Data Workflows

CSV Data Cleaning & Insights Reporter

Reviews a pasted CSV/table sample, flags data quality issues, proposes cleaning steps, and surfaces the top 3 insights worth investigating.

Last updated Jul 11, 2026
FreeClaudeChatGPTCursor
TL;DR

CSV Data Cleaning & Insights Reporter is a free AI skill for data workflows. Reviews a pasted CSV/table sample, flags data quality issues, proposes cleaning steps, and surfaces the top 3 insights worth investigating. It works with Claude, ChatGPT, Cursor and is ready to use out of the box.

Download Skill.md Package

About this skill

CSV Data Cleaning & Insights Reporter takes a sample of tabular data and produces two things: a data quality diagnosis (missing values, inconsistent formatting, likely duplicates, outliers) with specific cleaning steps, and a short list of the most notable patterns or insights worth a closer look. It's built for analysts, marketers, and operators who get handed messy spreadsheets and need to quickly know both 'is this data trustworthy' and 'what does it actually tell me.'

What it does

Given a sample of CSV/tabular data (pasted rows or column descriptions), the skill outputs a data quality report with specific cleaning recommendations, and a top-3 insights summary highlighting the most notable patterns.

What is included

  • Data quality diagnosis (missing values, formatting, duplicates, outliers)
  • Specific, actionable cleaning steps
  • Top-3 insights summary
  • Recommended next analysis steps
  • Works from pasted sample rows, no file upload required

How to use it

1. Download the SKILL.md file
2. Upload it to a new Claude conversation
3. Paste a sample of your CSV/table data (header row + several data rows)
4. Receive a data quality report and top insights

Examples

Example input
name,signup_date,plan,revenue
John Smith,2026-01-05,pro,49
john smith,01/05/2026,Pro,49
Jane Doe,2026-01-06,,120
Bob Lee,2026-01-07,enterprise,
Alice Wu,2026/01/08,pro,49
Example output
DATA QUALITY REPORT:

DUPLICATES: 'John Smith' and 'john smith' with matching date/revenue appear to be the same record with inconsistent casing — likely duplicate. Recommend deduplicating on normalized name + date.

MISSING VALUES: 'plan' missing for Jane Doe; 'revenue' missing for Bob Lee. Recommend flagging for manual follow-up rather than assuming a default value.

FORMATTING INCONSISTENCY: 'signup_date' uses three different formats (2026-01-05, 01/05/2026, 2026/01/08) — must standardize to ISO 8601 (YYYY-MM-DD) before any date-based analysis.

CASING INCONSISTENCY: 'plan' values mix casing ('pro' vs 'Pro') — standardize to lowercase before grouping.

CLEANING STEPS:
1. Normalize all text fields to consistent casing
2. Standardize date format to YYYY-MM-DD
3. Deduplicate on normalized name + date
4. Flag missing plan/revenue rows for manual review rather than auto-filling

TOP 3 INSIGHTS:
1. 'Pro' plan at $49 appears to be the dominant/default plan among these signups
2. Missing revenue on an 'enterprise' signup (Bob Lee) is worth investigating — enterprise deals are typically high-value and shouldn't have blank revenue
3. All signups fall within a single week, suggesting this may be a cohort or campaign-specific export rather than full historical data

FAQ

What is this skill for?
It reviews a sample of tabular data, diagnoses data quality issues, and surfaces the most notable insights worth investigating.
How do I install it?
Upload the SKILL.md file to a Claude conversation and paste a sample of your CSV or table data.
Do I need to upload a full CSV file?
No — pasting the header row plus a representative sample of data rows is enough for the diagnosis and insights.
Will it clean the actual file for me?
It provides specific cleaning steps and recommendations; for large files, pair this with a spreadsheet tool or script to apply the fixes at scale.
How is this different from just eyeballing the spreadsheet myself?
It systematically checks for duplicates, formatting inconsistencies, missing values, and outliers, and pairs that with pattern-level insights in one pass.
What if my data has no obvious insights?
The skill will note when patterns are inconclusive from the sample size given and suggest what additional data would help.

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