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AltimateAI
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data-engineering-skills
por AltimateAI · AltimateAI/data-engineering-skills
9 Claude Code skills for analytics engineering: 7 dbt workflows + 2 Snowflake query optimizers. 53% pass on real dbt tasks, 84% on Snowflake tuning.
Skills for the daily grind of analytics engineering. dbt skills cover creating, debugging, testing, documenting, migrating, refactoring, and incremental models. Snowflake skills find expensive queries and optimize either by text or by query_id. Philosophy: 'Read before you write. Build after you write. Verify your output.'
Por que usar
Principais recursos
- 7 dbt skills — create, debug, test, document, migrate, refactor, incremental
- 2 Snowflake skills — find expensive queries, optimize by text/id
- Skills auto-activate based on task context
- Benchmarked: 53% real-world dbt pass rate, 84% Snowflake optimization pass rate
- Includes dependency tracking and output verification steps
Demo ao vivo
Como fica na prática
data-engineering-skill.replay
▶ pronto
Casos de uso
Usos do mundo real: data-engineering-skills
Quando usar: dbt run just failed with a cryptic error and you don't know if it's schema, lineage, or SQL.
Fluxo
-
Feed Claude the error + model
Use debugging-dbt-errors. Here's the stderr and models/marts/fct_orders.sql. Diagnose the root cause — don't guess.✓ Copiado
→ Claude reads upstream refs, diagnoses in order: schema → lineage → SQL
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Apply the fix and verify
Apply the fix and run dbt build --select fct_orders+. Show me the before/after row counts.✓ Copiado
→ Clean run + row count verification
Resultado: Green CI plus a note of the root cause so it doesn't recur.
Armadilhas
- Fixing a symptom downstream when the bug is upstream — The skill enforces an upstream-first diagnosis; don't skip the lineage step
Quando usar: Finance flagged the Snowflake bill and you need to cut it without breaking dashboards.
Pré-requisitos
- Snowflake role with ACCOUNT_USAGE access — ACCOUNTADMIN typically, or a dedicated cost role
Fluxo
-
Identify worst offenders
Use finding-expensive-queries to list the top 20 queries in the past 30 days by credit cost. Group by app/user.✓ Copiado
→ Ranked table with credits, runtime, warehouse
-
Optimize each top one
For the top offender, use optimizing-query-by-id <query_id>. Propose rewrites with estimated savings.✓ Copiado
→ Rewritten SQL + before/after explain plan
-
Validate and deploy
Run the rewrite in a test warehouse — confirm same row count and shape before we swap.✓ Copiado
→ Safe swap candidate
Resultado: A prioritized list of fixes with measurable $ savings.
Armadilhas
- Rewrites change row count silently — Always diff before deploying — the skill enforces this
Quando usar: You've inherited a warehouse of nested CTEs and want them as documented, tested dbt models.
Fluxo
-
Point the skill at the source SQL
Use migrating-sql-to-dbt. Here's proc_monthly_revenue.sql. Convert it to dbt models with refs, documentation, and at least 2 tests per model.✓ Copiado
→ One or more .sql files, schema.yml with docs and tests
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Build and verify
dbt build the new models and compare row counts to the legacy output.✓ Copiado
→ Row counts match within tolerance
Resultado: Legacy logic lives as testable dbt models.
Armadilhas
- Hidden side effects in the proc (UPDATEs) — The skill flags side effects — separate them out, don't blindly convert
Quando usar: A daily model has grown too big for full refresh.
Fluxo
-
Analyze the model
Use developing-incremental-models on models/events.sql. Pick a strategy (merge / insert_overwrite / delete+insert) and justify.✓ Copiado
→ Strategy + unique_key + partition / cluster keys recommended
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Implement and back-fill
Apply the incremental config; outline a safe back-fill plan.✓ Copiado
→ Model + back-fill steps
Resultado: Daily runs that finish in minutes, not hours.
Armadilhas
- unique_key gets duplicates on late data — Use merge and test it
Combinações
Combine com outros MCPs para 10× de alavancagem
Apply the same optimize-by-id pattern to BigQuery expensive queries
Adapt finding-expensive-queries for BigQuery INFORMATION_SCHEMA.JOBS and list top 20.✓ Copiado
data-engineering-skill +
githubOpen a PR per migrated model so each is reviewable
For every migrated model, open a GitHub PR with dbt test output attached.✓ Copiado
Custo e limites
O que custa rodar
- Cota de API
- Snowflake queries cost credits like any other — ACCOUNT_USAGE reads are cheap
- Tokens por chamada
- 5–15k per dbt skill invocation
- Monetário
- Free skill
- Dica
- Run finding-expensive-queries once weekly, not on every session
Segurança
Permissões, segredos, alcance
Escopos mínimos: dbt: read + write to your project Snowflake: ACCOUNT_USAGE for cost skills
Armazenamento de credenciais: dbt profiles.yml / Snowflake key-pair in env; the skill doesn't store secrets
Saída de dados: None from the skill directly
Nunca conceda: SYSADMIN to the Claude session unless absolutely needed
Solução de problemas
Erros comuns e correções
dbt compile succeeds, run fails with column not found
Stale lineage — dbt deps + dbt clean + dbt build --select model+
finding-expensive-queries returns nothing
ACCOUNT_USAGE has ~45min delay; also confirm role has SNOWFLAKE.ACCOUNT_USAGE
Verificar: SHOW GRANTS TO ROLE <role>
Alternativas
data-engineering-skills vs. outros
| Alternativa | Quando usar | Troca |
|---|
| dbt Cloud IDE | You prefer managed UI over terminal | No Claude in the loop |
| SQL query optimizers (Select.dev, etc.) | You want visual query plans | Separate tool, separate context |