gage-rr-analyzer
Measurement System Analysis (MSA) skill for Gage R&R studies with variance component analysis and measurement adequacy assessment
Best use case
gage-rr-analyzer is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Measurement System Analysis (MSA) skill for Gage R&R studies with variance component analysis and measurement adequacy assessment
Teams using gage-rr-analyzer should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/gage-rr-analyzer/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How gage-rr-analyzer Compares
| Feature / Agent | gage-rr-analyzer | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Measurement System Analysis (MSA) skill for Gage R&R studies with variance component analysis and measurement adequacy assessment
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Gage R&R Analyzer
## Overview
The Gage R&R Analyzer skill provides comprehensive capabilities for conducting Measurement System Analysis (MSA) studies. It supports Gage R&R study design, ANOVA-based variance decomposition, and measurement system adequacy assessment following AIAG guidelines.
## Capabilities
- Gage R&R study design
- ANOVA variance decomposition
- Repeatability analysis
- Reproducibility analysis
- %GRR calculation
- Number of distinct categories
- Measurement bias and linearity
- Acceptance criteria evaluation
## Used By Processes
- SIX-004: Measurement System Analysis
- SIX-002: Statistical Process Control Implementation
- QMS-003: Quality Audit Management
## Tools and Libraries
- Minitab API
- Statistical analysis packages
- Calibration management systems
- Data collection tools
## Usage
```yaml
skill: gage-rr-analyzer
inputs:
study_type: "crossed" # crossed | nested
operators: 3
parts: 10
trials: 3
measurements:
- operator: "A"
part: 1
trial: 1
value: 10.2
# ... additional measurements
tolerance: 1.6 # USL - LSL
outputs:
- variance_components
- percent_grr
- percent_tolerance
- ndc
- acceptance_decision
- detailed_report
```
## Variance Components
| Component | Source | Description |
|-----------|--------|-------------|
| Part-to-Part | Process | Variation between parts |
| Repeatability | Equipment | Variation from repeated measurements |
| Reproducibility | Operator | Variation between operators |
| Part x Operator | Interaction | Operator effect varies by part |
## Acceptance Criteria (AIAG Guidelines)
| %GRR | Decision |
|------|----------|
| < 10% | Acceptable |
| 10% - 30% | Marginal, may be acceptable |
| > 30% | Unacceptable |
## Number of Distinct Categories (ndc)
```
ndc = 1.41 * (Part Variation / GRR)
Interpretation:
- ndc >= 5: Adequate measurement system
- ndc < 5: Measurement system needs improvement
```
## Study Design Requirements
| Study Type | Minimum Parts | Minimum Operators | Minimum Trials |
|------------|---------------|-------------------|----------------|
| Standard | 10 | 3 | 2-3 |
| Attribute | 30 | 3 | 3 |
| Destructive | Use nested design | 3 | 1 |
## Integration Points
- Calibration management systems
- Quality Management Systems
- Statistical analysis software
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