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Agent Skill · AI 工具

spc(程控)

spc 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

spc 是AI 工具类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证为 MIT,第三方扫描已通过,按安装量在AI 工具类的 2,536 个 Skill 里排第 1,513。

总安装 649周安装 26GitHub Stars 1下载 210核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:spc(程控)
来源仓库:https://github.com/robdtaylor/personal-ai-infrastructure
仓库路径:skills/spc
安装命令:
npx skills add https://github.com/robdtaylor/personal-ai-infrastructure --skill Spc
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/robdtaylor/personal-ai-infrastructure --skill Spc

这个 Skill 是做什么的

AI 工具类 Skill 用来扩展助手本身的能力,例如接模型、接检索、接外部服务。

spc 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息。适合在 Codex、Claude、Cursor、Gemini CLI 中围绕仓库状态、代码变更或协作事项进行整理。通过 npx skills add 命令从指定 GitHub 仓库安装使用。

安装前需确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写操作。spc 属于AI 工具类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/spc

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/robdtaylor/personal-ai-infrastructure --skill…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

spc 怎么安装?

在终端执行 npx skills add https://github.com/robdtaylor/personal-ai-infrastructure --skill Spc。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

spc 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

spc 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

spc 是免费的吗?

来源标注的许可证是 MIT。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

spc 用的人多吗?

公开数据是累计安装 649 次,近一周安装 26 次,下载 210 次,GitHub 1 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

spc 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 3 个目录同时收录,说明这个 Skill 流传比较广。
  • 许可证明确为 MIT,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理AI 工具类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

AI 工具类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
executing-plans

执行计划 · Codex、Claude

1,515,336173,110只读MIT通过
verification-before-completion

完成前的验证 · Codex、Claude

1,319,472173,121权限未标注MIT通过
subagent-driven-development

子 Agent 驱动开发 · Codex、Claude

1,312,416173,138只读MIT通过
copy-editing

复制编辑 · Codex、Claude

1,209,57625,632只读MIT通过
paywall-upgrade-cro

付费专区升级 cro · Codex、Claude

1,030,17625,617只读MIT通过
spc

程控 · 本页

6491只读MIT通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Statistical Process Control (SPC)

When to Activate This Skill

  • "Set up SPC for [characteristic]"
  • "Calculate Cpk for [process]"
  • "What control chart should I use?"
  • "Is this process in control?"
  • "Interpret out-of-control pattern"
  • "Conduct capability study"
  • "What's the difference between Cp and Cpk?"

Purpose of SPC

SPC uses statistical methods to monitor, control, and improve processes by distinguishing between:

  • Common cause variation - Normal, inherent process variation
  • Special cause variation - Abnormal, assignable causes requiring action

Why SPC Matters

Without SPC:

  • React only when defects occur
  • Cannot predict process behavior
  • May over-adjust stable processes
  • Miss early warning signs

With SPC:

  • Detect problems before defects
  • Understand process capability
  • Make data-driven decisions
  • Continuously improve

Control Chart Selection

Variable Data Charts (Measurements)

ChartData TypeWhen to Use
X-bar/RSubgroups n=2-9Standard variable control chart
X-bar/SSubgroups n≥10Large subgroups
I-MRIndividual measurementsLow volume, long cycle, destructive test

Attribute Data Charts (Counts/Categories)

ChartData TypeWhen to Use
p chartProportion defectiveVariable sample size, defective/not
np chartCount of defectivesFixed sample size, defective/not
c chartDefects per unitFixed area/unit, count defects
u chartDefects per unitVariable area/unit, count defects

X-bar/R Chart

Setup

ParameterGuideline
Subgroup size (n)3-5 typical, 5 preferred
Subgroup frequencyRational subgrouping - within-subgroup should be homogeneous
Minimum data points20-25 subgroups before calculating limits

Control Limit Formulas

X-bar Chart:

UCL = X̄̄ + A₂ × R̄
CL = X̄̄
LCL = X̄̄ - A₂ × R̄

R Chart:

UCL = D₄ × R̄
CL = R̄
LCL = D₃ × R̄

Constants (A₂, D₃, D₄)

nA₂D₃D₄
21.88003.267
31.02302.575
40.72902.282
50.57702.115
60.48302.004

Individual/Moving Range (I-MR) Chart

When to Use

  • Long cycle time
  • Destructive testing
  • Expensive testing
  • Batch processes

Control Limit Formulas

I Chart:

UCL = X̄ + 2.66 × MR̄
CL = X̄
LCL = X̄ - 2.66 × MR̄

MR Chart:

UCL = 3.267 × MR̄
CL = MR̄
LCL = 0

Out-of-Control Rules

Western Electric Rules (Standard)

RulePatternIndicates
Rule 11 point beyond 3σSudden shift
Rule 28 points in a row on same side of CL (some sources: 9)Process shift/mean shift
Rule 36 points in a row trending (up or down)Trend/drift
Rule 414 points in a row alternating up/downOver-adjustment

Nelson Rules (Extended)

RulePattern
Rule 52 of 3 points beyond 2σ (same side)
Rule 64 of 5 points beyond 1σ (same side)
Rule 715 points in a row within 1σ of CL
Rule 88 points beyond 1σ (both sides)

MNMUK Standard

Use Rules 1-4 (Western Electric) as standard. Apply Nelson rules for critical characteristics or detailed analysis.

Important: Western Electric Rule 2 triggers at 7 or more consecutive points on one side of the centerline (some references cite 8 or 9 — the broadly accepted standard is that 7+ points on one side constitutes an out-of-control signal requiring investigation). A run of 7 consecutive points on one side of the centerline IS an out-of-control condition.

