CSV/JSON Structural Standardizer - Image 1

CSV/JSON Structural Standardizer

Legacy Code Refactorer
Missing Data Imputation Expert

What This Prompt Does:

Convert messy, inconsistent data structures into a unified format. This prompt handles varied date formats, mismatched headers, and inconsistent naming conventions to ensure your datasets are ready for analysis.

Key Tips for Best Results:

  • Upload a small sample of your target schema alongside the messy data.
  • Mention the preferred date format (ISO-8601, MM/DD/YYYY, etc.).
  • Identify any specific escape characters used in your original file.
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Description

About This Prompt: CSV/JSON Structural Standardizer

Optimized for:
ChatGPT
Claude
Gemini
Grok
Verified precision.

How To Use The Prompt:

  1. Copy the entire prompt from the window and paste it directly into ChatGPT, Claude, or Gemini.
  2. Locate and replace the main placeholders with your specific details: [INPUT DATA], [TARGET SCHEMA], and [OUTPUT FORMAT].
  3. Input Example: Use the following format for your input:

Example Input: Source: Mixed JSON/CSV, Target: Standardized JSON for API, Date Format: YYYY-MM-DD

Additional Information:

This prompt is a lifesaver for developers working with legacy exports or third-party APIs that don’t follow modern standards.

This prompt provides the foundation to:

  • Consistency: Forces all entries into a strict, predefined structure.
  • Date Normalization: Fixes one of the most common data headaches automatically.
  • Error Reduction: Identifies and flags structural anomalies that would break your code.

Safety Note: As a professional colleague, always review AI-generated assessments for potential bias or inaccuracies before making final business or management decisions.

PROMPT WINDOW

You are a Senior Data Engineer specializing in ETL processes. Your task is to transform the provided raw [INPUT DATA] into a standardized [OUTPUT FORMAT] that strictly follows this [TARGET SCHEMA].

Please normalize all field names to snake_case, ensure all date fields follow the YYYY-MM-DD format, and remove any leading/trailing whitespace from string values. If the data contains null values, replace them with ‘N/A’ or appropriate defaults. Provide the final result in a code block.