Skip to main content
fmea-facilitator Failure Mode and Effects Analysis facilitation skill for risk identification and prioritization.
Ir para a instalação Skills Marketplace Descubra e explore skills de IA criadas pela comunidade.
Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.
Mostrar detalhes do prompt Um comando direto ignora o prompt de revisão. Verifique a origem antes de executá-lo.
npx skills add https://github.com/a5c-ai/babysitter --skill fmea-facilitatorO comando permanece em uma só linha. Role horizontalmente para revisá-lo antes de copiar.
Prefere uma cópia local? Baixe os arquivos disponíveis atualmente no SkillsMP.
Baixar Zip Baixando... Mais deste repositório Reference for querying the Atlas knowledge graph through its MCP tools — the SECONDARY enrichment/comparison layer that adds best-practice context to systems you have ALREADY scanned from your real sources (`az`, repos, dirs). Use when you need to look up nodes, edges, kinds, clusters, stats, or wiki pages in Atlas to compare against your real inventory. (atlas graph, query atlas, atlas mcp, search the graph, graph neighbors, atlas record, atlas kinds, enrichment layer)
Atlas turns your STATED NEED into a real systems atlas by SCANNING your actual sources (Azure via `az`, git repos, local dirs) and process/data mining them, THEN enriching against the Atlas knowledge graph. Use this skill when asked to inventory/map your real systems, scan your cloud + repos + directories, mine the real processes or data they contain, or collect their real constraints/gotchas. (atlas, scan my systems, inventory our azure account, map my repos, real systems atlas, process mining, data mining, collect nuances, system discovery)
assimilate-popular-workflows This skill should be used when the user asks to "find skills in the wild", "assimilate popular workflows", "discover SKILL.md files in repos", "research external skills", "find workflow patterns", "survey the skill landscape", "what skills exist out there", or wants to investigate public repositories for extractable processes, babysitter plugins, and reusable procedural insights. Searches GitHub for SKILL.md files, classifies repos by archetype, and maintains structured research under docs/reference-repos/.
Ocupações relacionadas SOC
Baseado na classificação ocupacional SOC
name fmea-facilitator description Failure Mode and Effects Analysis facilitation skill for risk identification and prioritization. allowed-tools Bash(*) Read Write Edit Glob Grep WebFetch metadata {"author":"babysitter-sdk","version":"1.0.0","category":"quality-engineering","backlog-id":"SK-IE-018"} graph {"domains":["domain:industrial-engineering"],"skillAreas":["skill-area:statistical-analysis","skill-area:organizational-design","skill-area:data-analysis"],"roles":["role:operations-analyst","role:research-engineer"]}
fmea-facilitator
You are fmea-facilitator - a specialized skill for facilitating Failure Mode and Effects Analysis for risk identification and prioritization.
Overview
This skill enables AI-powered FMEA including:
FMEA scope and boundary definition
Failure mode brainstorming facilitation
Severity, Occurrence, Detection rating guidance
RPN (Risk Priority Number) calculation
AIAG-VDA Action Priority (AP) methodology
Control plan integration
Recommended action tracking
FMEA revision and living document management
Capabilities
1. FMEA Structure
from dataclasses import dataclass, field
from typing import List , Optional
from datetime import datetime
from enum import Enum
class FMEAType (Enum ):
DESIGN = "DFMEA"
PROCESS = "PFMEA"
SYSTEM = "SFMEA"
@dataclass
class FailureMode :
id : str
item_function: str
potential_failure_mode: str
potential_effects: List [str ]
severity: int
potential_causes: List [str ]
occurrence: int
current_controls_prevention: List [str ]
current_controls_detection: List [str ]
detection: int
recommended_actions: List [str ] = field(default_factory=list )
responsibility: str = ""
target_date: Optional [datetime] = None
actions_taken: str = ""
severity_after: Optional [int ] = None
occurrence_after: Optional [int ] = None
detection_after: Optional [int ] = None
@property
def rpn (self ):
return self .severity * self .occurrence * self .detection
@property
def rpn_after (self ):
if all ([self .severity_after, self .occurrence_after, self .detection_after]):
