3. Data, Science & AI

Enables SAP Cloud SDK for AI integration in JavaScript/TypeScript and Java apps, supporting generative AI features and multiple foundation models.

Guides enterprise AI/ML development on SAP BTP with SAP AI Core, covering generative AI deployment, RAG, orchestration, and model integration.

Configures SAP Master Data Integration (MDI) on SAP BTP for connecting applications like S/4HANA and SuccessFactors, including security and replication setup.

Comprehensive plugin for SAP Datasphere development, enabling data warehouse building, analytic model creation, and data flow configuration across SAP and third-party systems.

Builds data processing pipelines, integrations, and machine learning scenarios using SAP Data Intelligence Cloud's operators and JupyterLab.

Provides command-line utilities for SAP HANA database development, administration, and cloud operations including SQL execution, object inspection, and HDI container management.

Extracts structured website data via Firecrawl API, converting pages to LLM-ready markdown for AI/ML workflows.

Analyzes DQMC simulation data to compute physical observables such as density and spin correlations with error estimates.

Enables systematic DQMC parameter studies for temperature sweeps, phase diagrams, and grid-based quantum simulations.

Provides advanced Dynamical Quantum Monte Carlo (DQMC) features for scientific computing, including unequal-time measurements, analytic continuation, and HDF5 data handling.

Executes Determinant Quantum Monte Carlo (DQMC) simulations on HDF5 files, supporting checkpointing, queue management, and status checks for scientific research.

Creates HDF5 files for Determinant Quantum Monte Carlo (DQMC) simulations with customizable physical parameters including lattice size, U, and mu.

Provides a platform for building, backtesting, and executing algorithmic trading strategies with quantitative finance data handling capabilities.

Designs structured data models with Pydantic schemas, comprehensive validation, and type annotations for reliable data handling.

Enables natural language search across Microsoft 365 data (emails, meetings, Teams, documents) via Work IQ MCP integration.

Orchestrates multi-agent systems for parallel task execution, dependency management, and self-organizing workflows using Claude's TeammateTool and Task system.

Designs and lints AI agent task files using CLEAR and CoVe frameworks to ensure unambiguous, verifiable, and hallucination-resistant instructions.

Guides implementation of Chain of Verification (CoVe) in LLM prompts to ensure factual accuracy and minimize hallucinations.

Guides users in creating and configuring Claude Code agents from scratch or via templates, following Anthropic's 2026 best practices for agent architecture.

Creates custom prompts for AI models, enabling efficient prompt engineering and workflow pattern extraction for LLM applications.

Orchestrates AI code reviews via Codex Ultra/Gemini Ultra and generates structured review reports with status and description.

Explores database schemas, table relationships, and generates ERD diagrams for PostgreSQL, MySQL, and SQLite databases.

Executes and debugs Stata statistical workflows, including .do files, dataset inspection, and graph exports via a local server.

Optimizes user prompts to reduce ambiguity and enhance clarity, resulting in higher-quality outputs from large language models.