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data-analysis

Generate statistical analysis code with a 4-round review workflow.

Updated Apr 23, 2026
Search Tags:#statistics#data-analysis#p-values#hypothesis-testing#effect-size#confidence-intervals#experimental-design
One-click install
npx skills add https://github.com/Embers-of-the-Fire/agent-research-skills-opencode --skill data-analysis-embers-of-the-fire
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-analysis
Source: https://github.com/Embers-of-the-Fire/agent-research-skills-opencode/tree/main/.opencode/skills/data-analysis
Command: npx skills add https://github.com/Embers-of-the-Fire/agent-research-skills-opencode --skill data-analysis-embers-of-the-fire

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, scipy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Generates automated statistical analysis code with a structured 4-round review to ensure correct test selection, interpretation, and reporting for experimental data.

Core Features & Use Cases

  • Automatic detection of numeric columns and data types to guide analysis.
  • Automated test selection and execution for common experimental designs, with effect sizes and confidence intervals.
  • End-to-end workflow that outputs a ready-to-run analysis script and a manuscript-ready summary.

Quick Start

Provide a dataset path and a hypothesis, and the skill will generate a complete statistical analysis script and report.

Frequently Asked Questions about data-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate statistical analysis and p-value calculation for experimental data?▼

Automate statistical analysis by providing a dataset path and hypothesis to generate a complete analysis script. The workflow detects numeric columns, computes p-values, effect sizes, and confidence intervals, then outputs structured JSON results.

What is the best way to run hypothesis testing on CSV files for a research paper?▼

Hypothesis testing on CSV files is handled by an automated workflow that recommends appropriate tests for your experimental design. It generates a ready-to-run script and a manuscript-ready summary with effect sizes and confidence intervals.

Can I use numpy and scipy for experimental design analysis with automated test selection?▼

Numpy and scipy are used as dependencies to execute automated test selection and statistical calculations. The workflow applies these libraries to compute effect sizes, p-values, and confidence intervals across experimental data formats.

Does this statistical analysis tool work with JSON and pickle data formats?▼

Statistical analysis is supported across CSV, JSON, pickle, and experiment log formats. You provide your dataset via an input path, and the tool automatically detects numeric columns to guide the analysis process.

How does the 4-round review process ensure correct statistical test selection?▼

The 4-round review process ensures correct statistical test selection by systematically validating test choices, interpretation, and reporting. This structured workflow generates rigorous analysis code while verifying appropriate experimental design application.

What statistical outputs are included in the structured JSON results?▼

Structured JSON results include computed p-values, effect sizes, and confidence intervals for your experimental data. The output provides a manuscript-ready summary alongside the generated statistical analysis script.

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