Skill Creator

Skill Creator (Meta-Skill)

📋 Overview

This is the Master Skill responsible for spawning new agent capabilities. It uses an interactive interview process to determine the requirements, selects the appropriate AI architecture (Local vs Cloud), and generates a production-grade skill package.

🚀 Usage

aios run skill-creator

🔄 Workflow Steps

1. Requirement Interview

Action: interactive_input Prompt: "What task would you like to automate? Please describe the inputs, desired outputs, and any sensitivity concerns."

2. Architecture Decision

Action: execute_python Logic: Run ModelSelector.analyze_requirements(input) Output: Returns ModelConfig (e.g., {"mode": "local_only", "model": "llama3"})

3. Skill Generation

Action: call_agent Agent: EnhancedSkillGenerator Parameters:

• voice_input: [User Response from Step 1]
• context: [Architecture Decision from Step 2]

4. Validation & Deployment

Action: terminal Command: python -c "import yaml; yaml.safe_load(open('skills/{generated_skill_name}/workflow.yaml'))" Description: Verifies the generated skill folder structure and YAML syntax.

5. Test Execution

Action: terminal Command: python -m ai_os.workflows.enhanced_skill_executor --test {generated_skill_name} Description: Tests the skill execution in a safe environment.

⚙️ Configuration

This skill is configured to run in local_first mode using llama3-local for privacy and security during skill creation.

🔄 Detailed Implementation Logic

Phase 1 (Discovery): User Interview

• Collect task requirements
• Identify sensitivity constraints
• Determine performance needs
• Gather context about data types

Phase 2 (Architecture): Model Selection

• Analyze requirements for privacy concerns
• Evaluate complexity and coding needs
• Check for vision or specialized requirements
• Select optimal execution mode and model

Phase 3 (Generation): Skill Creation

• Generate workflow steps based on requirements
• Create metadata with architectural decisions
• Build progressive loading folder structure
• Generate YAML frontmatter and templates

Phase 4 (Validation): Quality Assurance

• Verify YAML syntax and structure
• Test skill execution in sandbox
• Validate security constraints
• Ensure proper file organization

📚 References

• Generator Source: ai_os/workflows/enhanced_skill_generator.py
• Executor Source: ai_os/workflows/enhanced_skill_executor.py
• Templates: templates/workflow.yaml
• Architecture Docs: docs/ENHANCED_ARCHITECTURE.md

🔧 Technical Implementation

Model Selection Algorithm

def analyze_requirements(description, context):
    # Privacy Check
    if sensitive_data or latency_critical:
        return LOCAL_ONLY
    
    # Complexity Check
    if coding or vision or complex_reasoning:
        return CLOUD_PREFERRED
    
    # Default
    return HYBRID

Skill Structure Generation

generated_skill/
├── SKILL.md          # YAML frontmatter + content
├── workflow.yaml     # Execution logic
├── examples/         # Usage examples
├── references/       # Heavy context files
└── logs/            # Execution logs

✅ Success Criteria

1. Requirements Gathered: Complete user input collected
2. Architecture Selected: Optimal model and mode chosen
3. Skill Generated: Complete skill package created
4. Validation Passed: YAML syntax and structure verified
5. Test Successful: Skill executes without errors
6. Documentation Complete: All references and examples created

🚨 Error Handling

• Input Validation: Ensure user provides sufficient detail
• Model Selection: Handle edge cases in architecture decisions
• File Creation: Manage permission and path issues
• YAML Validation: Catch and report syntax errors
• Execution Testing: Handle test failures gracefully

🎯 Example Usage

# Start the skill creator
aios run skill-creator

# Example interaction:
> What task would you like to automate?
I need to analyze financial reports and generate summaries

> Architecture Decision: LOCAL_ONLY mode selected (financial data sensitivity)

> Skill Generated: financial-analyzer
> Location: ./skills/financial-analyzer/
> Validation: PASSED
> Test Execution: SUCCESS

This meta-skill enables users to create production-grade AI skills with proper architectural decisions and security constraints.

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