library/specializations/product-management/skills/feedback-aggregation/SKILL.md
Aggregate and analyze customer feedback from multiple sources for product insights
npx skillsauth add a5c-ai/babysitter Customer Feedback AggregationInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Specialized skill for aggregating and analyzing customer feedback from multiple sources. Enables product teams to synthesize voice-of-customer data into actionable insights for product decisions.
This skill integrates with the following processes:
jtbd-analysis.js - Voice of customer for jobs analysisfeature-definition-prd.js - Customer-driven requirementsrice-prioritization.js - Reach and impact scoringcustomer-advisory-board.js - CAB feedback synthesis{
"type": "object",
"properties": {
"sources": {
"type": "array",
"items": {
"type": "object",
"properties": {
"type": { "type": "string", "enum": ["support-tickets", "nps-verbatim", "sales-calls", "app-reviews", "interviews", "surveys"] },
"data": { "type": "array", "items": { "type": "object" } },
"dateRange": { "type": "object" }
}
},
"description": "Feedback data sources"
},
"analysisScope": {
"type": "string",
"enum": ["all", "feature-requests", "pain-points", "sentiment", "trends"],
"description": "Focus area for analysis"
},
"segmentation": {
"type": "array",
"items": { "type": "string" },
"description": "Dimensions to segment feedback by"
},
"timeRange": {
"type": "object",
"properties": {
"start": { "type": "string", "format": "date" },
"end": { "type": "string", "format": "date" }
}
}
},
"required": ["sources"]
}
{
"type": "object",
"properties": {
"summary": {
"type": "object",
"properties": {
"totalFeedbackItems": { "type": "number" },
"sourceBreakdown": { "type": "object" },
"dateRange": { "type": "object" },
"overallSentiment": { "type": "string" }
}
},
"themes": {
"type": "array",
"items": {
"type": "object",
"properties": {
"theme": { "type": "string" },
"frequency": { "type": "number" },
"sentiment": { "type": "string" },
"examples": { "type": "array", "items": { "type": "string" } },
"segments": { "type": "object" }
}
}
},
"featureRequests": {
"type": "array",
"items": {
"type": "object",
"properties": {
"feature": { "type": "string" },
"requestCount": { "type": "number" },
"customerSegments": { "type": "array", "items": { "type": "string" } },
"urgencyScore": { "type": "number" },
"impactEstimate": { "type": "string" },
"representativeQuotes": { "type": "array", "items": { "type": "string" } }
}
}
},
"painPoints": {
"type": "array",
"items": {
"type": "object",
"properties": {
"painPoint": { "type": "string" },
"severity": { "type": "string" },
"frequency": { "type": "number" },
"customerImpact": { "type": "string" }
}
}
},
"trends": {
"type": "object",
"properties": {
"emerging": { "type": "array", "items": { "type": "string" } },
"declining": { "type": "array", "items": { "type": "string" } },
"sentimentTrend": { "type": "string" }
}
},
"recommendations": {
"type": "array",
"items": {
"type": "object",
"properties": {
"recommendation": { "type": "string" },
"priority": { "type": "string" },
"evidence": { "type": "array", "items": { "type": "string" } }
}
}
}
}
}
const feedbackAnalysis = await executeSkill('feedback-aggregation', {
sources: [
{
type: 'support-tickets',
data: supportTickets,
dateRange: { start: '2026-01-01', end: '2026-01-24' }
},
{
type: 'nps-verbatim',
data: npsResponses
},
{
type: 'app-reviews',
data: appStoreReviews
}
],
analysisScope: 'all',
segmentation: ['plan_type', 'company_size', 'tenure']
});
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