list clean and compare

Messy spreadsheets with inconsistent formatting, duplicate entries, and hidden errors waste hours of manual work. This skill automates the tedious process of cleaning Excel and CSV files while ensuring data integrity through deterministic Python scripts and detailed change logs.

What it does

• Standardizes formatting by fixing irregular casing, trimming whitespace, and unifying date formats to YYYY-MM-DD.
• Validates data to identify invalid emails, malformed phone numbers, and missing required values.
• Detects duplicates using both exact matches and similarity algorithms to catch typos or near-misses.
• Performs list comparison between two versions to track who is new, who left, and exactly which cells changed.
• Preserves data by never modifying original files and outputting results to a dedicated directory with full audit trails.

How it works

1. Profiling phase where the agent analyzes the file structure to detect headers, column types, and data quality issues.
2. Cleaning phase where the user provides configuration for phone country codes and required fields to generate a cleaned output.
3. Deduplication phase where exact matches are merged and potential typos are flagged for human review.
4. Comparison phase where two lists are mapped and cross-referenced to identify additions, removals, and updates.

Frameworks & tools

The skill uses a standalone Python 3.8+ script utilizing the standard library for deterministic processing. It handles .xlsx and .csv formats natively.

Why this beats prompting it yourself

Standard LLM prompts often hallucinate data or silently truncate large lists when "cleaning" text. This skill uses executable code to ensure every single row is processed accurately with a permanent log of every change made.

Use cases

• Cleaning membership directories for clubs or non-profit associations.
• Reconciling monthly sales reports to find new leads and churned customers.
• Normalizing employee contact lists for HR system migrations.

Known limitations

Does not support legacy .xls files directly; users must save them as .xlsx or .csv first. Requires a Python 3.8 environment for execution.