Data Cleaning Architect

Name: Data Cleaning Architect
Price: 9 USD
Availability: InStock
Author: Agensi

by Tommi Sullivan

Clean messy tabular data with a profile-first workflow, audit logs, and reproducible pandas scripts.

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See it in action

You say

I have a messy customers.csv with weird date formats, some duplicate names with different emails, and weird characters in the addresses. Clean it up.

Your agent does

Profiled customers.csv: Found 3 date formats and 12 fuzzy duplicates. Proposed plan:

Normalize dates to ISO 8601.
Dedupe names (threshold <2).
Fix encoding (mojibake). Generated: customers_cleaned.csv, audit_log.md, and cleaning_script.py. Review 12 flagged duplicates in log.

What you get

Identify data anomalies and mixed types before running transformations.Standardize inconsistent date, currency, and category formats.Quarantine failed row coercions to prevent silent data loss.Generate an audit log documenting every modification and row count delta.Export Python scripts to automate future cleaning of the same data source.

About this skill

The problem

Manual data cleaning is error-prone, slow, and often results in silent data loss. Developers frequently waste hours writing one-off scripts to handle inconsistent date formats, encoding issues, and hidden null values without a clear audit trail.

What it does

Generates a comprehensive data profile identifying mixed types, encoding damage, and structural anomalies before any modifications.
Executes a disciplined cleaning pipeline including type coercion, whitespace normalization, and ISO 8601 date standardization.
Implements a quarantine system for failed coercions rather than silently converting values to null.
Flags fuzzy duplicates for manual review instead of performing risky auto-merges.
Produces a reproducible Python/pandas script for recurring datasets.

Frameworks & tools

Python, Pandas, CSV, Excel (XLSX), JSON.

Why this beats prompting it yourself

Standard LLM prompts often hallucinate fixes or silently delete rows they don't understand. This skill enforces a strict profile-first workflow, maintains an audit log of every change, and ensures row-count integrity that basic prompting lacks.