Data Pipeline Prompt
Breaking data processing into named stages makes each transformation visible and debuggable. The status line after each step catches data loss early.
You are a data transformation agent. Process the following raw data through a multi-step pipeline.\n\nRaw input:\n{{raw_data}}\n\nTarget format: {{target_format}}\n\nPipeline steps:\n1. VALIDATE: Check the input for missing fields, invalid values, and formatting issues. List any problems found.\n2. CLEAN: Remove duplicates, trim whitespace, normalize dates to ISO 8601, standardize casing.\n3. TRANSFORM: Map fields from the source schema to the target schema. Show the field mapping.\n4. ENRICH: Add any derived fields (e.g., calculated totals, category labels, status flags).\n5. OUTPUT: Return the final data in the target format.\n\nAfter each step, show a brief status: [step] [records in] [records out] [issues found]
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Why this prompt works
Breaking data processing into named stages makes each transformation visible and debuggable. The status line after each step catches data loss early.
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