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Skill System Implementation Summary

Overview

The LinX platform now supports a flexible, Claude Code-style dynamic skill system that allows users to create executable skills with actual Python code, not just metadata.

What Changed

1. Database Schema (✅ Completed)

Added new fields to the skills table:

  • skill_type: Type of skill (python_function, api_wrapper, database_query, etc.)
  • code: Python code for function skills (with @tool decorator)
  • config: YAML/JSON configuration for API/DB skills
  • is_active: Whether the skill is currently active
  • is_system: Whether it's a built-in system skill
  • execution_count: Number of times the skill has been executed
  • last_executed_at: Timestamp of last execution
  • average_execution_time: Average execution time in seconds
  • created_by: User who created the skill (for custom skills)
  • updated_at: Last update timestamp

2. New Backend Components (✅ Created)

Files Created:

  • backend/skill_library/skill_types.py - Skill type enums
  • backend/skill_library/templates.py - Pre-built skill templates
  • docs/backend/dynamic-skill-system.md - Complete architecture documentation
  • docs/backend/skill-system-summary.md - This file

Database Migration:

  • backend/alembic/versions/13be0d1967af_add_dynamic_skill_fields.py - Migration applied ✅

Updated Files:

  • backend/database/models.py - Enhanced Skill model
  • .kiro/specs/digital-workforce-platform/tasks.md - Added Phase 2.7 tasks

3. Skill Templates

Five ready-to-use templates are now available:

  1. Web Search - Search the internet using Tavily API
  2. HTTP API Call - Make requests to external APIs
  3. Data Analysis - Analyze data using pandas
  4. File Reader - Read file contents
  5. Calculator - Perform mathematical calculations

Next Steps

Immediate (Phase 2.7.2-2.7.4)

  1. Skill Execution Engine - Implement the actual code execution with sandboxing
  2. API Updates - Add endpoints for:
    • GET /skills/templates - List templates
    • POST /skills/from-template - Create from template
    • POST /skills/{id}/test - Test skill execution
    • POST /skills/{id}/activate - Activate skill
    • POST /skills/{id}/deactivate - Deactivate skill

Frontend (Phase 2.7.5-2.7.7)

  1. Code Editor - Replace rigid form with Monaco editor
  2. Template Selector - Visual template browser
  3. Skill Tester - Test skills before deployment
  4. Enhanced UI - Show skill type, stats, and code preview

Integration (Phase 2.7.8)

  1. Agent Integration - Load skills as LangChain tools dynamically
  2. Hot Reloading - Update skills without restarting agents
  3. Permission System - Check permissions before execution

Architecture Highlights

Code-First Approach

Skills are now defined by actual Python code:

from langchain_core.tools import tool

@tool
def my_skill(param: str) -> str:
    """Skill description.
    
    Args:
        param: Parameter description
        
    Returns:
        Result description
    """
    # Implementation
    return result

Dynamic Tool Creation

The execution engine will:

  1. Parse the Python code
  2. Extract the @tool decorated function
  3. Create a LangChain Tool instance
  4. Execute in a sandboxed environment
  5. Track execution stats

Security

  • All code runs in isolated sandboxes (gVisor/Firecracker/Docker)
  • Static analysis before execution
  • Resource limits (CPU, memory, time)
  • Permission checks
  • Audit logging

Benefits

  1. Flexibility - Users can create any type of skill
  2. LangChain Native - Direct integration with LangChain tools
  3. Type-Safe - Python type hints provide validation
  4. Testable - Built-in testing before deployment
  5. Observable - Execution stats and monitoring
  6. Secure - Sandboxed execution with resource limits

Example Usage

Creating a Skill from Template

POST /api/v1/skills/from-template?template_id=web_search&name=my_search

Creating a Custom Skill

POST /api/v1/skills
{
  "name": "custom_skill",
  "description": "My custom skill",
  "skill_type": "python_function",
  "code": "from langchain_core.tools import tool\n\n@tool\ndef custom_skill(input: str) -> str:\n    return f'Processed: {input}'",
  "interface_definition": {
    "inputs": {"input": "string"},
    "outputs": {"result": "string"},
    "required_inputs": ["input"]
  },
  "dependencies": [],
  "version": "1.0.0"
}

Testing a Skill

POST /api/v1/skills/{skill_id}/test
{
  "inputs": {
    "query": "test search"
  }
}

References

Status

  • ✅ Database schema updated
  • ✅ Skill types defined
  • ✅ Templates created
  • ✅ Documentation written
  • ⏳ Execution engine (next)
  • ⏳ API endpoints (next)
  • ⏳ Frontend UI (next)
  • ⏳ Agent integration (next)