ai-data-engineering
Data pipelines, feature stores, and embedding generation for AI/ML systems. Use when building RAG pipelines, ML feature serving, or data transformations. Covers feature stores (Feast, Tecton), embedding pipelines, chunking strategies, orchestration (Dagster, Prefect, Airflow), dbt transformations, data versioning (LakeFS), and experiment tracking (MLflow, W&B).
Packaged view
This page reorganizes the original catalog entry around fit, installability, and workflow context first. The original raw source lives below.
Install command
npx @skill-hub/cli install ancoleman-ai-design-components-ai-data-engineering
Repository
Skill path: skills/ai-data-engineering
Data pipelines, feature stores, and embedding generation for AI/ML systems. Use when building RAG pipelines, ML feature serving, or data transformations. Covers feature stores (Feast, Tecton), embedding pipelines, chunking strategies, orchestration (Dagster, Prefect, Airflow), dbt transformations, data versioning (LakeFS), and experiment tracking (MLflow, W&B).
Open repositoryBest for
Primary workflow: Analyze Data & AI.
Technical facets: Full Stack, Data / AI.
Target audience: Development teams looking for install-ready agent workflows..
License: Unknown.
Original source
Catalog source: SkillHub Club.
Repository owner: ancoleman.
This is still a mirrored public skill entry. Review the repository before installing into production workflows.
What it helps with
- Install ai-data-engineering into Claude Code, Codex CLI, Gemini CLI, or OpenCode workflows
- Review https://github.com/ancoleman/ai-design-components before adding ai-data-engineering to shared team environments
- Use ai-data-engineering for development workflows
Works across
Favorites: 0.
Sub-skills: 0.
Aggregator: No.