Data Engineering Skills

Claude Code skills for Analytics & Data engineers working with dbt and Snowflake

Altimate Data Skills is a collection of Claude Code skills that encode the workflows and best practices of experienced analytics engineers. These skills transform Claude from a code generator into a capable data engineering assistant.

Key Results

• 53% accuracy on ADE-bench (43 real-world dbt tasks)
• 3x improvement on model creation tasks vs baseline
• 84% pass rate on Snowflake query optimization (62 TPC-H queries, 1TB dataset)
• 3.6x better performance gains vs baseline (16.8% avg improvement vs 4.7%)
• Skills that teach Claude how to work, not just what to write

Quick Start

/plugin marketplace add AltimateAI/data-engineering-skills

Install individual skill packs:

# Install dbt skills
/plugin install dbt-skills@data-engineering-skills

# Install Snowflake skills
/plugin install snowflake-skills@data-engineering-skills

# Install altimate-code delegation skill
/plugin install altimate-code@data-engineering-skills

Available Skills

dbt Skills

Skill	Purpose	Key Behaviors
creating-dbt-models	Model creation	Convention discovery → Write → Build → Verify output
debugging-dbt-errors	Error troubleshooting	Read full error → Check upstream → Apply fix → Rebuild
testing-dbt-models	Schema tests	Study existing test patterns → Match project style
documenting-dbt-models	Documentation	Analyze model → Generate descriptions
migrating-sql-to-dbt	Legacy SQL conversion	Parse SQL → Create proper dbt model
refactoring-dbt-models	Safe restructuring	Track dependencies → Apply changes → Verify downstream
developing-incremental-models	Incremental models	Strategy selection → unique_key design → Handle edge cases
dbt-model-enhancement	Ticket-driven enhancement of one existing model → PR	Ticket as data (allow-list) → non-prod --target → dbt build --select <model>+ → self-review → PR
mssql-to-snowflake-dbt-migration	T-SQL → Snowflake dbt with isolated-schema deploy	Medallion or Data Vault → convert with TRAP-aware rewrites → compile → confirm → build to isolated schema → engine-neutral parity → dashboard

Snowflake Skills

Skill	Purpose	Key Behaviors
finding-expensive-queries	Cost analysis	Find and rank queries by cost/time/data scanned
optimizing-query-by-id	Performance tuning	Optimize using query ID from history
optimizing-query-text	Performance tuning	Profile query → Identify bottlenecks → Apply patterns
snowflake-dbt-model-refactoring	Snowflake-specific safe dbt model refactor	Downstream analysis → parity → outcome-axes report
snowflake-query-optimization	End-to-end query tuning (fetch → rewrite → parity → benchmark)	SELECT-only guard → HASH_AGG + multiplicity parity → cache-off benchmark via INFORMATION_SCHEMA
end-to-end-snowflake-pipeline	Ticket → dbt + orchestration + PR, in one pass	Auto-detect orchestrator (Airflow/Prefect/Dagster) + VCS → non-prod --target + --select → DAG parse → self-review → PR → ticket comment

altimate-code Delegation

Skill	Purpose	Key Behaviors
altimate-code	Hand off data tasks to altimate-code	Verify install → Invoke altimate-code run --yolo non-interactively → Read output file → Summarize for user

Use this skill when a task needs altimate-code's wired-up warehouse tools, column lineage, multi-step data exploration, or its 100+ specialized data tools.

Requires altimate-code: npm install -g altimate-code (Node 20+). Docs: docs.altimate.sh · Source: AltimateAI/altimate-code. The skill will detect a missing install and surface the exact command to the user.

How Skills Work

Skills are markdown files that teach Claude how to approach tasks, not just what syntax to use. Each skill has two parts:

1. Trigger Conditions

When should this skill activate?

---
name: creating-dbt-models
description: |
  Guide for creating dbt models. ALWAYS use this skill when:
  (1) Creating ANY new model (staging, intermediate, mart)
  (2) Task mentions "create", "build", "add" with model/table
  (3) Modifying model logic or columns
---

2. Workflow Instructions

What steps should Claude follow?

# dbt Model Development

**Read before you write. Build after you write. Verify your output.**

## Critical Rules
1. ALWAYS run `dbt build` after creating models - compile is NOT enough
2. ALWAYS verify output after build using `dbt show`
3. If build fails 3+ times, stop and reassess your approach
...

Usage Examples

Skills activate automatically based on your request:

Your Request	Skill Activated
"Create a new orders model"	creating-dbt-models
"Fix this compilation error"	debugging-dbt-errors
"Add tests to the customers model"	testing-dbt-models
"Document the revenue metrics"	documenting-dbt-models
"Create an incremental model for events"	developing-incremental-models
"This query is slow, optimize it"	optimizing-query-text

Combining with Altimate MCP Tools

Skills become even more powerful when combined with Altimate's MCP server. The MCP server provides real-time access to your dbt project and data warehouse:

MCP Tool	What It Provides
dbt_project_info	Project structure, model list, sources
dbt_model_details	Column types, dependencies, compiled SQL
dbt_compile	Compile models without CLI
snowflake_query_history	Recent query executions and stats
snowflake_table_stats	Row counts, clustering info

Kits

Kits bundle skills, MCP servers, and instructions into a single activatable unit. Instead of installing skills one by one, activate a kit to get a complete development setup.

Available Kits

Kit	Description	Skills	MCP
dbt-snowflake	Complete dbt + Snowflake setup	15 skills	dbt MCP server

Quick Start

# Install the kit
altimate-code kit install AltimateAI/data-engineering-skills

# Activate for your project
altimate-code kit activate dbt-snowflake

# Check what's active
altimate-code kit status

See kits/README.md for the full kit format reference and how to create your own.

Benchmark Results

Evaluated using ADE-bench, a framework for evaluating AI agents on analytics engineering tasks. All tests were run using Claude Sonnet 4.5.

Overall Results

Configuration	Accuracy	Tasks Resolved
Baseline Claude (no skills)	46.5%	20/43
Claude + Skills	53.5%	23/43

Results by Task Category

Category	Baseline	With Skills	Improvement
Model Creation	40%	65%	+25 pts
Bug Fixing	60%	70%	+10 pts
Debugging	35%	50%	+15 pts
Refactoring	30%	35%	+5 pts
Analysis	25%	30%	+5 pts

Snowflake Query Optimization (TPC-H SF1000)

Benchmark on TPC-H 1TB dataset (62 queries) testing optimizing-query-text skill. All tests were run using Claude Sonnet 4.5.

Configuration	Pass Rate	Avg Performance Improvement
Baseline Claude (no skills)	77.4% (48/62)	4.7%
Claude + Skills	83.9% (52/62)	16.8% (3.6x better)

Skills provide structured optimization with query profiling, anti-pattern detection, and semantic preservation validation.

Note: This benchmark uses our internal evaluation framework. We plan to open-source it soon with additional evals.

Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

Ideas for contributions:

• New skills for workflows we haven't covered
• Improvements to existing skills based on your team's patterns
• Benchmark results on different datasets
• Bug reports and feature requests

Roadmap

We're actively developing:

• Airflow skills — DAG development, debugging, testing
• Cross-platform migration — dbt to/from SQL Server, Oracle
• Snowflake cost optimization — Warehouse sizing, query patterns
• Data quality workflows — Anomaly detection, freshness checks

Resources

• Altimate MCP Server Docs
• ADE-bench Framework
• dbt Slack Channel
• Contact Us

License

This project is licensed under the MIT License - see the LICENSE file for details.

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Built by the team at Altimate AI — Making data engineering delightful.