3. Data, Science & AI

Debugging toolkit for AI agent workflows in customer support, diagnosing issues like incorrect responses, tool calls, and performance problems across frameworks including LangChain and Claude API.

Calculates phase and group velocity dispersion curves for layered Earth models with Numba-accelerated numerical methods.

Python library for geostatistical analysis, including variogram modeling, kriging, and spatial simulation inspired by GSLIB.

Offers computational tools for geophysical calculations including AVO analysis, fluid substitution, wavelets, and reflectivity in rock physics.

Processes geographic and Cartesian point data into regular grids using machine-learning inspired interpolation and gridding techniques.

Enables efficient handling of N-dimensional labeled arrays for geoscience data, including NetCDF file operations and climate/oceanographic analysis.

Processes and visualizes ground-penetrating radar (GPR) data with GUI and scripting interfaces for geophysical analysis.

Generates stereographic projections for structural geology data using matplotlib, including pole density contours and statistical calculations.

Provides geostatistical analysis tools including variogram computation, kriging interpolation, and spatial correlation analysis via a scikit-learn style API.

Bootstraps lean multi-agent orchestration with beads task tracking for efficient agent delegation, avoiding heavy MCP overhead.

Provides repo-agnostic patterns and style alignment checklist for JAX and Equinox numerical projects in machine learning development.

Generates images via OpenAI and Google APIs, supporting text-to-image, reference images, and aspect ratios for API-based image creation.

Manages MCP servers to discover, execute, and integrate AI tools and resources efficiently for LLM workflows.

Enables efficient semantic search and nearest neighbor queries using vector databases for optimized AI-driven retrieval performance.

Optimizes vector index performance for latency, recall, and memory by tuning HNSW parameters and selecting quantization strategies in AI search systems.

Designs business KPI dashboards with optimal metric selection, visualization best practices, and real-time monitoring patterns for data-driven decision making.

Expert guidance for developing deep learning models, including transformers, diffusion models, and LLMs using PyTorch, Transformers, and Diffusers.

Enables analytics queries for Mixpanel MCP, including segmentation, funnels, retention, and DuckDB-based SQL analysis.

Provides best practices for writing efficient, secure, and maintainable SQL queries.

Enables development and optimization of deep learning models using PyTorch, including transformer architectures and diffusion models for AI applications.

Provides optimized techniques for efficient data manipulation, analysis, and DataFrame operations using the Pandas library in Python.

Expert guidance for building production-ready RAG applications using LlamaIndex, covering vector stores, document processing, and query engines.

Enables data analysis, visualization, and analytics workflows with Python and Jupyter, following industry best practices.

Provides expert guidance for data analysis and visualization in Jupyter Notebooks using pandas, matplotlib, seaborn, and numpy.