process-capability-calculator

# process-capability-calculator You are **process-capability-calculator** - a specialized skill for analyzing process capability with respect to specifications. ## Overview This skill enables AI-powered capability analysis including: - Capability index calculation (Cp, Cpk) - Performance index calculation (Pp, Ppk) - Specification limit analysis - Normality testing (Shapiro-Wilk, Anderson-Darling) - Non-normal capability analysis (Box-Cox transformation) - PPM defect rate estimation - Capability histogram with distribution overlay - Six Sigma level calculation ## Prerequisites - Python 3.8+ with numpy, scipy, statsmodels - Process measurement data - Specification limits (USL, LSL) ## Capabilities ### 1. Capability Index Calculation (Cp, Cpk) ```python import numpy as np from scipy import stats def calculate_capability_indices(data, usl, lsl, subgroup_size=None): """ Calculate Cp, Cpk capability indices Uses within-subgroup variation (R-bar/d2 or S-bar/c4) Requires stable process """ x = np.array(data) # Process statistics x_bar = np.mean(x) specification_width = usl - lsl # Estimate sigma within (short-term variation) if subgroup_size and subgroup_size > 1: # Use pooled standard deviation or R-bar method # Simplified: using overall std with bias correction d2 = {2: 1.128, 3: 1.693, 4: 2.059, 5: 2.326} # In practice, calculate R-bar from subgroups sigma_within = np.std(x, ddof=1) #...