Best Claude Code Skills for Data Engineering

Data engineering tasks — writing SQL, designing schemas, building pipelines, optimizing queries — follow well-known patterns. Skills encode these patterns so Claude Code generates data work that matches your stack instead of producing generic solutions.

Quick Answer: The best Claude Code skills for data engineering include SQL and database skills, ETL and pipeline skills, schema design skills, and data quality and validation skills.

SQL and database skills

SQL skills are high-impact because database conventions vary enormously. A skill that knows you use PostgreSQL with specific naming conventions, indexing strategies, and migration tools produces dramatically better output than one assuming generic SQL.

What a good SQL skill covers:

• Table and column naming conventions (snake_case, prefixes, singular vs plural)
• Index strategy (when to add indexes, composite index patterns)
• Migration format (your migration tool's specific syntax)
• Query optimization patterns (avoiding N+1, using CTEs, window functions)
• Constraint patterns (foreign keys, check constraints, defaults)

Browse data engineering skills on Agensi.

See SKILL.md in action

code-reviewer

Free

Run a structured code review on your recent changes without waiting for a teammate. This skill checks for security vulnerabilities (SQL injection, XSS, authentication bypasses), logic errors, edge cases, performance issues, and style violations.Findings are organized by severity: Critical, Warning, and Suggestion. Each finding includes the file, line number, a description of the issue, and a concrete fix. Use it as a first pass before peer review, or as your only reviewer on solo projects.

Get this skill

git-commit-writer

Free

Stop writing vague commit messages. This skill reads your actual staged diff and generates precise, informative commit messages following the Conventional Commits specification. It detects the commit type (feat, fix, refactor, docs, chore, etc.), identifies the scope from the changed files, flags breaking changes, and suggests splitting commits when multiple logical changes are staged. Works with any git repository.`

Get this skill

designing-hybrid-context-layers

$10

What This Skill DoesMost RAG systems fail silently — not because the model is weak, but because the retrieval architecture assumes every query is a lookup. This skill teaches you to design hybrid context layers that match the retrieval strategy to the query type, so your agent gets the right kind of context every time.Problems It SolvesThe RAG-for-everything trap — routing relational and temporal queries through vector search causes silent structural failure, expensive reranking, and answers that get worse as you add more context.Multi-hop blindness — "Which teams own services that depend on the deprecated API?" is an entity-traversal query, not a lookup. Vector RAG cannot answer it accurately.Missing organizational causation — questions like "What decisions led to this incident?" require a temporal event graph, not a document chunk.Context-reasoning mismatch — good context routed to a weak reasoning tier, or long context with no causal structure, produces hallucinations at scale.What You GetThe skill defines a three-layer context model:Layer 1 — Factual Store (Vector RAG): Single-fact, single-document point queries — the only case where RAG is structurally correct.Layer 2 — Relational Store (Knowledge Graph): Entity relationships, dependency chains, and multi-hop queries that require traversal across linked nodes.Layer 3 — Temporal/Episodic Store (Timeline Index): Event sequences, causal decision chains, and "how did we get here" queries that require timestamped structure.You also get a query router decision tree — a concrete classification step that routes every incoming query to the correct layer before any retrieval begins, plus a phased implementation roadmap for teams migrating from RAG-only systems.Who Should Use ThisTeams building AI agents over enterprise knowledge bases, architecture decision records, incident histories, or any organizational system where the agent must answer relational or causal questions — not just fact lookups.

Get this skill

Browse all skills

ETL and pipeline skills

ETL skills help Claude Code generate data pipeline code that follows your patterns:

• Source extraction (API connectors, file parsers, database reads)
• Transformation logic (cleaning, normalization, deduplication)
• Loading strategy (upserts, append, full refresh)
• Error handling (retry logic, dead letter queues, alerting)
• Scheduling (cron expressions, orchestrator config)

Schema design skills

Schema design is where AI assistance is most valuable — Claude can suggest normalized structures, identify potential performance issues, and generate migrations. A schema design skill that knows your conventions produces schemas that fit your existing database without refactoring.

Data quality and validation

Data quality skills instruct Claude to add validation at every stage of a pipeline:

• Input validation (schema checks, data type enforcement, null handling)
• Transformation validation (row count verification, value range checks)
• Output validation (referential integrity, business rule checks)
• Monitoring (data freshness alerts, volume anomaly detection)

Building a data engineering skill

---
name: data-standards
description: Enforces data engineering standards for SQL, schemas, and data pipelines.
---

# Data Engineering Standards

## SQL
- PostgreSQL syntax (not MySQL/MSSQL)
- snake_case for all identifiers
- Singular table names (user, not users)
- Always include created_at and updated_at timestamps
- Use UUIDs for primary keys, not auto-increment

## Migrations
- Use Prisma migrate format
- One migration per logical change
- Always include down migration
- Test migrations against staging before production

## Queries
- Use CTEs for complex queries (no nested subqueries)
- Always add indexes for foreign keys
- Use EXPLAIN ANALYZE to verify query plans
- Batch operations over 1000 rows

---

Find data engineering skills at Agensi.