AltimateAI/data-engineering-skills

Latest recorded source activity: October 27, 2026 at 06:58

SkillsMP catalog refreshed: November 2, 2026 at 18:02

skills collected: 17

GitHub stars: 133
GitHub forks: 12

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Follow https://skillsmp.com/skill-install/prompt.md to install Agent Skills from https://github.com/AltimateAI/data-engineering-skills.

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# Data Engineering Skills **Claude Code skills for Analytics & Data engineers working with dbt and Snowflake** [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![Claude Code](https://img.shields.io/badge/Claude%20Code-Skills-blueviolet)](https://claude.ai/claude-code) 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](https://github.com/dbt-labs/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 ```bash /plugin marketplace add AltimateAI/data-engineering-skills ``` Install individual skill packs: ```bash # 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](https://docs.altimate.sh) · Source: [AltimateAI/altimate-code](https://github.com/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? ```yaml --- 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? ```markdown # 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](https://docs.myaltimate.com/). 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](kits/dbt-snowflake/) | Complete dbt + Snowflake setup | 15 skills | dbt MCP server | ### Quick Start ```bash # 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](kits/README.md) for the full kit format reference and how to create your own. ## Benchmark Results Evaluated using [ADE-bench](https://github.com/dbt-labs/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](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](https://docs.myaltimate.com/) - [ADE-bench Framework](https://github.com/dbt-labs/ade-bench) - [dbt Slack Channel](https://getdbt.slack.com/archives/C05KPDGRMDW) - [Contact Us](https://app.myaltimate.com/contactus) ## License This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. --- *Built by the team at [Altimate AI](https://altimate.ai/) — Making data engineering delightful.*

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