Agent skill

Customer Feedback Analyzer

by nicepkg in nicepkg/ai-workflow

Synthesize user feedback from multiple channels and identify patterns to inform product decisions.

MITAuto-check passedSales & Support

Install Customer Feedback Analyzer

skills CLI
$ npx skills add nicepkg/ai-workflow --skill customer-feedback-analyzer -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install nicepkg/ai-workflow customer-feedback-analyzer --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/nicepkg/ai-workflow.git skills-src && mkdir -p .claude/skills && cp -r skills-src/workflows/product-manager-workflow/.claude/skills/customer-feedback-analyzer .claude/skills/customer-feedback-analyzer && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
customer-feedback-analyzer
GitHub stars
285
Token cost
~2.5k tokens
SKILL.md length
281 words
Files
2
Skills in repo
61
Repo updated
First seen
Licence
MIT

At a glance

Synthesize user feedback from multiple channels and identify patterns to inform product decisions.

  • Works in 10 steps: In-App Feedback Widget → NPS Surveys → Support Tickets → …
  • Analyzing feedback
  • SKILL.md covers Core Principle, Feedback Channels, Feedback Categorization and Prioritization Framework, plus 6 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Customer Feedback Analyzer is an agent skill from nicepkg/ai-workflow. Synthesize user feedback from multiple channels and identify patterns to inform product decisions. Use when analyzing feedback, prioritizing feature requests, conducting NPS surveys, or understanding user sentiment. Covers feedback collection, categorization, prioritization frameworks, and closing the feedback loop.

Its SKILL.md is about 2.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `manifest.yaml`).

It sits in Sales & Support, covering Customer feedback analysis and Prioritization frameworks. The repository describes itself as: 🚀 170+ pre-built skills for Claude Code, Cursor, Codex & 14+ AI tools. Stop re-teaching your AI the same things. One command → instant domain expertise. Marketing, SEO, Trading… The licence is MIT.

When your agent uses it

  • Analyzing feedback
  • Prioritizing feature requests
  • Conducting NPS surveys
  • Understanding user sentiment

Example prompts

  • “/customer-feedback-analyzer”

Workflow steps

10 steps, taken from the step headings in SKILL.md.

  1. In-App Feedback Widget
  2. NPS Surveys
  3. Support Tickets
  4. User Interviews
  5. Feature Request Voting
  6. Exit Interviews
  7. Acknowledge
  8. Act
  9. Notify Users Who Requested It
  10. Public Changelog

What it can do on your machine

Read from SKILL.md and the folder at commit d167b41. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are yaml, markdown and javascript).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Customer Feedback Analyzer loads about 2.5k tokens when it runs. Until then it costs about 86 tokens; SKILL.md has 281 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~86
When it runs · the whole SKILL.md, loaded when a task matches
~2.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from nicepkg/ai-workflow at commit d167b41, republished under its MIT licence (© nicepkg). 281 words, ~2,476 tokens.

Download SKILL.mdSave it as .claude/skills/customer-feedback-analyzer/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
customer-feedback-analyzer
description
Synthesize user feedback from multiple channels and identify patterns to inform product decisions. Use when analyzing feedback, prioritizing feature requests, conducting NPS surveys, or understanding user sentiment. Covers feedback collection, categorization, prioritization frameworks, and closing the feedback loop.

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

javascript
// 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

yaml
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?"

3. Support Tickets

Best for: Identifying recurring issues

yaml
Pattern Recognition:
  - Same issue reported 5+ times → UX problem, not edge case
  - Support time > 10 min per ticket → Needs better docs
  - Ticket volume spike → Recent deploy likely caused issue
4. User Interviews

Best for: Deep qualitative insights

yaml
Interview Structure:
  1. Background (5 min): Their role, use case
  2. Problem Discovery (10 min): Challenges they face
  3. Solution Validation (10 min): Show prototype, get reaction
  4. Wrap-up (5 min): Any other feedback?

Sample Size: 5-10 users per persona
5. Feature Request Voting

Best for: Prioritizing roadmap

Tools: Canny, ProductBoard, Upvoty

yaml
Benefits:
  - See most requested features
  - Reduce duplicate requests
  - Public roadmap transparency
  - Close the loop automatically

Avoid:
  - Building everything requested
  - Letting voters drive strategy
6. Exit Interviews

Best for: Understanding why users churn

yaml
Key Questions:
  - What made you decide to cancel?
  - What feature/change would have kept you?
  - What are you switching to?
  - What did we do well?