Process Capability

Indices Overview

IndexMeasuresFormula
CpPotential capability (spread)(USL - LSL) / 6σ
CpkActual capability (considers centering)Min(Cpu, Cpl)
PpProcess performance (spread)(USL - LSL) / 6s
PpkProcess performance (considers centering)Min(Ppu, Ppl)

Key Difference: Cp/Cpk vs Pp/Ppk

AspectCp/CpkPp/Ppk
Variation estimateWithin-subgroup (R̄/d₂ or S̄/c₄)Overall (sample std dev)
RepresentsProcess potentialProcess performance
Use whenProcess in controlInitial assessment
TypicallyHigherLower

Capability Formulas

Cp (Process Potential):

Cp = (USL - LSL) / 6σ

Where σ = R̄/d₂ (within-subgroup estimate)

Cpk (Process Capability):

Cpu = (USL - X̄̄) / 3σ
Cpl = (X̄̄ - LSL) / 3σ
Cpk = Min(Cpu, Cpl)

Pp (Process Performance):

Pp = (USL - LSL) / 6s

Where s = sample standard deviation

Ppk (Process Performance Index):

Ppu = (USL - X̄) / 3s
Ppl = (X̄ - LSL) / 3s
Ppk = Min(Ppu, Ppl)

d₂ Constants

nd₂
21.128
31.693
42.059
52.326
62.534

Capability Targets

Automotive Industry Standards

IndexMinimumPreferredFor CC
Cpk1.331.671.67
Ppk1.331.671.67

Interpretation

Cpk ValuePPM (one tail)Interpretation
0.6722,750Poor, not capable
1.001,350Barely capable
1.3332Capable (minimum automotive)
1.503.4Good
1.670.3Very good (CC target)
2.000.001Excellent

Capability Study Process

Step 1: Plan the Study

  • Identify characteristic
  • Select measurement system (verify MSA)
  • Determine sample size (minimum 30, prefer 50-100)
  • Define sampling method

Step 2: Collect Data

  • Collect samples under normal conditions
  • Record in time order
  • Document any special events

Step 3: Analyze Data

  • Create histogram (check distribution)
  • Check normality
  • Calculate statistics
  • Create control chart
  • Check for statistical control

Step 4: Calculate Capability

  • If in control: Calculate Cp, Cpk
  • If not in control: Address special causes first, or report Pp, Ppk only
  • Compare to requirements

Step 5: Interpret and Act

  • Is capability adequate?
  • What actions needed?
  • Document results

Pre-Control (Alternative to SPC)

When to Use Pre-Control

  • Very capable processes (Cpk >1.33)
  • Short runs
  • Quick setup verification
  • Simpler than SPC

Pre-Control Zones

┌─────────────────────────────────────────────┐
│               RED ZONE                       │ → Stop, adjust
├─────────────────────────────────────────────┤
│             YELLOW ZONE                      │ → Caution
├─────────────────────────────────────────────┤
│       GREEN ZONE (Middle 50%)                │ → OK
├─────────────────────────────────────────────┤
│             YELLOW ZONE                      │ → Caution
├─────────────────────────────────────────────┤
│               RED ZONE                       │ → Stop, adjust
└─────────────────────────────────────────────┘
      LSL           Target           USL

Pre-Control Rules

  1. Startup: 5 consecutive in Green = run production
  2. Running:

- Both in Green → Continue - One Yellow → Check again immediately - Both Yellow → Investigate/adjust - Red → Stop, investigate


Output Format

When generating SPC content:

# SPC Analysis

## Characteristic Information
| Field | Value |
|-------|-------|
| **Characteristic** | [Description] |
| **Specification** | [LSL - USL] |
| **Target** | [Nominal] |
| **Chart Type** | [X-bar/R, I-MR, etc.] |

## Control Chart Data
| Subgroup | X̄ (or X) | R (or MR) |
|----------|----------|-----------|
| 1 | | |
| ... | | |

## Control Limits
| Chart | LCL | CL | UCL |
|-------|-----|----|----|
| X-bar | | | |
| R | | | |

## Process Capability
| Index | Value | Requirement | Status |
|-------|-------|-------------|--------|
| Cpk | | ≥1.33 | PASS/FAIL |
| Ppk | | ≥1.33 | PASS/FAIL |

## Assessment
- In Control: Yes / No
- Capable: Yes / No
- Actions Required: [List]

Integration with Related Skills

ControlPlan

Control Plan specifies SPC requirements:

  • Which characteristics require SPC
  • Sample size and frequency
  • Reaction to out-of-control

Load: read ~/.claude/skills/Controlplan/SKILL.md

MSA

SPC validity requires adequate measurement system:

  • ndc ≥5 for meaningful SPC
  • Poor MSA = poor SPC decisions
  • Verify MSA before starting SPC

Load: read ~/.claude/skills/Msa/SKILL.md

AutomotiveManufacturing

Work instructions should include SPC procedures:

  • How to collect data
  • How to plot points
  • How to interpret charts
  • What to do when out of control

Load: read ~/.claude/skills/Automotivemanufacturing/SKILL.md


Supplementary Resources

For detailed guidance: read ~/.claude/skills/Spc/CLAUDE.md

For capability study template: read ~/.claude/skills/Spc/templates/capability-study.md

For control chart selection: read ~/.claude/skills/Spc/reference/control-chart-selection.md

For capability indices: read ~/.claude/skills/Spc/reference/capability-indices.md

For out-of-control rules: read ~/.claude/skills/Spc/reference/out-of-control-rules.md

平台分布

Claude Code

29.69%
按下载量换算62

windsurf

24.17%
按下载量换算51

trae

16.39%
按下载量换算34

OpenCode

10.73%
按下载量换算23

Cursor

7.87%
按下载量换算17

Codex

3.35%
按下载量换算7

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。

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