All examples for Feedback Auto-Tagger

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.

Input

What you provide to the skill

Tag this customer feedback with Bug, Feature, Question categories and show confidence scores:

  • “App crashes every time I open settings”
  • “Would love to see dark mode added”
  • “How do I change my password?”
  • “The export feature is broken”
  • “Can you add Slack notifications?”

Output

What the skill generates for you

FEEDBACK TAGGING RESULTS

========================



| Feedback | Primary Tag | Confidence | Secondary Tags | Reasoning |

|----------|-------------|------------|----------------|-----------|

| App crashes every time I open settings | Bug | 98% | - | Explicit crash behavior with clear reproducible steps |

| Would love to see dark mode added | Feature Request | 99% | - | Clear feature request using typical request language ("would love") |

| How do I change my password? | Question | 100% | - | Straightforward how-to question with classic question pattern |

| The export feature is broken | Bug | 92% | - | Explicit defect statement, though less detailed than ideal |

| Can you add Slack notifications? | Feature Request | 97% | - | Direct feature request with specific integration mentioned |



SUMMARY STATISTICS

==================

- Total items processed: 5

- Successfully tagged: 5 (100%)

- Average confidence: 97.2%

- Items with multiple tags: 0 (0%)

- Items flagged for review (<60% confidence): 0 (0%)



TAG DISTRIBUTION

================

- Bug: 2 (40%)

- Feature Request: 2 (40%)

- Question: 1 (20%)



CONFIDENCE BREAKDOWN

====================

- High confidence (>85%): 5 items (100%)

- Medium confidence (70-85%): 0 items (0%)

- Low confidence (<60%): 0 items (0%)
View all examples for Feedback Auto-Tagger

About This Skill

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

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More 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.

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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