Expertise Module Details

Expertise modules provide deep knowledge and advanced capabilities in specific technical fields.

Available Expertise Modules

State-of-the-art legal engineering expertise for 2024-2025. A comprehensive approach integrating law, technology, and business processes through automation, smart contracts, RegTech, and AI-driven compliance systems.

Software Engineering

State-of-the-art software engineering expertise based on SWEBOK v4.0 (2024). Comprehensive coverage of modern development practices including DevSecOps, sustainable computing, AI-assisted development, and production quality assurance.

Parallel and Distributed Computing

Advanced parallel and distributed computing expertise for 2024-2025. Covers modern paradigms beyond MapReduce including heterogeneous computing, edge-cloud continuum, quantum-classical hybrid systems, and AI workload distribution.

Machine Learning and AI

Comprehensive ML/AI expertise for 2024-2025 covering the complete lifecycle from experimentation to production. Includes MLOps, Responsible AI, LLMs, AutoML 2.0, and edge AI deployment with a focus on governance and sustainability.

Data Space

Data space construction expertise for 2024-2025 based on GAIA-X and IDS standards. Comprehensive knowledge of data sovereignty, interoperability, and secure data sharing architectures.

Usage

These expertise modules can be combined with other modules to create specialized AI assistants. Each module includes:

  1. YAML Configuration: Defines variables, dependencies, and compatible tasks
  2. Markdown Documentation: Provides detailed implementation guidance and examples

Module Combination Examples

modules:
  - core/role_definition
  - expertise/legal_engineering
  - skills/api_design
  - domains/fintech
  - quality/production

ML Engineer

modules:
  - core/role_definition
  - expertise/machine_learning
  - expertise/software_engineering
  - skills/performance
  - quality/production

Distributed Systems Architect

modules:
  - core/role_definition
  - expertise/parallel_distributed
  - expertise/software_engineering
  - skills/system_design
  - methods/agile

Data Space Builder

modules:
  - core/role_definition
  - expertise/data_space
  - skills/api_design
  - skills/authentication
  - domains/healthcare  # or other industry

Full Stack AI Engineer

modules:
  - core/role_definition
  - expertise/software_engineering
  - expertise/machine_learning
  - skills/ui_ux
  - skills/performance
  - methods/agile

Module Structure

Each expertise module includes:

Variable Customization

Each module provides rich variables that can be customized to fit project needs:

Example: Software Engineering Module

# Development methodology customization
development_methodology: "extreme_programming"
architecture_patterns: ["microservices", "event_driven", "serverless"]
quality_metrics_focus: ["performance", "security", "maintainability"]
ai_assistance_level: "extensive"

Example: Machine Learning Module

# ML/AI task customization
ml_task: "real_time_inference"
model_type: "deep_learning"
deployment: "edge_cloud_hybrid"
mlops_maturity: "advanced"
explainability_requirement: "high"

Best Practices

  1. Selecting Appropriate Modules
    • Analyze project technical requirements
    • Identify required expertise
    • Consider combining multiple expertise modules
  2. Dependency Management
    • Check required/optional dependencies for each expertise module
    • Avoid potentially conflicting modules
  3. Gradual Introduction
    • Start with one expertise module
    • Gradually add other modules
    • Verify operation at each stage

Build advanced AI assistants using expertise modules!