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Agent skill

Experimental Data Analysis

by aipoch in aipoch/medical-research-skills

Statistical analysis and reporting for experimental datasets; use when you need to interpret experimental results, test significance (t-tests/ANOVA), or generate reproducible reports.

MITAuto-check passedData & Analytics

Install Experimental Data Analysis

skills CLI
$ npx skills add aipoch/medical-research-skills --skill experimental-data-analysis -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install aipoch/medical-research-skills experimental-data-analysis --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/aipoch/medical-research-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/'scientific-skills/Data Analysis/experimental-data-analysis' .claude/skills/experimental-data-analysis && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
experimental-data-analysis
GitHub stars
2k
Token cost
~1.1k tokens
SKILL.md length
448 words
Files
6 (incl. scripts, references)
Skills in repo
567
Repo updated
First seen
Licence
MIT

At a glance

Statistical analysis and reporting for experimental datasets; use when you need to interpret experimental results, test significance (t-tests/ANOVA), or generate reproducible reports.

  • Works in 4 steps: Data Preparation → Descriptive Statistics → Inferential Statistics → …
  • You need to interpret experimental results
  • SKILL.md covers When to Use, Key Features, Dependencies and Example Usage, plus 1 more section
  • Runs Python scripts from its folder; calls python

What it does

Experimental Data Analysis is an agent skill from aipoch/medical-research-skills. Statistical analysis and reporting for experimental datasets; use when you need to interpret experimental results, test significance (t-tests/ANOVA), or generate reproducible reports.

Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including scripts and reference files (for example `experimental-analysis_audit_result_v1.json`, `references/reporting-template.md` and `references/stats-method-selection.md`).

It sits in Data & Analytics, covering Statistics and Data analysis. The repository describes itself as: Hundreds of agent skills for medical research, including protocol design, data analysis, evidence insights, and academic writing. The licence is MIT.

When your agent uses it

  • You need to interpret experimental results
  • Test significance (t-tests/ANOVA)
  • Generate reproducible reports

Example prompts

  • “/experimental-data-analysis”

Requirements

  • Python 3

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. Data Preparation
  2. Descriptive Statistics
  3. Inferential Statistics
  4. Assumption Checks and Reporting Standards

What it can do on your machine

Read from SKILL.md and the folder at commit 686e09d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 2 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Experimental Data Analysis loads about 1.1k tokens when it runs, and up to ~1.3k if it reads all its reference files. Until then it costs about 53 tokens; SKILL.md has 448 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~53
When it runs · the whole SKILL.md, loaded when a task matches
~1.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~1.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from aipoch/medical-research-skills at commit 686e09d, republished under its MIT licence (© aipoch). 448 words, ~1,125 tokens.

Download SKILL.mdSave it as .claude/skills/experimental-data-analysis/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.
name
experimental-data-analysis
description
Statistical analysis and reporting for experimental datasets; use when you need to interpret experimental results, test significance (t-tests/ANOVA), or generate reproducible reports.
license
MIT
author
AIPOCH

Source: https://github.com/aipoch/medical-research-skills

When to Use

  • You have experimental results in CSV form and need a reproducible end-to-end analysis workflow (clean → test → report).
  • You need to compare two conditions (independent or paired) and determine statistical significance with effect sizes.
  • You need to compare 3+ groups (one-way) or multiple factors (multi-way) using ANOVA and post-hoc multiple comparisons.
  • You must validate assumptions (normality, homogeneity of variance) and document them in a report.
  • You need standardized run outputs (timestamped run directories) for traceability and auditing.

Key Features

  • Reproducible, run-based execution that writes all artifacts into outputs/runs/<timestamp>/.
  • Data preparation guidance: missing values, outliers, and variable type identification (continuous/categorical; grouping factors).
  • Descriptive statistics: means, standard deviations, confidence intervals, and grouped summary tables.
  • Inferential testing:
    • t-tests (independent/paired) and non-parametric alternatives when assumptions fail.
    • ANOVA (one-way and multi-way) with post-hoc testing (e.g., Tukey).
  • Reporting outputs: test statistics, p-values, effect sizes, tables, charts, and explicit assumption notes.
  • Reference materials for method selection and reporting templates:
    • references/stats-method-selection.md
    • references/reporting-template.md

Dependencies

  • Python 3.10+
  • pandas >= 2.0
  • numpy >= 1.24
  • scipy >= 1.10

Example Usage

The workflow is run-directory based. Initialize a new run, then analyze using the latest run by default.

bash
# 1) Initialize a new run directory with sample inputs/config
python scripts/init_run.py

# 2) Run analysis (uses the latest outputs/runs/<timestamp>/ by default)
python scripts/analyze_experiment.py

Expected directory conventions:

  • A new run directory is created at: outputs/runs/<timestamp>/
  • Configuration file location: outputs/runs/<timestamp>/config.json
  • All intermediate and final artifacts (config, inputs, outputs, figures, tables) must be written inside the run directory.
  • Writing outside the run directory is prohibited.

