Production-grade Agent Skills for data engineering AI agents: 73 workflows, platform presets, safe backfill/replay, Kafka & Spark reliability, MCP observability, and VS Code/JetBrains installers.

Generate production-ready Python and AI stacks by combining frameworks, data engines, tooling, and cloud deployment.

A MCP (Model Context Protocol) server for interacting with dbt.

🐚 Python-powered shell. Full-featured, cross-platform and AI-friendly.

LLM-Driven Extraction of Unstructured Data — Built for API Deployments & ETL Pipeline Workflows

Stop re-explaining your data to your AI every session. The individual-analyst context layer, delivered over MCP (Claude Code / Cursor / Codex).

Production-ready PySpark ETL template for Databricks — medallion architecture, DABs, tests, DQX, CI/CD, and agentic development with Claude Code.

AI agent skills for building, operating and troubleshooting Apache Kafka applications. Topic audit, consumer lag, schema review, security, connectors and DLQ

ktx is an executable context layer for data and analytics agents 🐙 Allow Claude Code, Codex, or other AI agents to query analytical databases accurately and with full context of your company

Zero-config entity resolution feeding a durable identity layer: messy records from any source become stable golden entities, a Customer 360 with provenance, merge/split and audit. Fellegi-Sunter beats hand-tuned Splink. Arrow-native/Rust, 250M rows in 11.2 min. Python + edge TypeScript (WASM), SQL-native in Postgres & DuckDB, 97 MCP tools + REST.