savings for simple tasks
- ✅ 3-5x faster execution for deterministic operations
- ✅ Optimized quality-to-cost ratio
- ✅ Automatic model selection based on task

---

### 4. 📦 Progressive Disclosure for Skills

**Concept**: Load skills dynamically only when needed (wshobson's approach).

**Implementation**:

```python
# claude_force/skills_manager.py

class ProgressiveSkillsManager:
    """
    Load skills on-demand to reduce token usage.

    Instead of loading all 11 skills into every agent prompt,
    activate only relevant skills based on task analysis.
    """

    def __init__(self):
        self.skills_registry = self._load_skills_registry()
        self.skill_cache = {}

    def analyze_required_skills(
        self,
        agent_name: str,
        task: str
    ) -> List[str]:
        """
        Analyze task to determine which skills are needed.

        Returns:
            List of skill IDs to activate
        """
        required = []

        # Get agent's preferred skills
        agent_skills = self.skills_registry.get_agent_skills(agent_name)

        # Analyze task keywords
        task_lower = task.lower()

        # Skill activation rules
        if any(kw in task_lower for kw in ["test", "testing", "pytest", "unit test"]):
            required.append("test-generation")

        if any(kw in task_lower for kw in ["review", "code quality", "security"]):
            required.append("code-review")

        if any(kw in task_lower for kw in ["api", "rest", "graphql", "endpoint"]):
            required.append("api-design")

        if any(kw in task_lower for kw in ["docker", "container", "dockerfile"]):
            required.append("dockerfile")

        if any(kw in task_lower for kw in ["git", "commit", "pr", "branch"]):
            required.append("git-workflow")

        if any(kw in task_lower for kw in ["agent", "create agent", "new agent"]):
            required.append("create-agent")

        if any(kw in task_lower for kw in ["skill", "create skill", "new skill"]):
            required.append("create-skill")

        # Combine with agent preferences (take intersection)
        if agent_skills:
            required = list(set(required) & set(agent_skills))

        return required

    def load_skills(self, skill_ids: List[str]) -> str:
        """
        Load skill content on-demand.

        Returns:
            Combined skill content as markdown string
        """
        content_parts = []

        for skill_id in skill_ids:
            if skill_id in self.skill_cache:
                content = self.skill_cache[skill_id]
            else:
                content = self._load_skill_file(skill_id)
                self.skill_cache[skill_id] = content

            content_parts.append(content)

        if not content_parts:
            return ""

        return "\n\n---\n\n".join([
            f"# Skill: {skill_id}\n\n{content}"
            for skill_id, content in zip(skill_ids, content_parts)
        ])

# Enhanced orchestrator
class EnhancedOrchestrator(HybridOrchestrator):

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.skills_manager = ProgressiveSkillsManager()

    def _build_prompt(
        self,
        agent_def: str,
        task: str,
        agent_name: str
    ) -> str:
        """
        Build prompt with progressive skill loading.

        Before: Load all 11 skills (~15K tokens)
        After: Load only relevant skills (~3-5K tokens)
        """
        # Analyze which skills are needed
        required_skills = self.skills_manager.analyze_required_skills(
            agent_name=agent_name,
            task=task
        )

        # Load only required skills
        skills_content = self.skills_manager.load_skills(required_skills)

        # Build final prompt
        prompt = f"""{agent_def}

{f"## Available Skills\n\n{skills_content}" if skills_content else ""}

## Task

{task}

## Instructions

Complete the task following the agent definition and available skills.
"""

        # Log token savings
        tokens_saved = (11 - len(required_skills)) * 1500  # Avg 1.5K per skill
        if tokens_saved > 0:
            logger.info(
                f"Progressive disclosure saved ~{tokens_saved} tokens "
                f"(loaded {len(required_skills)}/11 skills)"
            )

        return prompt
```

**Benefits**:
- ✅ 40-60% reduction in prompt tokens
- ✅ Faster API responses
- ✅ Lower costs
- ✅ Skills still available when needed

**Metrics**:
```
Before (all skills): 15,000 tokens/request
After (progressive): 5,000-8,000 tokens/request
Savings: 40-60% token reduction
Cost savings: $0.015 → $0.006 per request (Sonnet)
```

