- name
- review-defect-miner
- description
- Extract and cluster defect signals from ecommerce reviews and social feedback into actionable quality/fix priorities. Use when the user asks why ratings are low, what issues drive bad sentiment, or which product problems should be fixed first.
# Review Defect Miner
## Skill Card
- Category: Voice of Customer - Core problem: What product defects and dissatisfaction patterns are hidden in reviews/comments? - Best for: Product and content teams diagnosing quality gaps - Expected input: Low-star reviews, comments, support tickets, return notes - Expected output: Defect clusters by severity, frequency, and fix priority with evidence snippets - Creatop handoff: Convert top defect clusters into product fixes + expectation-setting scripts
## Workflow
1. Normalize raw review/comment text and tag source + date + star level.
- Detect defect themes (quality, packaging, expectation mismatch, delivery, usability).
- Score each theme by severity, frequency, and conversion impact risk.
- Output top fix backlog and messaging mitigations.
## Output format
Return in this order: 1. Executive summary (max 5 lines) 2. Priority actions (P0/P1/P2) 3. Evidence table (signal, confidence, risk) 4. 7-day execution plan
## Quality and safety rules
- Preserve original evidence snippets for traceability.
- Separate quality defects from logistics/service issues.
- Do not over-generalize from tiny sample sizes.
## License
Copyright (c) 2026 Razestar.
This skill is provided under CC BY-NC-SA 4.0 for non-commercial use. You may reuse and adapt it with attribution to Razestar, and share derivatives under the same license.
Commercial use requires a separate paid commercial license from Razestar. No trademark rights are granted.