Feedback Categorization

By Type
yaml
Bug: Something broken
  - "Export fails with >100 rows"
  - Priority: Fix immediately

Feature Request: New capability
  - "Add Slack integration"
  - Priority: Vote/validate

Enhancement: Improve existing feature
  - "Export should include timestamps"
  - Priority: Nice to have

Usability: Confusing UX
  - "Can't find where to invite team members"
  - Priority: High (friction)

Performance: Speed issue
  - "Dashboard loads slowly"
  - Priority: Depends on impact
By Severity
yaml
Critical: Blocks core workflow
  - "Can't save projects"
  - Action: Hotfix immediately

High: Significant friction
  - "Onboarding confusing"
  - Action: Fix this sprint

Medium: Minor annoyance
  - "Button text unclear"
  - Action: Fix next quarter

Low: Edge case or cosmetic
  - "Spacing looks off on mobile"
  - Action: Backlog
By Frequency
yaml
Widespread: 50+ reports
  → High priority

Common: 10-50 reports
  → Medium priority

Occasional: 5-10 reports
  → Low priority, monitor

Rare: <5 reports
  → Likely edge case, document workaround
By User Segment
yaml
Power Users: High engagement, experienced
  → Actionable, technical feedback

New Users: Recently signed up
  → Onboarding issues, first impressions

Churned Users: Cancelled/inactive
  → Why did they leave?

Enterprise: Paying customers
  → Security, compliance, integrations

Prioritization Framework

Priority Score Formula
Score = Impact (1-5) × Frequency (1-5) × Strategic Alignment (1-5)

Score ≥ 40: High Priority (next sprint)
Score 20-39: Medium Priority (next quarter)
Score < 20: Low Priority (backlog or never)

Example:

yaml
Feedback: "Add Slack integration"
  Impact: 4 (significantly improves collaboration)
  Frequency: 5 (50+ requests)
  Strategic Alignment: 4 (fits roadmap)
  Score: 4 × 5 × 4 = 80

  Decision: HIGH PRIORITY - Build next sprint

Feedback: "Change button color"
  Impact: 1 (minor cosmetic)
  Frequency: 1 (1 person mentioned)
  Strategic Alignment: 1 (not strategic)
  Score: 1 × 1 × 1 = 1

  Decision: LOW PRIORITY - Backlog

Close the Feedback Loop

1. Acknowledge
markdown
Email Template:

Subject: Thanks for your feedback!

Hi [Name],

Thanks for taking the time to share your thoughts on [topic].

We review all feedback and use it to prioritize our roadmap. I've shared
your input with the product team.

You can track feature requests on our public roadmap: [link]

Thanks for helping us improve!
[Your Name]
2. Act
yaml
Decision Tree: Is it reported 10+ times?
  Yes → Add to roadmap
  No → Monitor

  Does it align with strategy?
  Yes → Prioritize
  No → Document why not

  Can we ship in 2 weeks?
  Yes → Quick win, do it
  No → Add to backlog
3. Notify Users Who Requested It
markdown
Email Template:

Subject: You asked for [Feature] - it's live!

Hi [Name],

Remember when you asked us to add [feature]? Good news - it's live!

[Screenshot/GIF of feature]

Here's how it works:

1. [Step 1]
2. [Step 2]

Try it now: [Link]

Thanks for the feedback that made this happen.
[Your Name]

P.S. Have more ideas? Reply to this email.
4. Public Changelog
markdown
## [Feature] is now live!

Requested by 47 users, [Feature] lets you [benefit].

How it works:

- [Key point 1]
- [Key point 2]

Try it: [Link]
Thanks to everyone who suggested this!