Implementation Details

Reproducible Run Management
  • Before each execution, run:
    • scripts/init_run.py to create outputs/runs/<timestamp>/ and populate initial inputs/config.
  • Analysis scripts default to the latest run directory under outputs/runs/ unless explicitly overridden (if supported by the script).
Show full SKILL.md (188 more words)Show less
Analysis Pipeline

  1. Data Preparation

    • Handle missing values (e.g., drop, impute, or flag) according to the experimental design.
    • Detect and treat outliers (e.g., robust rules, domain thresholds), documenting any exclusions.
    • Identify variable roles:
      • Outcome variable(s): typically continuous measurements.
      • Grouping factors: categorical condition labels (treatment/control, timepoint, genotype, etc.).

  2. Descriptive Statistics

    • Compute summary metrics per group:
      • Mean, standard deviation, and confidence intervals (commonly 95% CI).
    • Produce grouped summary tables suitable for reporting.

  3. Inferential Statistics

    • Two-group comparisons
      • Use an independent t-test for separate groups.
      • Use a paired t-test for repeated measures / matched pairs.
      • If assumptions are violated, switch to an appropriate non-parametric alternative.
    • Multi-group / multi-factor comparisons
      • Use one-way ANOVA for a single factor with 3+ levels.
      • Use multi-way ANOVA when multiple factors are present.
    • Multiple comparisons
      • Apply post-hoc procedures (e.g., Tukey) after ANOVA when needed.
      • Define and document the multiple-comparison control strategy.

  4. Assumption Checks and Reporting Standards

    • Validate and report:
      • Normality (per group or model residuals, as appropriate).
      • Homogeneity of variance.
    • Report, at minimum:
      • Test statistic, degrees of freedom (if applicable), p-value.
      • Effect size(s) and confidence intervals where applicable.
    • Retain analysis code and random seeds to ensure reproducibility.

© aipoch, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 5 other files (scripts, references) in scientific-skills/Data Analysis/experimental-data-analysis of aipoch/medical-research-skills.

  • SKILL.md
  • experimental-analysis_audit_result_v1.json
  • references/reporting-template.md
  • references/stats-method-selection.md
  • scripts/analyze_experiment.py
  • scripts/init_run.py

Open the folder on GitHubat commit 686e09d

Compare with similar skills

Experimental Data Analysis next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Gwas Catalog Region FetchClawBio/ClawBio1.2k1 repos~3.5kAutomated safety check: PassMIT

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Categories

Questions about Experimental Data Analysis

What does Experimental Data Analysis do?

Statistical analysis and reporting for experimental datasets; use when you need to interpret experimental results, test significance (t-tests/ANOVA), or generate reproducible reports. Experimental Data Analysis is an agent skill from aipoch/medical-research-skills. Statistical analysis and reporting for experimental datasets; use when you need to interpret experimental results, test significance (t-tests/ANOVA), or generate reproducible reports.

When should I use Experimental Data Analysis?

Experimental Data Analysis fits situations like: you need to interpret experimental results; test significance (t-tests/ANOVA); generate reproducible reports.

How do I install Experimental Data Analysis in Claude Code?

Run `npx skills add aipoch/medical-research-skills --skill experimental-data-analysis -a claude-code`. Or copy the skill folder (scientific-skills/Data Analysis/experimental-data-analysis in aipoch/medical-research-skills) into .claude/skills/experimental-data-analysis in your project. Claude Code loads it when a task matches its description.

How do I install Experimental Data Analysis in Codex?

Run `npx skills add aipoch/medical-research-skills --skill experimental-data-analysis -a codex`. Or copy the skill folder (scientific-skills/Data Analysis/experimental-data-analysis in aipoch/medical-research-skills) into .agents/skills/experimental-data-analysis in your project. Codex loads it when a task matches its description.

Can I use Experimental Data Analysis in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add aipoch/medical-research-skills --skill experimental-data-analysis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/experimental-data-analysis, .gemini/skills/experimental-data-analysis, .github/skills/experimental-data-analysis and .opencode/skills/experimental-data-analysis in your project.

What does Experimental Data Analysis need to run?

Going by SKILL.md and its folder, Experimental Data Analysis needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Experimental Data Analysis access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Experimental Data Analysis safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Experimental Data Analysis use?

Experimental Data Analysis is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Experimental Data Analysis use?

About 1.1k tokens (SKILL.md is roughly 4.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 150 tokens, read only when the agent opens those files.

What are the alternatives to Experimental Data Analysis?

Skills that share tags, products or a category with Experimental Data Analysis: Matlab (zLanqing/codex-claude-academic-skills, 4.6k stars), Eqtl Catalogue Region Fetch (ClawBio/ClawBio, 1.2k stars), CSV Data Analysis (5zjk5/prompt-engineering, 127 stars) and Meridian MMM Model Building (google/meridian, 1.6k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Experimental Data Analysis?

aipoch (a GitHub organization) maintains it in aipoch/medical-research-skills, which has 1,974 GitHub stars. The repository holds 567 skills in this directory. The repository was last updated on September 17, 2026.

Source: aipoch/medical-research-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.