Feedback Auto-Tagger

Pro v1.0.0 1 view

Automatically tag and classify customer feedback from CSV files using semantic analysis with confidence scores and emerging theme detection.

What You Get

Achieve 80-90% tagging accuracy (vs 60% manual), 100% consistency, and 10-20x time savings when classifying support tickets, survey responses, or multi-channel feedback.

The Problem

Product and support teams spend 5-10 hours monthly manually tagging feedback with inconsistent results. Manual tagging averages only 60% accuracy, misses emerging themes, and varies by tagger. Teams can't trust their feedback data for prioritization decisions.

The Solution

This skill uses Claude's semantic understanding to classify feedback items with consistent taxonomy, confidence scores, and multi-tag support. It processes 200-5000 items, detects emerging themes not covered by existing taxonomy, and generates distribution reports. Teams get tagged CSV with confidence scores, low-confidence items flagged for review, and actionable recommendations for taxonomy expansion.

How It Works

  1. 1 Load CSV and validate structure, identifying feedback text column and metadata columns
  2. 2 Establish or validate tag taxonomy with user, suggesting categories if none provided
  3. 3 Classify each feedback item with primary tag, confidence score, optional secondary tags, and reasoning
  4. 4 Detect emerging themes by analyzing patterns across all feedback and suggest taxonomy additions
  5. 5 Generate analysis report with tag distribution, confidence breakdown, and co-occurrence analysis
  6. 6 Export tagged CSV, distribution report, and low-confidence review file

What You'll Need

  • CSV file with feedback text column
  • Optional: existing tag taxonomy
  • Optional: metadata columns (source, product_area, date, feedback_id)

Get This Skill

Requires Pro subscription ($9/month)

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Examples

Auto-Suggest Taxonomy from Diverse Feedback

Processing 10 feedback items without a predefined taxonomy. Demonstrates the skill's ability to analyze feedback patterns, suggest an appropriate 8-category taxonomy, apply multi-tag support for complex items, and generate co-occurrence analysis.

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Multi-Category Feedback with Expanded Taxonomy

Classifying 8 diverse feedback items using a 5-category taxonomy (Bug, Feature Request, Praise, Complaint, Question). Shows variety of feedback types including positive sentiment and performance complaints with consistently high confidence scores.

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Support Ticket Classification with Predefined Taxonomy

Classifying 5 support feedback items using a simple Bug/Feature/Question taxonomy. Demonstrates high-confidence tagging with clear reasoning and distribution statistics for a small batch of typical support tickets.

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