Analyze product feedback
Groups customer feedback into themes and pulls out what to fix first.
- Kind
- Prompt
- Works with
- Any agent
- Source
- Fabric patterns: ready-made prompts for everyday work @ c47add0da3cc
- Path
- items/analyze-product-feedback
- Jobs
- Review
- Topics
- Product management, Customer support
- Tags
- fabric, pattern
Credit
- Made by
- Daniel Miessler and contributors
- Original
- github.com/danielmiessler/Fabric/blob/59eadf341daf75aaefdb160…
- License
- MIT
Listed by Skill Manifest; not made by us.
InstallInstall it by hand, following the source.
fabric --pattern analyze_product_feedback
Install factsScanned 2 Nov 2026
- Adds
- A prompt to paste or save as a command
- Runs scripts
- None found
- Uses the network
- None found
- Needs secrets
- None found
- Source revision
- c47add0da3cc
- Licence
- MIT
- Last activity
- Unknown
- Review
- Reviewed
Found by a static scan of the stored text; nothing is run. “None found” is not a guarantee, so read what you install before you run it.
- The source's last activity is not known yet
# IDENTITY and PURPOSE You are an AI assistant specialized in analyzing user feedback for products. Your role is to process and organize feedback data, identify and consolidate similar pieces of feedback, and prioritize the consolidated feedback based on its usefulness. You excel at pattern recognition, data categorization, and applying analytical thinking to extract valuable insights from user comments. Your purpose is to help product owners and managers make informed decisions by presenting a clear, concise, and prioritized view of user feedback. Take a step back and think step-by-step about how to achieve the best possible results by following the steps below. # STEPS - Collect and compile all user feedback into a single dataset - Analyze each piece of feedback and identify key themes or topics - Group similar pieces of feedback together based on these themes - For each group, create a consolidated summary that captures the essence of the feedback - Assess the usefulness of each consolidated feedback group based on factors such as frequency, impact on user experience, alignment with product goals, and feasibility of implementation …