return self .severity_after * self .occurrence_after * self .detection_after
return None
@dataclass
class FMEA :
fmea_type: FMEAType
item_name: str
revision: str
team_members: List [str ]
start_date: datetime
failure_modes: List [FailureMode] = field(default_factory=list )
def add_failure_mode (self, fm: FailureMode ):
self .failure_modes.append(fm)
def get_high_rpn_items (self, threshold=100 ):
return [fm for fm in self .failure_modes if fm.rpn >= threshold]
2. Severity Rating Guide SEVERITY_RATINGS = {
10 : {
"effect" : "Hazardous without warning" ,
"description" : "Very high severity ranking when potential failure mode affects safe operation without warning" ,
"criteria" : "May endanger operator; Noncompliance with regulations"
},
9 : {
"effect" : "Hazardous with warning" ,
"description" : "Very high severity ranking when potential failure mode affects safe operation with warning" ,
"criteria" : "May endanger operator with warning"
},
8 : {
"effect" : "Very High" ,
"description" : "Product inoperable, loss of primary function" ,
"criteria" : "100% of product affected, customer very dissatisfied"
},
7 : {
"effect" : "High" ,
"description" : "Product operable but performance level reduced" ,
"criteria" : "Most customers dissatisfied"
},
6 : {
"effect" : "Moderate" ,
"description" : "Product operable but comfort/convenience items inoperable" ,
"criteria" : "Customer experiences discomfort"
},
5 : {
"effect" : "Low" ,
"description" : "Product operable but comfort/convenience items reduced" ,
"criteria" : "Customer somewhat dissatisfied"
},
4 : {
"effect" : "Very Low" ,
"description" : "Fit and finish items do not conform, noticed by most customers" ,
"criteria" : "Defect noticed by most customers"
},
3 : {
"effect" : "Minor" ,
"description" : "Fit and finish items do not conform, noticed by average customers" ,
"criteria" : "Defect noticed by average customers"
},
2 : {
"effect" : "Very Minor" ,
"description" : "Fit and finish items do not conform, noticed by discriminating customers" ,
"criteria" : "Defect noticed by discriminating customers"
},
1 : {
"effect" : "None" ,
"description" : "No discernible effect" ,
"criteria" : "No effect"
}
}
def get_severity_guidance (context="process" ):
"""Return severity rating guidance"""
return SEVERITY_RATINGS
3. Occurrence Rating Guide OCCURRENCE_RATINGS = {
10 : {"probability" : "Very High" , "rate" : ">= 100 per 1000" , "cpk" : "< 0.33" },
9 : {"probability" : "High" , "rate" : "50 per 1000" , "cpk" : ">= 0.33" },
8 : {"probability" : "High" , "rate" : "20 per 1000" , "cpk" : ">= 0.51" },
7 : {"probability" : "Moderately High" , "rate" : "10 per 1000" , "cpk" : ">= 0.67" },
6 : {"probability" : "Moderate" , "rate" : "5 per 1000" , "cpk" : ">= 0.83" },
5 : {"probability" : "Moderate" , "rate" : "2 per 1000" , "cpk" : ">= 1.00" },
4 : {"probability" : "Moderately Low" , "rate" : "1 per 1000" , "cpk" : ">= 1.17" },
3 : {"probability" : "Low" , "rate" : "0.5 per 1000" , "cpk" : ">= 1.33" },
2 : {"probability" : "Very Low" , "rate" : "0.1 per 1000" , "cpk" : ">= 1.50" },
1 : {"probability" : "Remote" , "rate" : "<= 0.01 per 1000" , "cpk" : ">= 1.67" }
}
4. Detection Rating Guide DETECTION_RATINGS = {
10 : {
"likelihood" : "Almost Impossible" ,
"description" : "No known controls to detect failure mode" ,
"criteria" : "Cannot detect or not checked"
},
9 : {
"likelihood" : "Very Remote" ,
"description" : "Controls probably will not detect" ,
"criteria" : "Control achieved with indirect or random checks only"
},
8 : {
"likelihood" : "Remote" ,
"description" : "Controls have poor chance of detection" ,
"criteria" : "Control achieved with visual inspection only"
},
7 : {
"likelihood" : "Very Low" ,
"description" : "Controls have poor chance of detection" ,
"criteria" : "Control achieved with double visual inspection"
},
6 : {
"likelihood" : "Low" ,
"description" : "Controls may detect" ,
"criteria" : "Control achieved with charting methods (SPC)"
},
5 : {
"likelihood" : "Moderate" ,
"description" : "Controls may detect" ,
"criteria" : "Control based on variable gauging after parts leave station"
},
4 : {
"likelihood" : "Moderately High" ,
"description" : "Controls have good chance to detect" ,
"criteria" : "Error detection in subsequent operations"
},
3 : {
"likelihood" : "High" ,
"description" : "Controls have good chance to detect" ,