---

### 5. 🌐 Agent Import/Export Tool

**Concept**: Import agents from wshobson/agents and export claude-force agents to their format.

**Implementation**:

```python
# claude_force/import_export.py

class AgentPortingTool:
    """Import/export agents between claude-force and wshobson/agents."""

    def import_from_wshobson(
        self,
        agent_path: str,
        generate_contract: bool = True
    ) -> Agent:
        """
        Import agent from wshobson/agents format.

        Args:
            agent_path: Path to agent markdown file
            generate_contract: Auto-generate claude-force contract

        Returns:
            Agent object ready for claude-force
        """
        # Parse wshobson format
        content = Path(agent_path).read_text()
        agent = self._parse_wshobson_format(content)

        # Convert to claude-force format
        claude_force_agent = self._convert_to_claude_force(agent)

        # Generate contract if requested
        if generate_contract:
            contract = self._generate_contract(claude_force_agent)
            claude_force_agent.contract = contract

        return claude_force_agent

    def export_to_wshobson(
        self,
        agent_name: str,
        plugin_name: str
    ) -> Plugin:
        """
        Export claude-force agent to wshobson plugin format.

        Creates a plugin structure compatible with wshobson/agents.
        """
        agent = self.orchestrator.get_agent(agent_name)

        # Create plugin structure
        plugin = Plugin(
            name=plugin_name,
            description=agent.description,
            agents=[agent],
            version="1.0.0"
        )

        # Convert to wshobson format (simpler, no contracts)
        wshobson_agent = self._convert_to_wshobson(agent)

        # Create directory structure
        self._create_plugin_directory(plugin, wshobson_agent)

        return plugin

# CLI commands
@import_export.command()
def import_agent(
    source: str,
    agent_file: str,
    name: str = None
):
    """
    Import agent from external source.

    Examples:
        claude-force import wshobson kubernetes-engineer.md
        claude-force import wshobson python-senior.md --name python-senior-dev
    """
    tool = AgentPortingTool()
    agent = tool.import_from_wshobson(agent_file)

    console.print(f"✅ Imported {agent.name}")
    console.print(f"   Generated contract: {agent.contract.path}")
    console.print(f"   Added to .claude/agents/")

@import_export.command()
def export_agent(
    agent_name: str,
    format: str = "wshobson",
    output_dir: str = "./exported"
):
    """
    Export claude-force agent to external format.

    Examples:
        claude-force export ai-engineer --format wshobson
        claude-force export prompt-engineer --format wshobson --output-dir ./my-plugins
    """
    tool = AgentPortingTool()
    plugin = tool.export_to_wshobson(agent_name, f"{agent_name}-plugin")

    console.print(f"✅ Exported {agent_name} to {output_dir}/")
    console.print(f"   Plugin: {plugin.name}")
    console.print(f"   Format: {format}")
```

---

### 6. 🎨 Template Gallery with Examples

**Concept**: Visual gallery of project templates with screenshots and example outputs.

**Implementation**:

```bash
# .claude/templates/gallery/
├── fullstack-web/
│   ├── template.yaml
│   ├── screenshot.png
│   ├── example-task.md
│   └── example-output.md
├── ml-project/
├── llm-app/
└── data-pipeline/

# CLI
$ claude-force templates gallery

📸 Template Gallery
===================

1. Full-Stack Web Application ⭐⭐⭐⭐⭐ (127 uses)

   [Screenshot: React + FastAPI + PostgreSQL architecture diagram]

   Description: Complete web app with frontend, backend, and database
   Agents: 5 (frontend-architect, backend-architect, database-architect, ui-components-expert, python-expert)
   Workflows: 3 (full-stack-feature, frontend-only, backend-only)

   Example task: "Build user authentication with OAuth"
   Expected output:
     - Frontend: Login/signup components
     - Backend: Auth endpoints, JWT middleware
     - Database: Users table, sessions
     - Tests: 15+ unit/integration tests

   Try it: claude-force init --template fullstack-web

2. LLM-Powered Application ⭐⭐⭐⭐⭐ (89 uses)
   ...
```

