All examples for Feature Request Deduplicator

Basic Duplicate Detection

Simple 3-request example demonstrating core duplicate detection. Identifies that 'Slack integration' and 'Integrate with Slack' are semantically identical while keeping 'Dark mode' separate. Shows confidence scoring and demand ranking.

Input

What you provide to the skill

Deduplicate these feature requests: request_id,title,description,source,customer
1,Slack integration,Need Slack notifications,email,AcmeCorp
2,Integrate with Slack,Slack for team alerts,support,BetaCo
3,Dark mode,Add dark theme option,email,GammaCorp

Output

What the skill generates for you

Step 1 - Data Loading

Loaded 3 requests. Required columns present: request_id, title, description, source, customer.

Step 2 - Duplicate Analysis

Found 1 duplicate cluster(s):

Cluster 1: Slack Integration (Confidence: 92%)

  • Request #1: “Slack integration” (email, AcmeCorp)
  • Request #2: “Integrate with Slack” (support, BetaCo)
  • Reasoning: All requests mention Slack integration/notifications for team alerts with high semantic similarity

Step 3 - Deduplicated List

Total unique requests: 2 (down from 3)

  1. Slack Integration (2 mentions)

    • Sources: support, email
    • Customers: AcmeCorp, BetaCo

  2. Dark Mode (1 mention)

    • Sources: email
    • Customers: GammaCorp

Step 4 - Theme Clustering

  1. Integrations & Notifications (1 request, 50%)
  2. UI/UX Enhancements (1 request, 50%)

Step 5 - True Demand Ranking

  1. Slack Integration - 2 customers
  2. Dark Mode - 1 customer

Insight: Without deduplication, Slack appeared as 2 separate requests. After consolidation, it’s clearly top priority with 2 distinct customers requesting the same feature.

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About This Skill

Deduplicate and cluster feature requests from multiple sources. Identifies duplicates using semantic similarity, merges related requests, clusters into themes, and reveals true demand signals.

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More Examples

Ambiguous Case Handling

Tests edge case where all 4 requests relate to 'notifications' but target different channels (mobile, Slack, email, real-time). Demonstrates proper ambiguous case flagging at 72% confidence rather than auto-merging, recommending manual review.

Revenue-Weighted Enterprise Prioritization

Demonstrates revenue-weighted analysis with 5 requests totaling $455K. Shows how SSO/SAML requests from enterprise customers ($350K combined) should be prioritized over webhooks ($30K) despite equal request counts. Includes strategic recommendations.

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