"criteria" : "Error detection at station (gauging)"
},
2 : {
"likelihood" : "Very High" ,
"description" : "Controls almost certain to detect" ,
"criteria" : "Error detection at station (automatic gauging)"
},
1 : {
"likelihood" : "Almost Certain" ,
"description" : "Controls certain to detect" ,
"criteria" : "Error proofing - cannot produce discrepant part"
}
}
5. AIAG-VDA Action Priority (AP) def calculate_action_priority (severity, occurrence, detection ):
"""
Calculate Action Priority per AIAG-VDA FMEA handbook
Returns: 'H' (High), 'M' (Medium), 'L' (Low)
"""
if severity >= 9 and occurrence >= 4 :
return "H"
if severity >= 9 and detection >= 7 :
return "H"
if severity >= 5 and occurrence >= 7 and detection >= 5 :
return "H"
if severity <= 4 and occurrence <= 3 :
return "L"
if severity <= 4 and detection <= 3 :
return "L"
if severity <= 6 and occurrence <= 2 and detection <= 4 :
return "L"
return "M"
def get_ap_table ():
"""
Return AP lookup table summary
"""
return {
"H" : {
"description" : "High Priority" ,
"action" : "Required - Must take action to improve controls" ,
"review" : "Management attention required"
},
"M" : {
"description" : "Medium Priority" ,
"action" : "Recommended - Should take action to improve controls" ,
"review" : "Team decision"
},
"L" : {
"description" : "Low Priority" ,
"action" : "Optional - May take action to improve controls" ,
"review" : "Team discretion"
}
}
6. FMEA Analysis and Reporting def analyze_fmea (fmea: FMEA ):
"""
Analyze FMEA and generate summary report
"""
analysis = {
"total_failure_modes" : len (fmea.failure_modes),
"rpn_statistics" : {},
"ap_distribution" : {"H" : 0 , "M" : 0 , "L" : 0 },
"top_risks" : [],
"actions_status" : {
"pending" : 0 ,
"in_progress" : 0 ,
"completed" : 0
}
}
rpns = [fm.rpn for fm in fmea.failure_modes]
if rpns:
analysis["rpn_statistics" ] = {
"max" : max (rpns),
"min" : min (rpns),
"average" : sum (rpns) / len (rpns),
"above_100" : sum (1 for r in rpns if r >= 100 ),
"above_200" : sum (1 for r in rpns if r >= 200 )
}
for fm in fmea.failure_modes:
ap = calculate_action_priority(fm.severity, fm.occurrence, fm.detection)
analysis["ap_distribution" ][ap] += 1
sorted_fms = sorted (fmea.failure_modes, key=lambda x: x.rpn, reverse=True )
analysis["top_risks" ] = [
{
"id" : fm.id ,
"failure_mode" : fm.potential_failure_mode,
"rpn" : fm.rpn,
"severity" : fm.severity,
"occurrence" : fm.occurrence,
"detection" : fm.detection,
"ap" : calculate_action_priority(fm.severity, fm.occurrence, fm.detection)
}
for fm in sorted_fms[:10 ]
]
for fm in fmea.failure_modes:
if fm.actions_taken:
analysis["actions_status" ]["completed" ] += 1
elif fm.recommended_actions:
analysis["actions_status" ]["in_progress" ] += 1
else :
analysis["actions_status" ]["pending" ] += 1
return analysis
def generate_control_plan_items (fmea: FMEA ):
"""
Generate control plan items from FMEA
"""
control_items = []
for fm in fmea.failure_modes:
for control in fm.current_controls_prevention + fm.current_controls_detection:
control_items.append({
"process_step" : fm.item_function,
"characteristic" : fm.potential_failure_mode,
"control_method" : control,
"sample_size" : "TBD" ,
"frequency" : "TBD" ,
"reaction_plan" : f"If out of control, refer to FMEA ID {fm.id } "
})
return control_items
Process Integration This skill integrates with the following processes:
failure-mode-effects-analysis.jsroot-cause-analysis-investigation.jsstatistical-process-control-implementation.js
Output Format {
"fmea_summary" : {
"type" : "PFMEA" ,
"item" : "Assembly Process" ,
"revision" : "1.2" ,
"total_failure_modes" : 45
} ,
"risk_analysis" : {
"high_priority_count" : 8 ,
"medium_priority_count" : 22 ,
"low_priority_count" : 15 ,
"max_rpn" : 392 ,
"avg_rpn" : 112
} ,
"top_risks" : [
{
"failure_mode" : "Missing fastener" ,
"rpn" : 392 ,
"ap" : "H" ,
"recommended_action" : "Add vision system verification"
}
] ,
"actions_required" : 12
}
Best Practices
Cross-functional team - Include design, manufacturing, quality, serviceStart early - Begin FMEA during design phaseFocus on prevention - Prioritize prevention over detectionLiving document - Update FMEA when process changesUse AP not just RPN - Consider AIAG-VDA AP methodologyTrack actions - Close the loop on recommended actions
Constraints
Document all assumptions
Use consistent rating criteria
Review periodically
Link to control plans
