Data Explorer

Community

MAGA2010

data-explorer

Descriptive analysis and EDA on cleaned data: distributions, missingness mechanism screen, correlations, categorical structure. No p-values for group comparisons.

Overview

Publisher	MAGA2010
Repository	`data-master`
Skill name	`data-explorer`
Stars	55
Forks	1
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by MAGA2010 on GitHub. Read the source before you install it.

Installation

Install the Data Explorer AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/MAGA2010/data-master/tree/main/skills/data-explorer
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/MAGA2010/data-master.git /tmp/data-master
mkdir -p .claude/skills
cp -r /tmp/data-master/skills/data-explorer .claude/skills/data-explorer

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Data Explorer in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Data Explorer on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Data Explorer is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

data-explorer

Stage 2a of the data-master pipeline. The router in ../../SKILL.md owns the end-to-end run; this skill owns one stage and one gate. Read the parent contract before changing anything here: the invariants in ../../SKILL.md (additive-only, types decided once, default-FAIL gates, three-part provenance) are not restated, they are inherited.

When to invoke

After cleaning passes its gate. Run this before inferential statistics so the test choice is informed by the actual distributions, not assumed.

Inputs

outputs/cleaned/cleaned.csv via dm_types.load_cleaned (manifest replayed)
analysis/profile.json

Outputs

analysis/descriptive.json: numeric (mean, SD, SE, median, IQR, range, skew, missing_pct), categorical (levels, counts, mode), missing_mechanism_screen, correlation_pearson

Commands

powershell
python scripts/stats_descriptive.py --clean outputs/cleaned/cleaned.csv --out outputs/analysis/descriptive.json

Gate (default FAIL)

Every numeric variable carries n, missing_pct, mean and median, SD, IQR and skew. Reporting only the mean for a strongly skewed variable is a fail, because the mean is then the least trustworthy number on the page.

Failure modes seen in real runs

Mean without median hides a 3 SD outlier.
A missingness screen presented as a mechanism proof. Little's test describes the pattern; it cannot prove MCAR. The wording must say "screening result".
Pearson only on a monotone-but-curved pair understates the association; Spearman is reported alongside, and its use is recorded.

Do not

do not compute group p-values here -- that is the statistical-analyzer's job;
do not read the cleaned file with pd.read_csv; that re-guesses the types;
do not drop the median because the mean was requested.

Upstream / downstream

previous: ../data-cleaner/SKILL.md
next: ../statistical-analyzer/SKILL.md
contract: ../../references/workflow-contract.md

Frequently asked questions

What does the Data Explorer AI skill do?

Descriptive analysis and EDA on cleaned data: distributions, missingness mechanism screen, correlations, categorical structure. No p-values for group comparisons.

Why use Data Explorer on TypingMind?

Because you install it once and use it with any model. Data Explorer is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Data Explorer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/MAGA2010/data-master/tree/main/skills/data-explorer. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Data Explorer?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Data Explorer?

As many as you like. As long as a model supports skills, you can use Data Explorer with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Data Explorer AI skill free?

Yes. It is published on GitHub by MAGA2010 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

data-explorer

When to invoke

Inputs

Outputs

Commands

Gate (default FAIL)

Failure modes seen in real runs

Do not

Upstream / downstream

Frequently asked questions

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