robustagile

robustagile / six-sigma-in-r-skill

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AI skill for Claude Code and Codex that helps agents write correct R for Six Sigma and SPC work, including control charts, capability analysis, hypothesis tests, and ANOVA, with a method-selection layer that recommends the right technique from a plain-English problem description.

claude-skills r six-sigma
10
0
100% credibility
Found Apr 21, 2026 at 10 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

A set of reference guides that teach AI assistants to generate precise calculation instructions for Six Sigma quality analysis and statistical process control.

How It Works

1
🔍 Discover the skill

You stumble upon this handy guide that turns your AI helper into a whiz at quality control math for business processes.

2
📥 Add it to your AI

You follow the simple guide to slip this skill into your AI assistant, so it's always ready for stats questions.

3
💬 Describe your problem

You chat with your AI about your data challenge, like 'Check if my factory process is stable' or 'Show defects over time'.

4
✨ AI delivers spot-on advice

Your AI picks the perfect math method for your situation and hands you ready-to-use steps with exact formulas.

5
📋 Copy and run

You paste those clear instructions into your stats software and hit go to crunch the numbers.

📊 Unlock process insights

You get beautiful charts, reports, and clear answers showing exactly how well your process is running.

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AI-Generated Review

What is six-sigma-in-r-skill?

This GitHub skill for Claude Code and compatible agents like Codex turns plain-English Six Sigma queries into correct, runnable R code for SPC analysis, control charts, capability metrics, hypothesis tests, and ANOVA. Instead of guessing formulas or constants, it includes a method-selection layer that picks the right technique based on DMAIC phase or data shape, delivering scripts that plot P charts from CSVs or assess process capability without errors. Developers get reliable base R outputs, with ggplot2 only on request, solving the pain of AI-generated stats code that's often approximate or wrong.

Why is it gaining traction?

It stands out in the GitHub skill directory and skill Claude marketplace by nailing SPC constants like A2/D3 and sequencing analysis steps correctly—benchmarks show it matches or beats bare Claude Opus on tough tasks like non-normal capability or Nelson rules. Users notice the edge in method recommendations for agents, plus verdict-printing hypothesis tests and LCL clamping, making it a go-to for skill Claude Anthropic or skill GitHub Claude setups over generic tools like GitHub Copilot for stats work.

Who should use this?

Quality engineers running process control in R, Six Sigma black belts using AI agents for quick analysis, or manufacturing analysts handling subgrouped Cp/Cpk from production data. Ideal for teams in skill Claude code marketplace experimenting with GitHub agents for DMAIC workflows, especially if you're tired of debugging AI stats scripts.

Verdict

With 10 stars and a 1.0% credibility score, it's early-stage but backed by solid benchmarks and clear docs—grab it from the GitHub skill marketplace if you do R-based SPC via Claude agents. Skip for production unless you verify outputs; otherwise, it's a niche win for analysis pros.

(198 words)

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