Data Analysis

Community

lingzhi227

data-analysis

Generate statistical analysis code with 4-round review. Select appropriate statistical tests, interpret results, and produce analysis reports with p-values, effect sizes, and confidence intervals. Use when analyzing experimental data for a paper.

Overview

Publisher	lingzhi227
Repository	`agent-research-skills`
Skill name	`data-analysis`
Stars	426
Forks	46
Bundled files	3
Links	Skill source Repository

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.
3 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by lingzhi227 on GitHub. Read the source before you install it.

Installation

Install the Data Analysis 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/lingzhi227/agent-research-skills/tree/main/skills/data-analysis
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/lingzhi227/agent-research-skills.git /tmp/agent-research-skills
mkdir -p .claude/skills
cp -r /tmp/agent-research-skills/skills/data-analysis .claude/skills/data-analysis

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

Use it in TypingMind

Enable Data Analysis 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 Analysis 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 Analysis 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 Analysis

Generate rigorous statistical analysis code with multi-round review.

Input

$0 — Data source (CSV, JSON, pickle, or experiment logs)
$1 — Research goal or hypothesis to test

References

4-round code review prompts: ~/.claude/skills/data-analysis/references/review-prompts.md

Scripts

Statistical summary and comparison

bash
python ~/.claude/skills/data-analysis/scripts/stat_summary.py --input results.csv --compare method --metric accuracy --output summary.json
python ~/.claude/skills/data-analysis/scripts/stat_summary.py --input results.csv --describe

Detects data types, recommends tests, runs comparisons, outputs effect sizes and significance stars. Requires numpy, scipy.

Format p-values

bash
python ~/.claude/skills/data-analysis/scripts/format_pvalue.py --values "0.001 0.05 0.23" --format stars
python ~/.claude/skills/data-analysis/scripts/format_pvalue.py --csv results.csv --column pvalue --format latex

Formats p-values with stars, LaTeX notation, or plain text. Stdlib-only.

Workflow

Step 1: Generate Analysis Code

Structure the code with these sections:

# IMPORT — pandas, numpy, scipy, statsmodels, sklearn
# LOAD DATA — Load from original data files
# DATASET PREPARATIONS — Missing values, units, exclusion criteria
# DESCRIPTIVE STATISTICS — Summary tables if needed
# PREPROCESSING — Dummy variables, normalization
# ANALYSIS — Statistical tests per hypothesis
# SAVE ADDITIONAL RESULTS — Extra results to pickle

Step 2: 4-Round Code Review

Round 1 — Code Flaws: Mathematical/statistical errors, wrong calculations, trivial tests
Round 2 — Data Handling: Missing values, units, preprocessing, test choice
Round 3 — Per-Table: Sensible values, measures of uncertainty, missing data
Round 4 — Cross-Table: Completeness, consistency, missing variables

Step 3: Produce Results

Every nominal value must have uncertainty (CI, STD, or p-value)
Statistical tests must be appropriate for the data type
Results must match actual data — never hallucinate

Allowed Packages

pandas, numpy, scipy, statsmodels, sklearn, pickle

Statistical Test Selection

Data Type	Test
Two groups, normal	Independent t-test
Two groups, non-normal	Mann-Whitney U
Paired samples	Paired t-test / Wilcoxon
Multiple groups	ANOVA / Kruskal-Wallis
Categorical	Chi-square / Fisher's exact
Correlation	Pearson / Spearman
Regression	OLS / Logistic / Mixed effects

Rules

Always report p-values for statistical tests
Account for relevant confounding variables
Use inherent package functionality (e.g., formula = "y ~ a * b" for interactions)
Do not manually implement available statistical functions
Access dataframes using string-based column names, not integer indices

Related Skills

Upstream: experiment-code, experiment-design
Downstream: table-generation, figure-generation, backward-traceability
See also: math-reasoning

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Data Analysis AI skill do?

Why use Data Analysis on TypingMind?

Because you install it once and use it with any model. Data Analysis 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 Analysis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/lingzhi227/agent-research-skills/tree/main/skills/data-analysis. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Data Analysis?

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 Analysis?

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

Is the Data Analysis AI skill free?

It is published on GitHub by lingzhi227. Check the repository for licensing terms. 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 Analysis

Input

References

Scripts

Statistical summary and comparison

Format p-values

Workflow

Step 1: Generate Analysis Code

Step 2: 4-Round Code Review

Step 3: Produce Results

Allowed Packages

Statistical Test Selection

Rules

Related Skills

Bundled files

Frequently asked questions

Related AI Skills

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