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Algo Mfg Fmea Agent Skill

Conduct FMEA to systematically identify, prioritize, and mitigate potential failure modes. Use this skill when the user needs to assess product or process risks, prioritize corrective actions, or build a risk register — even if they say 'failure mode analysis', 'risk assessment', 'what could go wrong', or 'RPN calculation'.

8k tokens
context cost
the whole folder, loaded on every use
4
files
instructions only
0
copies elsewhere
how many repositories repackaged it
223
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/asgard-ai-platform/skills --skill algo-mfg-fmea

What comes with it

27 848 bytes besides the instruction
examples/sample_scenario.md
references/aiag-vda-ap.md
references/scoring-rubrics.md

The instruction itself

14 sections, as written by the author

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

  • List all potential failure modes for each function/process step
  • For each failure mode, identify: effect on customer, root cause(s), current prevention controls, current detection controls
  • Score: Severity (1-10), Occurrence (1-10), Detection (1-10)
  • Classic RPN: RPN = S × O × D. Prioritize high RPNs.
  • AIAG-VDA AP: Use the S-O-D combination matrix to assign Action Priority: High, Medium, Low.
  • 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

| Input | Expected | Why |

|-------|----------|-----|

| S=10, any O and D | Always high priority | Safety-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 risk | This is why AIAG-VDA AP replaces pure RPN |

| No current controls | D=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

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How to use it

Copy the folder

Take asgard-ai-platform/algo-mfg-fmea from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

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