Customer Feedback Analyzer

This walks you through building a complete feedback system, from setting up collection channels to actually closing the loop with users. The core principle hits hard: never collect feedback you won't act on. It covers the usual suspects like NPS surveys and support tickets, but the real value is in the prioritization framework (Impact × Frequency × Strategic Alignment) and the specific templates for notifying users when you ship what they asked for. The pattern recognition section is useful too, especially the warning about squeaky wheel syndrome where one loud user doesn't equal real demand. If you're drowning in feature requests or just letting feedback pile up in a spreadsheet, this gives you a structured way to triage and respond.

skills.sh installs: 190
GitHub stars: 39
last updated: Aug 21, 2026

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Install

$ npx -y skills add daffy0208/ai-dev-standards --skill customer-feedback-analyzer --agent claude-code

Installs into .claude/skills of the current project.

Files

Customer Feedback Analyzer

Collect, analyze, and prioritize user feedback to inform product decisions.

Core Principle

Never collect feedback you won't act on. Collecting feedback creates expectation of action. If you can't commit to reviewing and acting on it, don't ask for it. Destroys trust.

Feedback Channels

1. In-App Feedback Widget

Best for: Contextual feedback, low friction

// Contextual feedback
<FeedbackWidget
  context={{
    page: 'dashboard',
    feature: 'export',
    user_action: 'clicked_export'
  }}
  placeholder="How can we improve exports?"
/>

Pros: High quality (contextual), immediate Cons: Can interrupt user flow

2. NPS Surveys

Best for: Measuring overall satisfaction and loyalty

Question: "How likely are you to recommend [Product] to a friend or colleague?"
Scale: 0-10

Scoring:
  Promoters (9-10): Love your product, will advocate
  Passives (7-8): Satisfied but not enthusiastic
  Detractors (0-6): Unhappy, will churn

NPS = % Promoters - % Detractors

Benchmarks:
  Excellent: ≥50
  Good: 30-49
  Needs Work: <30

Follow-up question: "What's the main reason for your score?"

SKILL.md on GitHub