Feedback aggregation

Skill a5c-ai/babysitter/library/specializations/product-management/skills/feedback-aggregation

Aggregate and analyze customer feedback from multiple sources for product insightsFrom its SKILL.md

Install

npx -y skills add a5c-ai/babysitter --skill feedback-aggregation

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SKILL.md

5.4 KB, ~1.3k tokens by cl100k_base, as published. Nobody here has run it

Customer Feedback Aggregation Skill

Overview

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.

Capabilities

Data Collection

Parse support tickets for feature requests
Analyze NPS/CSAT verbatim responses
Extract themes from sales call notes
Monitor app store reviews
Aggregate feedback from Intercom/Zendesk
Process customer interview transcripts

Analysis

Calculate feature request frequency
Track sentiment trends over time
Identify emerging themes and patterns
Segment feedback by customer type
Correlate feedback with customer attributes
Detect urgency and impact signals

Synthesis

Generate feedback summary reports
Create feature request rankings
Build customer pain point matrices
Generate insight recommendations
Create feedback-to-feature mapping

Target Processes

This skill integrates with the following processes:

jtbd-analysis.js - Voice of customer for jobs analysis
feature-definition-prd.js - Customer-driven requirements
rice-prioritization.js - Reach and impact scoring
customer-advisory-board.js - CAB feedback synthesis

Input Schema

{
  "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"]
}

Output Schema

{
  "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" } }
        }
      }
    }
  }
}

Usage Example

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']
});

Dependencies

NLP capabilities
Support platform APIs (Intercom, Zendesk)
App store APIs

What ships with it: 1 file

1.0 KB alongside SKILL.md

README.md1.0 KB

Gives 0 of the 12 instructions most customer support skills give in ~1.3k tokens

Counted across 123 of the 124 authors here whose files we hold, read 2026-08-07

Call RUBE_SEARCH_TOOLS first to get current schemasin 12 of 123, across 4 files
Confirm connection status is ACTIVE before running workflowsin 12 of 123, across 4 files
Stop and ask for clarification if required inputs are missingin 9 of 123, across 2 files
Call RUBE_MANAGE_CONNECTIONS with the helpdesk toolkitin 9 of 123, across 2 files
Use both timestamp and ID for cursor navigationin 8 of 123, across 1 file
Implement backoff on 429 responsesin 8 of 123, across 1 file
Parse response data defensively with fallback patternsin 8 of 123, across 1 file
Use this skill only when the task clearly matches the scopein 8 of 123, across 1 file
Pass a JSON file as the positional argumentin 7 of 123, across 1 file
Specify output format with the --format flagin 7 of 123, across 1 file
Run health, churn, and expansion scripts togetherin 7 of 123, across 1 file
Verify output files contain expected records before continuingin 7 of 123, across 1 file

Said here and by no other author read

parse support tickets for feature requests
analyze verbatim responses
extract themes from sales call notes
monitor app store reviews
aggregate feedback from support platforms
process customer interview transcripts

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.