3. Data, Science & AI

Performs matrix operations, FFT, and linear algebra computations using NumPy for marine engineering numerical analysis.

Generates interactive data visualizations using Apache ECharts, offering customizable charts with a user-friendly API.

Builds searchable document knowledge bases using PDF extraction, OCR, vector embeddings, and semantic search.

Processes marine engineering data workflows, including time series analysis and OrcaFlex results, using Pandas for efficient data manipulation.

Generates enterprise-grade interactive charts using the Highcharts library for data visualization.

Performs hydrodynamic analysis for offshore structures using Boundary Element Method (BEM), Response Amplitude Operators (RAOs), and wave load calculations.

Analyzes 30 days of cross-repository git history using RAG to extract patterns and automatically generate new development skills.

Generates interactive HTML reports with Plotly visualizations from data analysis, featuring dashboards and professional styling for data-driven insights.

Enables design of multi-agent systems using supervisor, peer-to-peer, and hierarchical patterns for coordination and context isolation.

Processes PDF documents using Gemini AI for content analysis and data extraction.

Grounds AI responses in real-time location data using Google Maps for contextually accurate outputs.

Generates music in real-time using AI and provides control over the generation process.

Provides programmatic access to The Movie Database (TMDB) API for movie metadata, cast, crew, and image data.

Enables semantic search over user documents using RAG for efficient information retrieval and context-aware results.

Extracts textual content from PDF documents for data processing and analysis in data science workflows.

Executes and manages 26 Genkit AI flows via MCP with 90% caching, reducing token usage by 70-90% for resume analysis, cover letters, and multi-step AI workflows.

Automates podcast audio generation via NotebookLM Enterprise API on Google Cloud, handling notebook creation, source uploads, and custom focus prompts.

Optimizes token usage and context window management for AI models during long or complex sessions.

Extracts structured medical/surgical relationships from neurosurgical text using NLP, returning relationship triples in JSON format.

Provides AI-driven medical question answering using a neurosurgical knowledge graph, offering both partial and synthesized responses from community-sourced summaries.

Generates structured summaries from neurosurgical knowledge graphs, including title, summary, key entities, and findings.

Creates custom subagents for specialized AI tasks, enabling task-specific agents, code reviewers, debuggers, and domain-specific assistants with tailored prompts.

Extracts metadata from SPSS .sav files and outputs as JSON using the pyreadstat library.

Facilitates knowledge base management with document ingestion, semantic search, and RAG-powered LLM responses for internal Q&A systems.