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/algo-mfg-cpk

\"Calculate Cpk process capability index to assess whether a process meets specification requirements. Use this skill when the user needs to evaluate process capability, compare processes, or determine if quality targets are achievable — even if they say 'can our process meet

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$ npx -y skills add charlieviettq/awesome-agent-skill --skill algo-mfg-cpk --agent claude-code

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  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/algo-mfg-cpk

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\"Calculate Cpk process capability index to assess whether a process meets specification requirements. Use this skill when the user needs to evaluate process capability, compare processes, or determine if quality targets are achievable — even if they say 'can our process meet

SKILL.md

algo-mfg-cpk.SKILL.md
name: "\"algo-mfg-cpk\""
description: "\"Calculate Cpk process capability index to assess whether a process meets specification requirements. Use this skill when the user needs to evaluate process capability, compare processes, or determine if quality targets are achievable — even if they say 'can our process meet spec', 'process capability', or 'Cpk calculation'.\"."
allowed-tools: Bash, Read, Write, Edit, Glob, Grep

Cpk Process Capability Index

Overview

Cpk measures how well a process fits within specification limits, accounting for both variation (spread) and centering. Cpk = min((USL - μ) / 3σ, (μ - LSL) / 3σ). Cpk ≥ 1.33 is typically required; Cpk ≥ 1.67 for critical characteristics. Unlike Cp, Cpk penalizes off-center processes.

When to Use

**Trigger conditions:**

  • Assessing whether a manufacturing process can meet customer specifications
  • Comparing capability across processes, machines, or time periods
  • Qualifying a process for production readiness

**When NOT to use:**

  • When the process is not in statistical control (stabilize first with SPC)
  • For non-normal distributions without transformation

Algorithm

IRON LAW: Cpk Is Only Valid for a STABLE, IN-CONTROL Process
Computing Cpk on an unstable process gives a meaningless number.
The process MUST be in statistical control (per SPC charts) before
capability analysis. An unstable process with Cpk=2.0 today may
produce defects tomorrow when it shifts.

Phase 1: Input Validation

Collect: 100+ measurements from a stable process. Determine: USL, LSL (customer specifications). Verify process is in control (SPC charts show stability). **Gate:** Process in control, specifications defined, 100+ data points.

Phase 2: Core Algorithm

1. Compute process mean: μ = Σxᵢ / n 2. Compute process standard deviation: σ = estimated from R-bar/d₂ or S-bar/c₄ (within-subgroup) — NOT overall std dev 3. Cp = (USL - LSL) / 6σ (potential capability, ignoring centering) 4. Cpk = min((USL - μ) / 3σ, (μ - LSL) / 3σ) (actual capability) 5. Estimate PPM defective from Cpk (e.g., Cpk=1.33 → ~63 PPM)

Phase 3: Verification

Check: Cp vs Cpk difference indicates centering issue (Cp >> Cpk = off-center). Distribution is approximately normal (histogram, normality test). **Gate:** Capability computed, centering assessed, normality verified.

Phase 4: Output

Return capability indices with defect rate estimates.

Output Format

{
  "capability": {"cp": 1.8, "cpk": 1.45, "ppm_defective": 27},
  "centering": {"mean": 50.2, "target": 50.0, "offset_pct": 0.4},
  "specs": {"usl": 55, "lsl": 45, "target": 50},
  "metadata": {"samples": 200, "sigma_method": "rbar_d2", "normality_p": 0.35}
}

Examples

Sample I/O

**Input:** USL=55, LSL=45, μ=50.2, σ=1.5 **Expected:** Cp = (55-45)/(6×1.5) = 1.11. Cpk = min((55-50.2)/4.5, (50.2-45)/4.5) = min(1.07, 1.16) = 1.07. Below 1.33 target.

Edge Cases

| Input | Expected | Why | |-------|----------|-----| | μ exactly at target | Cp = Cpk | Perfectly centered | | μ outside specs | Cpk < 0 | Process mean beyond specification limit | | One-sided spec only | Use Cpk for that side only | e.g., surface finish has only USL |

Gotchas

  • **σ estimation method**: Use within-subgroup σ (R̄/d₂), NOT overall σ. Overall σ includes between-subgroup variation that inflates σ and understates Cpk.
  • **Non-normal data**: Cpk assumes normality. For skewed data (surface finish, concentricity), use Box-Cox transformation or non-parametric capability indices.
  • **Short-term vs long-term**: Cp/Cpk are short-term (within subgroup variation). Pp/Ppk use overall variation (long-term). Customers often want Ppk.
  • **Sample size confidence**: Cpk from 30 samples has wide confidence intervals. Report confidence intervals alongside point estimates.
  • **Cpk ≠ defect-free**: Even Cpk=2.0 has a theoretical defect rate (~0.002 PPM). For ultra-critical applications, higher Cpk or process validation is required.

Scripts

| Script | Description | Usage | |--------|-------------|-------| | `scripts/cpk.py` | Compute Cp, Cpk, Cpm, and PPM defective from process data | `python scripts/cpk.py --help` |

Run `python scripts/cpk.py --verify` to execute built-in sanity tests.

References

  • For Cp/Cpk/Pp/Ppk comparison, see `references/capability-indices.md`
  • For non-normal capability analysis, see `references/non-normal-capability.md`
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