Data Cleaning & Insight Report Skill
Turn messy spreadsheets and CSVs into clean data and clear, business-ready insight reports — without inventing numbers or overstating trends.
Data Cleaning & Insight Report Skill is a free, reviewed AI skill for data workflows. Turn messy spreadsheets and CSVs into clean data and clear, business-ready insight reports — without inventing numbers or overstating trends. It works with ChatGPT, Claude, Gemini and is ready to use out of the box.
- • Cleaning quarterly sales or revenue CSV exports
- • Reviewing customer feedback or survey tables
- • Auditing project tracker spreadsheets for risk
- • Requires the actual data or a described structure — cannot connect to live databases.
- • Small samples can only support directional observations, not confirmed trends.
- • Ambiguous columns without context become open questions, not confident findings.
About this skill
Messy data slows everyone down. This skill takes spreadsheets, CSVs, and tables full of inconsistent formatting, duplicates, and gaps, and turns them into clean, structured data and clear insight reports. It flags every quality issue honestly (missing values, duplicates, inconsistent formats, invalid entries, outliers), recommends a standardized structure, and summarizes what the data actually shows in plain language. Patterns and possible trends are always tied to the specific data provided and qualified by sample size — never fabricated or smoothed over.
What it does
Runs a systematic data quality pass, recommends a cleaned structure, summarizes the dataset in plain language, surfaces data-supported patterns, qualifies trend claims by sample size, and produces cleaning reports, insight summaries, executive reports, or issue logs.
Use cases
- Cleaning quarterly sales or revenue CSV exports
- Reviewing customer feedback or survey tables
- Auditing project tracker spreadsheets for risk
- Preparing exec-ready insight summaries from raw data
- Standardizing inherited or vendor-supplied datasets
- Producing issue logs before a data migration
- Turning ambiguous exports into next-action lists
What is included
- SKILL.md
- workflow.md
- data-quality-checklist.md
- insight-report-framework.md
- output-templates.md
- examples.md
- piskill-page-content.md
How to use it
1. Download the ZIP using the Download button. 2. Unzip the data-cleaning-insight-report-skill folder. 3. Load SKILL.md into your AI assistant as a skill / system prompt. 4. Paste your CSV rows or describe the table, then share your goal or the report format you want.
Examples
Here's our sales export from last quarter, can you clean it up and tell me what's going on? [pasted CSV with inconsistent date formats, a duplicate row, a missing Qty, a missing Customer name, and a negative Qty of -2]
Dataset: Q-sales export Rows Reviewed: 24 Issues Found: 1. Inconsistent date formats across rows 2. Duplicate row (Beta LLC, Widget B) appearing twice 3. Missing Qty value in one row 4. Missing Customer name in one row 5. Negative Qty (-2) — unclear if return or error 6. Inconsistent capitalization in Customer and Product names Recommended Cleaning Actions: - Standardize all dates to YYYY-MM-DD - Remove exact duplicate row - Normalize casing for Customer and Product columns Unresolved Items Needing Clarification: - Are "Acme Co" and "acme co" the same customer? - Does the negative Qty represent a return, or a data entry error? - Should the row with a missing Customer be excluded from any summary? Limitations: - Sample is too small and unresolved to support a sales trend claim — no trend reported.
Known limitations
- Requires the actual data or a described structure — cannot connect to live databases. - Small samples can only support directional observations, not confirmed trends. - Ambiguous columns without context become open questions, not confident findings. - Does not replace a full statistical analysis or BI tool.
Quality and safety notes
- ✓ Every missing value, duplicate, and inconsistency is flagged, not filled in silently
- ✓ Factual observations are separated from interpretive insights
- ✓ Trend claims are qualified by sample size and completeness
- ✓ Assumptions from unclear data are explicitly labeled
- ✓ No invented numbers, statistics, or external data
- Never fills in missing values silently — flags them for review instead.
- Never invents statistics, trends, or external data not present in the input.
- Never claims access to live databases or historical data not shared by the user.