Is the AI Data Engineering skill free?

Yes. The skill itself is free from ancoleman/ai-design-components. SkillProof publishes the install command and an independent test verdict at no cost.

What is the AI Data Engineering SkillProof Score?

8.8/10 — installs cleanly 5/5, triggers reliably 5/5, output vs. baseline 7/10, docs & honesty 5/5.

How do I install AI Data Engineering?

Copy the install command from this page, run it in your terminal, and restart Claude Code. Skills live in ~/.claude/skills/ (global) or .claude/skills/ inside a project.

Can I use AI Data Engineering with Cursor, Copilot, Gemini CLI, Codex or other AI tools?

The SKILL.md format is native to Claude (Claude Code, Desktop, claude.ai). The instructions inside adapt to other assistants: Cursor rules, GitHub Copilot instructions, Windsurf rules, Custom GPTs, AGENTS.md for OpenAI Codex, and GEMINI.md for Google Gemini CLI — our conversion guides cover each, and the free converter on the tools page does the wrapping for you.

AI Data Engineering

RAG pipelines, chunking, embeddings, feature stores and orchestration for AI/ML data systems

by ancoleman · ancoleman/ai-design-components

Tested · Works

Install — copy the command Download source ↗

Test report

Verdict: Tested · Works
Score: 8.8/10
Tested: Jul 21, 2026
Environment: Claude Code 2.x (agent harness)
Upstream re-checked: Aug 10, 2026 · 67b6dee

Fetched SKILL.md and spot-checked 3 referenced paths via raw fetch (scripts/chunk_documents.py, references/rag-architecture.md, examples/langchain-rag/basic_rag.py) — all HTTP 200 with real substantive code, no security smells (grepped for curl|sh, base64, os.system, exfil). Installed into a temp HOME; SKILL.md landed at the correct target path. For output I wrote two chunkers and ran both on a 2232-char sample: the baseline fixed-512-char splitter cut chunk 0 mid-word ("...vector store.\n\nChunking ") with zero overlap, while the skill-following recursive-separator chunker (512/50 per skill body) ended chunk 0 cleanly at the "\n\n" boundary and started chunk 1 with a 50-char overlap, plus structured chunk_id/source/metadata output — a concrete retrieval-quality difference, not a restatement of rules.

Scored on four weighted criteria — install, triggering, output vs. baseline, docs. How scoring works

Installs cleanly 5/5
Triggers reliably 5/5
Output vs. baseline 7/10
Docs & honesty 5/5

What AI Data Engineering does

Guides building data infrastructure for AI/ML systems: RAG pipelines (ingestion, chunking, embedding, retrieval, RAGAS evaluation), feature stores (Feast), embedding pipelines, and orchestration (Dagster, Prefect, Airflow, dbt). Triggers when building RAG or semantic-search backends, setting up ML feature serving, or creating embedding and data-transformation pipelines. Ships reference docs, runnable example projects (LangChain, LlamaIndex, Feast, Dagster), and standalone chunking/evaluation/setup scripts.

How to install AI Data Engineering

git clone --depth 1 https://github.com/ancoleman/ai-design-components.git /tmp/ai-data-engineering-src
mkdir -p ~/.claude/skills
cp -R /tmp/ai-data-engineering-src/skills/ai-data-engineering ~/.claude/skills/ai-data-engineering
# SKILL.md lands at ~/.claude/skills/ai-data-engineering/SKILL.md
# Skill guidance is usable as-is. The bundled example projects and scripts (scripts/chunk_documents.py,
# evaluate_rag.py, setup_qdrant.py) need Python deps to run:
#   pip install langchain langchain-core langchain-openai langchain-voyageai langchain-qdrant qdrant-client ragas datasets feast dagster
# Some examples also need API keys (OPENAI_API_KEY / VOYAGE_API_KEY) and a running Qdrant instance.

Skills live in ~/.claude/skills/ (global) or .claude/skills/ (per-project). Restart Claude Code after installing.

Commands — how to trigger AI Data Engineering

/ai-data-engineering RAG pipelines, chunking, embeddings, feature stores and orchestration for AI/ML data systems

It also activates on plain-language prompts like these:

Design a RAG pipeline with chunking and embeddings
Set up a feature store for our ML models
Orchestrate this data pipeline with Dagster

Frequently asked questions

Is the AI Data Engineering skill free?: Yes. The skill itself is free from ancoleman/ai-design-components. SkillProof publishes the install command and an independent test verdict at no cost.
Does AI Data Engineering work with Claude Code?: We tested it with Claude Code 2.x (agent harness) on Jul 21, 2026. Verdict: Tested · Works. Fetched SKILL.md and spot-checked 3 referenced paths via raw fetch (scripts/chunk_documents.py, references/rag-architecture.md, examples/langchain-rag/basic_rag.py) — all HTTP 200 with real substantive code, no security smells (grepped for curl|sh, base64, os.system, exfil). Installed into a temp HOME; SKILL.md landed at the correct target path. For output I wrote two chunkers and ran both on a 2232-char sample: the baseline fixed-512-char splitter cut chunk 0 mid-word ("...vector store.\n\nChunking ") with zero overlap, while the skill-following recursive-separator chunker (512/50 per skill body) ended chunk 0 cleanly at the "\n\n" boundary and started chunk 1 with a 50-char overlap, plus structured chunk_id/source/metadata output — a concrete retrieval-quality difference, not a restatement of rules.
What is the AI Data Engineering SkillProof Score?: 8.8/10 — installs cleanly 5/5, triggers reliably 5/5, output vs. baseline 7/10, docs & honesty 5/5.
How do I install AI Data Engineering?: Copy the install command from this page, run it in your terminal, and restart Claude Code. Skills live in ~/.claude/skills/ (global) or .claude/skills/ inside a project.
Can I use AI Data Engineering with Cursor, Copilot, Gemini CLI, Codex or other AI tools?: The SKILL.md format is native to Claude (Claude Code, Desktop, claude.ai). The instructions inside adapt to other assistants: Cursor rules, GitHub Copilot instructions, Windsurf rules, Custom GPTs, AGENTS.md for OpenAI Codex, and GEMINI.md for Google Gemini CLI — our conversion guides cover each, and the free converter on the tools page does the wrapping for you.