---

### 7. 🔍 Intelligent Agent Recommendation Engine

**Concept**: Enhanced semantic selection using both repos' agents.

**Implementation**:

```python
# Enhanced semantic selector with marketplace
class MarketplaceSemanticSelector(SemanticAgentSelector):
    """
    Semantic selection across both claude-force and wshobson/agents.
    """

    def __init__(self):
        super().__init__()
        self.marketplace = MarketplaceManager()

    def recommend_agents(
        self,
        task: str,
        top_k: int = 5,
        include_marketplace: bool = True
    ) -> List[AgentMatch]:
        """
        Recommend agents from both built-in and marketplace.
        """
        # Get built-in recommendations
        builtin = super().recommend_agents(task, top_k * 2)

        if not include_marketplace:
            return builtin[:top_k]

        # Get marketplace recommendations
        marketplace_agents = self.marketplace.search_agents(task)
        marketplace_matches = self._score_marketplace_agents(
            task,
            marketplace_agents
        )

        # Combine and re-rank
        all_matches = builtin + marketplace_matches
        all_matches.sort(key=lambda x: x.confidence, reverse=True)

        return all_matches[:top_k]

# CLI
$ claude-force recommend --task "Kubernetes deployment" --include-marketplace

🤖 Agent Recommendations (including marketplace):

1. kubernetes-engineer (wshobson/agents) - 0.94 confidence ⭐
   Source: wshobson/agents (not installed)
   Expertise: Kubernetes manifests, Helm charts, GitOps

   💡 To install: claude-force marketplace install kubernetes-ops

2. devops-architect (claude-force) - 0.87 confidence
   Source: Built-in
   Expertise: Docker, Kubernetes, infrastructure as code

   ✅ Already available

3. deployment-integration-expert (claude-force) - 0.73 confidence
   Source: Built-in
   Expertise: Deployment configuration, CI/CD

   ✅ Already available
```

---

### 8. 📚 Community Contribution System

**Concept**: Allow users to contribute agents/skills back to either repository.

**Implementation**:

```bash
# CLI
$ claude-force contribute agent mobile-app-expert --target wshobson

🎁 Contributing mobile-app-expert to wshobson/agents

✅ Validation checks:
   - Agent definition complete
   - Contract exists
   - Examples included
   - Documentation clear

📝 Preparing submission:
   - Converting to wshobson format
   - Removing claude-force specific sections
   - Generating plugin structure

🚀 Next steps:
   1. Review exported plugin at ./exported/mobile-app-plugin/
   2. Fork wshobson/agents repository
   3. Add plugin to marketplace.json
   4. Create pull request

📄 PR template created at ./PR_TEMPLATE.md
```

---

### 9. 🔧 Smart Workflow Composer

**Concept**: Combine agents from both repos to create custom workflows.

**Implementation**:

```python
# claude_force/workflow_composer.py

class WorkflowComposer:
    """
    Intelligent workflow creation using agents from multiple sources.
    """

    def compose_workflow(
        self,
        goal: str,
        available_agents: List[str] = None,
        max_agents: int = 10
    ) -> Workflow:
        """
        Compose optimal workflow for a goal.

        Uses both built-in and marketplace agents.
        """
        # Step 1: Understand the goal
        analysis = self.prompt_engineer.analyze_goal(goal)

        # Step 2: Identify required agent types
        required_types = analysis.required_agent_types
        # e.g., ["architect", "implementation", "testing", "security", "deployment"]

        # Step 3: Find best agents for each type
        agents_sequence = []

        for agent_type in required_types:
            candidates = self._find_agents_by_type(
                agent_type,
                include_marketplace=True
            )

            # Score based on goal relevance
            best_agent = self._select_best_agent(
                candidates,
                goal,
                analysis
            )

            agents_sequence.append(best_agent)

        # Step 4: Validate workflow
        validated = self._validate_workflow_sequence(agents_sequence)

        # Step 5: Create workflow object
        workflow = Workflow(
            name=f"custom-{slugify(goal)}",
            description=f"Custom workflow for: {goal}",
            agents=validated,
            created_by="workflow-composer",
            goal=goal
        )

        return workflow

# CLI
$ claude-force compose "Deploy ML model to production with monitoring"

🎯 Analyzing goal: "Deploy ML model to production with monitoring"

📋 Required steps identified:
   1. Model preparation (ML Engineer)
   2. API wrapper (Backend Architect)
   3. Containerization (DevOps)
   4. Deployment (Kubernetes Engineer)
   5. Monitoring setup (MLOps Engineer)
   6. Security review (Security Specialist)

🤖 Selecting optimal agents:

   Step 1: ML Engineer
     ✅ ai-engineer (claude-force) - 0.92 confidence

   Step 2: Backend Architect
     ✅ backend-architect (claude-force) - 0.89 confidence

   Step 3: DevOps
     ✅ devops-architect (claude-force) - 0.86 confidence

   Step 4: Kubernetes Engineer
     ⚠️  kubernetes-engineer (wshobson/agents) - 0.94 confidence (not installed)
     💡 Alternative: devops-architect (claude-force) - 0.76 confidence

   Step 5: MLOps Engineer
     ⚠️  mlops-engineer (wshobson/agents) - 0.91 confidence (not installed)
     💡 Alternative: ai-engineer (claude-force) - 0.71 confidence

   Step 6: Security review
     ✅ security-specialist (claude-force) - 0.88 confidence

📊 Workflow Summary:
   Name: custom-deploy-ml-model-to-production
   Agents: 6 (4 built-in, 2 marketplace)
   Estimated duration: 45-60 minutes
   Estimated cost: $2.50-$4.00

Options:
   1. Use all built-in agents (available now)
   2. Install marketplace agents for better results (recommended)
   3. Customize workflow manually

Select option [2]:
```

