3. Data, Science & AI

Provides optimized PostgreSQL patterns for schema design, indexing, query performance, and security based on Supabase best practices.

Framework for developing autonomous AI agents that perform coding tasks, featuring agent harness, subagents, and long-term memory using LangChain.

Converts PDF, Word, images, audio, and other formats to Markdown for LLM processing and text analysis.

Provides OpenAI-compatible interface for Ollama, enabling seamless integration with LangChain, LlamaIndex, and other AI tools.

Official Python library for LLM inference, enabling text generation, chat, embeddings, and model management with streaming support.

Estimates flight duration between airports accounting for winds aloft, using ICAO codes, cruise altitude, and true airspeed.

Automates Power BI data preparation through Pandas data cleaning, DAX measure generation, and Python integration scripts.

Creates premium golden-themed Power BI dashboards with JSON themes and visual configurations for KPIs, charts, and slicers.

Monitors GPU status, VRAM usage, loaded models, and inference performance metrics for Ollama, providing real-time insights for AI model optimization.

Fetches real-time aviation weather data including METAR, TAF, PIREPs, and SIGMETs from NOAA for any global airport using ICAO codes.

Provides foundational knowledge and key concepts of the ai-context-core project for AI context management in LLM applications.

Enables direct REST API interactions with Ollama for LLM model management, chat completion, text generation, and embeddings using the requests library.

Imports GGUF models from HuggingFace into Ollama with direct hf.co pulling and inference capabilities.

Enterprise context management with token budget optimization and state persistence for efficient LLM application development.

Manages database schema migrations for Supabase, enabling version control and application of database changes through command triggers.

Comprehensive kit for authoring Claude Code agent skills, sub-agents, orchestration patterns, memory, and IAM rules, following official documentation.

Governs AI agent development with cost control, security, and monitoring for LLM API calls and AI-driven workflows to maximize customer value.

Provides C++20 numerical computing patterns for matrix operations, iterative solvers, and HPC I/O via MPI-IO and HDF5.

Spawns AI agents to analyze and answer queries on Profit and Loss (P&L) financial datasets, delivering data-driven business insights.

Builds classical ML models from datasets, handling requirement gathering, baseline training, and iterative improvement for classification, regression, time-series forecasting, and anomaly detection.

Automates Power BI data preparation with Pandas cleaning, DAX measure generation, and Python integration scripts.

AI-powered contact management system featuring semantic search, relationship tracking, and multi-platform integration for enhanced user connectivity.

Designs reliable AI agent flows using orchestration patterns including routing, tool use, ReAct, and fallbacks for LLM-based systems.

Creates and registers custom sub-agents for LLM task execution, enabling specialized workflow automation through agent-based systems.