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algo-mfg-fmeaAlgo 制造厂 FMEA

Agent Skill

algo-mfg-fmea 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。

总安装

367

周安装

15

GitHub Stars

125

下载量

119
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

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:algo-mfg-fmea(Algo 制造厂 FMEA)
来源仓库:https://github.com/asgard-ai-platform/skills
仓库路径:skills/algo-mfg-fmea
安装命令:
npx skills add https://github.com/asgard-ai-platform/skills --skill algo-mfg-fmea
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。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/asgard-ai-platform/skills --skill algo-mfg-fmea

简介

algo-mfg-fmea 实施失效模式与影响分析,系统性识别潜在故障及其后果与控制措施。

  • 采用 Severity、Occurrence、Detection 评分体系,传统 RPN 或新版 Action Priority 进行风险排序。
  • 广泛用于新产品开发、医疗器械与汽车等行业合规性管理,预防重大质量问题。
  • 安装方式:GitHub 仓库;需跨职能团队协作完成功能网络图与探测机制定义。
  • 注意:AIAG-VDA 标准已逐步替代 RPN,建议优先采用 AP 矩阵进行优先级判断。

SKILL.md

FMEA (Failure Mode and Effects Analysis)

Overview

FMEA systematically identifies potential failure modes, their effects, causes, and current controls. Each failure is scored on Severity (S), Occurrence (O), and Detection (D) on 1-10 scales. RPN = S × O × D prioritizes which risks to address first. AIAG-VDA FMEA (2019) replaces RPN with Action Priority (AP) matrix.

When to Use

Trigger conditions:

  • Designing new products/processes and identifying risks proactively
  • Systematically evaluating existing failure modes for prioritization
  • Meeting automotive (IATF 16949) or medical device (ISO 13485) quality requirements

When NOT to use:

  • For root cause analysis of a known problem (use fishbone/5-why)
  • For statistical analysis of defect data (use SPC or Pareto)

Algorithm

IRON LAW: Severity Can NEVER Be Reduced by Design Changes
Severity is determined by the EFFECT on the customer. A brake failure
is always severity 10, regardless of how unlikely or detectable it is.
FMEA reduces risk by: lowering Occurrence (better design/process) or
improving Detection (better testing/inspection). NEVER inflate
Detection scores to lower RPN artificially.

Phase 1: Input Validation

Define scope: Design FMEA (DFMEA) or Process FMEA (PFMEA). Assemble cross-functional team. Prepare: process flow diagram or system block diagram. Gate: Scope defined, team assembled, reference diagrams available.

Phase 2: Core Algorithm

  1. List all potential failure modes for each function/process step
  2. For each failure mode, identify: effect on customer, root cause(s), current prevention controls, current detection controls
  3. Score: Severity (1-10), Occurrence (1-10), Detection (1-10)
  4. Classic RPN: RPN = S × O × D. Prioritize high RPNs.
  5. AIAG-VDA AP: Use the S-O-D combination matrix to assign Action Priority: High, Medium, Low.
  6. Define recommended actions for High-priority items with responsibility and target dates

Phase 3: Verification

Review: are all functions/steps covered? Do severity scores match actual customer impact? Are detection scores realistic (not overly optimistic)? Gate: Complete coverage, realistic scoring, actions assigned for high-priority items.

Phase 4: Output

Return FMEA register with prioritized actions.

Output Format

{
  "fmea_items": [{"failure_mode": "seal leak", "effect": "water damage", "cause": "material degradation", "severity": 8, "occurrence": 4, "detection": 6, "rpn": 192, "ap": "high", "action": "add pressure test at final inspection"}],
  "summary": {"total_modes": 45, "high_priority": 8, "medium": 15, "low": 22},
  "metadata": {"type": "PFMEA", "scope": "assembly line 3"}
}

Examples

Sample I/O

Input: Coffee machine brewing module, function: "heat water to 93°C" Expected: Failure modes: overheating (S=7, O=3, D=4, RPN=84), under-heating (S=5, O=4, D=3, RPN=60), no heating (S=8, O=2, D=2, RPN=32).

Edge Cases

InputExpectedWhy
S=10, any O and DAlways high prioritySafety-critical failures require action regardless of RPN
RPN=100 (S=10,O=1,D=10) vs (S=1,O=10,D=10)Same RPN, very different riskThis is why AIAG-VDA AP replaces pure RPN
No current controlsD=10 (no detection)Honest assessment drives improvement

Gotchas

  • RPN is misleading: RPN=100 from S=10,O=1,D=10 (catastrophic but rare, undetectable) is very different from S=1,O=10,D=10 (trivial but frequent). AIAG-VDA AP matrix addresses this flaw.
  • Scoring consistency: Without calibration, different team members score differently. Use scoring rubrics with examples and calibrate as a team.
  • Detection ≠ prevention: A low Detection score (good detection) doesn't prevent the failure — it only catches it. Prioritize Occurrence reduction over Detection improvement.
  • Living document: FMEA must be updated when design/process changes, new failure data appears, or corrective actions are implemented. A static FMEA provides diminishing value.
  • Scope creep: An FMEA that tries to cover everything becomes unmanageable. Focus on the critical functions or highest-risk areas first.

References

  • For AIAG-VDA AP matrix and scoring tables, see references/aiag-vda-ap.md
  • For S/O/D scoring rubrics, see references/scoring-rubrics.md

适合场景

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02

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03

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能力概览

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能力 2

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能力 3

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能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Claude

34.94%
按下载量换算42

Codex

34.57%
按下载量换算41

Cursor

17.97%
按下载量换算21

Gemini CLI

10.34%
按下载量换算12

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

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来源信息

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