---

### 10. 📊 Cross-Repository Analytics

**Concept**: Analytics on agent usage, performance comparison between repos.

**Implementation**:

```python
# claude_force/analytics.py

class CrossRepoAnalytics:
    """
    Analytics across claude-force and wshobson/agents.
    """

    def compare_agent_performance(
        self,
        task: str,
        agents: List[str]
    ) -> ComparisonReport:
        """
        Run same task with different agents and compare results.
        """
        results = []

        for agent_name in agents:
            result = self.orchestrator.run_agent(agent_name, task)
            results.append({
                "agent": agent_name,
                "source": self._get_agent_source(agent_name),
                "duration": result.duration,
                "tokens": result.tokens_used,
                "cost": result.cost,
                "quality_score": self._assess_quality(result.output)
            })

        return ComparisonReport(results)

# CLI
$ claude-force analyze compare \
    --task "Review authentication code" \
    --agents "code-reviewer,code-review-ai"

📊 Agent Performance Comparison
================================

Task: "Review authentication code"
Agents tested: 2

Results:

1. code-reviewer (claude-force)
   Duration: 45s
   Tokens: 12,500
   Cost: $0.038
   Quality: 8.5/10
   Model: claude-3-5-sonnet

   Strengths:
     - Comprehensive security analysis
     - OWASP Top 10 coverage
     - Detailed recommendations

   Weaknesses:
     - Longer response time
     - Higher token usage

2. code-review-ai (wshobson/agents)
   Duration: 18s
   Tokens: 4,200
   Cost: $0.001
   Quality: 7.2/10
   Model: claude-3-haiku

   Strengths:
     - Very fast
     - Low cost
     - Good for quick checks

   Weaknesses:
     - Less detailed analysis
     - Misses some edge cases

💡 Recommendation:
   Use code-reviewer (claude-force) for:
     - Production code reviews
     - Security-critical code
     - Complete analysis needed

   Use code-review-ai (wshobson/agents) for:
     - Quick PR checks
     - Draft code reviews
     - Development feedback
```

---

## 🎯 Implementation Roadmap

### Phase 1: Foundation (Week 1-2)
1. ✅ Quick Start / Project Initialization
2. ✅ Hybrid Model Orchestration
3. ✅ Progressive Disclosure for Skills

### Phase 2: Marketplace (Week 3-4)
4. Plugin Marketplace Infrastructure
5. Agent Import/Export Tool
6. Template Gallery

### Phase 3: Intelligence (Week 5-6)
7. Enhanced Semantic Selection
8. Smart Workflow Composer
9. Community Contribution System

### Phase 4: Analytics (Week 7-8)
10. Cross-Repository Analytics
11. Performance Monitoring
12. Cost Optimization Dashboard

---

## 📈 Expected Benefits

### For Users
- ✅ **Faster Setup**: 5 minutes vs 2 hours (96% time savings)
- ✅ **Lower Costs**: 60-80% reduction with hybrid models
- ✅ **More Agents**: 19 → 100+ agents (both repos combined)
- ✅ **Better Matches**: Semantic selection across all agents
- ✅ **Flexibility**: Install only what you need

### For Development
- ✅ **Code Reuse**: Leverage 85 agents from wshobson
- ✅ **Best Practices**: Learn from both approaches
- ✅ **Community**: Tap into larger ecosystem
- ✅ **Innovation**: Combine strengths of both systems

### For Ecosystem
- ✅ **Interoperability**: Standard formats between repos
- ✅ **Growth**: More contributors, more agents
- ✅ **Quality**: Competition drives improvement
- ✅ **Choice**: Users pick best tools for their needs

---

## 🔗 Next Steps

1. **Review & Prioritize**: Which ideas resonate most?
2. **Prototype**: Build POC for top 3 ideas
3. **User Testing**: Get feedback from real users
4. **Iterate**: Refine based on feedback
5. **Launch**: Roll out features incrementally

---

**Which integration ideas interest you most? I can help implement any of these in detail!** 🚀
