Data Analysisv1.2.0featured
Explore, clean and analyse tabular data (CSV, Excel, JSON) with code, then report insights with charts and the exact steps taken. Use when the user shares a dataset or asks what the data shows, wants trends, comparisons, anomalies, or a chart.
- Author
- Velonx
- License
- MIT
- Updated
- Sep 25, 2026
Overview
A disciplined analysis loop: understand the data, clean it transparently, answer the actual question, and show the evidence. Every number in the report can be traced to code.
When to Use
- "What does this data show?", "why did X drop in March?", "chart revenue by region"
- Any CSV/Excel/JSON the user wants insights from.
For data in a live database, use sql-analysis.
Installation
velonx install data-analysisGet the skill folder
The skill isgit clone --depth 1 https://github.com/velonx/agent-skills.gitagent-skills/skills/data-analysis/. Or open it on GitHub and download the files.Give SKILL.md to your agent as instructions
Add it to the system prompt, custom instructions or rules file your agent reads. Keep the rest of the folder next to it if the skill refers to examples or scripts.Ask for the task in your own words
The agent follows the skill when your request matches it. You can also name it: “use data-analysis”.
Get the skill folder
The skill isgit clone --depth 1 https://github.com/velonx/agent-skills.gitagent-skills/skills/data-analysis/. Or open it on GitHub and download the files.Claude Code: copy it into a skills folder
Usecp -R agent-skills/skills/data-analysis ~/.claude/skills/.claude/skills/inside a project instead to share it with your team through git.Claude apps: upload it
Zip the folder and upload it under Settings → Capabilities → Skills.Ask for the task in your own words
The agent follows the skill when your request matches it. You can also name it: “use data-analysis”.
Get the skill folder
The skill isgit clone --depth 1 https://github.com/velonx/agent-skills.gitagent-skills/skills/data-analysis/. Or open it on GitHub and download the files.ChatGPT: add it to a GPT or project
PasteSKILL.mdinto the instructions, and upload files from the folder as knowledge if the skill uses them.API: send it as instructions
UseSKILL.mdas the system / developer message, or as agent instructions in your SDK.Ask for the task in your own words
The agent follows the skill when your request matches it. You can also name it: “use data-analysis”.
Get the skill folder
The skill isgit clone --depth 1 https://github.com/velonx/agent-skills.gitagent-skills/skills/data-analysis/. Or open it on GitHub and download the files.Gemini app: create a Gem
PasteSKILL.mdinto the Gem’s instructions.Gemini CLI or API
Add the contents to your project’sGEMINI.md, or pass it as thesystem_instructionin the API.Ask for the task in your own words
The agent follows the skill when your request matches it. You can also name it: “use data-analysis”.
Get the skill folder
The skill isgit clone --depth 1 https://github.com/velonx/agent-skills.gitagent-skills/skills/data-analysis/. Or open it on GitHub and download the files.Add it as a project rule
Create.cursor/rules/data-analysis.mdcand paste the body ofSKILL.md. Use the skill’s description as the rule description so the agent applies it when relevant.Keep the folder in your repo
Only needed if the skill refers to examples or scripts.Ask for the task in your own words
The agent follows the skill when your request matches it. You can also name it: “use data-analysis”.
Agents move these settings between versions — if a path doesn’t match, check your agent’s docs.
Usage
- Clarify the question and what decision it supports. If the user just says "analyse this", propose 3 questions and pick the most useful.
- Profile the data with pandas: shape, dtypes,
head(), missing values per column, duplicates, value ranges, date coverage. - Clean transparently. Log every step (dropped rows, parsed dates, fixed types, outlier handling) with row counts before/after. Never silently drop data.
- Analyse with the simplest method that answers the question: group-bys, rates, period-over-period change, distributions. Use statistics (confidence intervals, tests) only when comparing groups and say what they mean in plain words.
- Chart only where it helps: one message per chart, labelled axes with units, a title that states the finding ("Refunds doubled after the March price change").
- Report:
- Top 3 findings, each with the number and the chart or table behind it.
- Caveats (data gaps, small samples, correlation ≠ causation).
- Cleaning log.
- The code, so the user can rerun it.
Examples
Prompt: "Here's our sales export. Why did Q2 revenue fall?"
The agent profiles 48k rows, finds 3% duplicate orders, removes them, splits revenue by region and product, and shows that the drop is almost entirely one product line in one region after a stock-out — with a chart and the query behind it.
Requirements & compatibility
Your agent needs
- Run code (e.g. Python)
- Read and write local files
Environment: Needs Python 3.10+ with pandas; matplotlib for charts.
Written for
Limitations
- Large files (>1 GB) may need sampling; the agent says when it samples.
- Findings are descriptive; causal claims need experiments or domain knowledge.
Files
Changelog
- 1.2.0 — Cleaning log with before/after row counts.
- 1.1.0 — Chart titles state the finding.
- 1.0.0 — Initial release.
Contributing
This skill lives in velonx/agent-skills. Improvements go through a pull request, reviewed by maintainers.