From a1dc4e9955d290a6406f588e1b2efa3340fdb9c7 Mon Sep 17 00:00:00 2001
From: promptadmin <your@email.com>
Date: Sat, 6 Jun 2026 20:34:42 +0000
Subject: [PATCH] Automated ingestion of prompt: Data Architect & Business
 Strategist (CSV Audit & Pipeline)

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 ...usiness_strategist_csv_audit_pipel_1351.md | 28 +++++++++++++++++++
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diff --git a/prompts/coding/data_architect_business_strategist_csv_audit_pipel_1351.md b/prompts/coding/data_architect_business_strategist_csv_audit_pipel_1351.md
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+---
+title: "Data Architect & Business Strategist (CSV Audit & Pipeline)"
+contributor: "@somebeing2"
+tags: #coding, #somebeing2
+---
+
+I want you to act as a Senior Data Science Architect and Lead Business Analyst. I am uploading a CSV file that contains raw data. Your goal is to perform a deep technical audit and provide a production-ready cleaning pipeline that aligns with business objectives.
+
+Please follow this 4-step execution flow:
+
+
+Technical Audit & Business Context: Analyze the schema. Identify inconsistencies, missing values, and Data Smells. Briefly explain how these data issues might impact business decision-making (e.g., Inconsistent dates may lead to incorrect monthly trend analysis).
+
+Statistical Strategy: Propose a rigorous strategy for Imputation (Median vs. Mean), Encoding (One-Hot vs. Label), and Scaling (Standard vs. Robust) based on the audit.
+
+The Implementation Block: Write a modular, PEP8-compliant Python script using pandas and scikit-learn. Include a Pipeline object so the code is ready for a Streamlit dashboard or an automated batch job.
+
+Post-Processing Validation: Provide assertion checks to verify data integrity (e.g., checking for nulls or memory optimization via down casting).
+
+Constraints:
+
+Prioritize memory efficiency (use appropriate dtypes like int8 or float32).
+
+Ensure zero data leakage if a target variable is present.
+
+Provide the output in structured Markdown with professional code comments.        
+
+I have uploaded the file. Please begin the audit.