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SKILL verified MIT Self-run

Data Engineering

skill-ihatesea69-kiro-kit-data-engineering · by ihatesea69

Design and implement data pipelines, ETL processes, and data infrastructure. Use when building data ingestion, transformation, or storage systems.

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Install

$ agentstack add skill-ihatesea69-kiro-kit-data-engineering

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • ✓ Prompt-injection patterns
  • ✓ Secret / credential exfiltration
  • ✓ Dangerous shell & filesystem operations
  • ✓ Untrusted network calls
  • ✓ Known-malicious package signatures

What it can access

  • ✓ Network access No
  • ✓ Filesystem access No
  • ✓ Shell / process execution No
  • ✓ Environment & secrets No
  • ✓ Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

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Reliability & compatibility

✓ Security review passed
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Declared compatibility

Claude CodeClaude Desktop

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

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About

Data Engineering

Activate this skill when designing data pipelines or working with data infrastructure.

When to Use

  • Building ETL/ELT pipelines
  • Designing data warehouse schemas
  • Implementing streaming data processing
  • Optimizing data storage and retrieval
  • Setting up data quality checks

Core Tools

  • Apache Airflow: Workflow orchestration
  • dbt: SQL-based transformations
  • Apache Spark/PySpark: Distributed processing
  • DVC: Data version control
  • Great Expectations: Data validation

Patterns

# Airflow DAG pattern
from airflow import DAG
from airflow.operators.python import PythonOperator

with DAG("etl_pipeline", schedule="@daily") as dag:
    extract = PythonOperator(task_id="extract", python_callable=extract_fn)
    transform = PythonOperator(task_id="transform", python_callable=transform_fn)
    load = PythonOperator(task_id="load", python_callable=load_fn)
    extract >> transform >> load

Rules

  • Idempotent operations (safe to re-run)
  • Schema validation at pipeline boundaries
  • Incremental processing over full reloads when possible
  • Monitor data freshness and quality metrics
  • Version control data schemas alongside code

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.

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