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.
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.
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
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
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 dataFAQ
What is this skill for?
How do I install it?
Do I need to upload a full CSV file?
Will it clean the actual file for me?
How is this different from just eyeballing the spreadsheet myself?
What if my data has no obvious insights?
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