Harness-engineering harness-data-pipeline

name: harness-data-pipeline version: "1.0.0" description: ETL/ELT patterns, data quality checks, pipeline testing, and data workflow management stability: static cognitive_mode: meticulous-verifier triggers:

• manual
• on_pr
• on_commit platforms:
• claude-code
• gemini-cli
• cursor
• codex tools:
• Bash
• Read
• Write
• Edit
• Glob
• Grep
• emit_interaction cli: command: harness skill run harness-data-pipeline args:
  • name: path description: Project root path required: false
  • name: framework description: "Pipeline framework: dbt, airflow, dagster, prefect. Auto-detected when omitted." required: false
  • name: check-quality description: Run data quality validation rules against pipeline definitions required: false mcp: tool: run_skill input: skill: harness-data-pipeline path: string type: rigid tier: 3 internal: false keywords:
• data pipeline
• ETL
• ELT
• data quality
• pipeline testing
• Airflow
• dbt
• Dagster
• Prefect
• data transformation
• data ingestion
• data warehouse
• BigQuery
• Snowflake stack_signals:
• "dbt/"
• "dbt_project.yml"
• "airflow/"
• "dags/"
• "dagster/"
• "pipelines/"
• "etl/"
• "src//transforms/"
• "models/" phases:
• name: detect description: Identify pipeline framework, DAG structure, data sources, and sink targets required: true
• name: analyze description: Evaluate pipeline patterns, dependency graphs, idempotency, and error handling required: true
• name: validate description: Check data quality rules, schema contracts, freshness SLAs, and test coverage required: true
• name: document description: Generate pipeline documentation, lineage diagrams, and quality check reports required: true state: persistent: false files: [] depends_on: []