Common Feedback Patterns

Squeaky Wheel Syndrome
yaml
Problem: Vocal minority ≠ Real need

Example:
  - 1 user emails daily about dark mode
  - Analytics show 2% use dark mode

Action: Validate with data before building
Silent Churn
yaml
Problem: Users leave without complaining

Example:
  - Retention drops from 40% to 30%
  - No feedback, no complaints

Action:
  - Proactive exit interviews
  - Check analytics for drop-off points
  - Run usability tests
Feature Bloat Risk
yaml
Problem: Building everything requested leads to bloat

Example:
  - 'Add Excel export'
  - 'Add CSV export'
  - 'Add JSON export'
  - 'Add PDF export'

Action: Build generic solution, not every variant

Synthesis & Reporting

Weekly Feedback Summary
yaml
feedback_summary:
  period: "2024-01-15 to 2024-01-22"
  total_items: 87

  top_themes:
    - theme: "Slack Integration"
      frequency: 23
      severity: high
      example_quotes:
        - "We need Slack notifications"
        - "Can't notify team without Slack"
      recommended_action: "Build Slack integration next sprint"

    - theme: "Slow Dashboard Load"
      frequency: 15
      severity: medium
      example_quotes:
        - "Dashboard takes 10+ seconds"
        - "Performance is terrible"
      recommended_action: "Optimize queries, add caching"

    - theme: "Mobile App Request"
      frequency: 8
      severity: low
      example_quotes:
        - "I want a mobile app"
      recommended_action: "Monitor, not enough demand yet"

  nps:
    score: 42
    detractor_reasons:
      - "Too expensive" (12 mentions)
      - "Missing features" (8 mentions)
      - "Slow performance" (5 mentions)

  prioritized_backlog:
    - feedback: "Add Slack integration"
      score: 80
      priority: high

    - feedback: "Optimize dashboard performance"
      score: 45
      priority: medium

    - feedback: "Mobile app"
      score: 16
      priority: low

Tools & Software

yaml
Feedback Collection:
  - In-app: Canny, UserVoice, Intercom
  - Surveys: Typeform, SurveyMonkey, Delighted (NPS)
  - User Research: Calendly, Zoom, UserTesting.com

Analysis:
  - Qualitative: Dovetail, Notion, Airtable
  - Quantitative: Excel, Google Sheets, Tableau
  - Sentiment: MonkeyLearn, Lexalytics

Roadmap Transparency:
  - Public Roadmap: Canny, ProductBoard, Trello
  - Changelog: Headway, ReleaseNotes.io, Beamer

Feedback Cadence

yaml
Daily:
  - Review support tickets
  - Monitor in-app feedback

Weekly:
  - Synthesize themes
  - Share with product team
  - Prioritize top requests

Monthly:
  - Send NPS survey
  - Review feature requests
  - Update public roadmap

Quarterly:
  - User interviews (5-10)
  - Exit surveys for churned users
  - Competitive feedback analysis

Quick Start Checklist

  • Set up in-app feedback widget
  • Schedule NPS survey (monthly)
  • Create feedback tracking spreadsheet
  • Review support tickets weekly
  • Conduct 2-3 user interviews
  • Set up public roadmap (optional)
  • Create email templates for acknowledging feedback
  • Document feedback categorization process

Common Pitfalls

❌ Collecting feedback without acting: Damages trust ❌ Building everything requested: Feature bloat ❌ Not validating with data: Vocal minority ≠ majority ❌ Ignoring silent majority: Not everyone gives feedback ❌ No follow-up: Users want to know you listened

Summary

Great feedback analysis:

  • ✅ Multiple channels (surveys, tickets, interviews)
  • ✅ Categorize and prioritize systematically
  • ✅ Act on high-priority feedback quickly
  • ✅ Close the loop (notify users)
  • ✅ Balance requests with strategy
  • ✅ Share insights with team

© nicepkg, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in workflows/product-manager-workflow/.claude/skills/customer-feedback-analyzer of nicepkg/ai-workflow.

  • SKILL.md
  • manifest.yaml

Open the folder on GitHubat commit d167b41

Compare with similar skills

Customer Feedback Analyzer next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Customer Feedback Analyzer compared with similar skills
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Customer Feedback Triageborghei/Claude-Skills881—~2.1kAutomated safety check: PassMIT
Review Analysisliangdabiao/amazon-sorftime-research-MCP-skill9461 repos~2.5kAutomated safety check: PassNone
Bggg Data Amazonbinggandata/bggg-skills603—~1.4kAutomated safety check: PassMIT
Zsxqunnoo/zsxq-skill304—~3.8kAutomated safety check: PassMIT
Siftranknoperator/siftrank224—~2.9kAutomated safety check: PassMIT

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Categories

Questions about Customer Feedback Analyzer

What does Customer Feedback Analyzer do?

Synthesize user feedback from multiple channels and identify patterns to inform product decisions. Customer Feedback Analyzer is an agent skill from nicepkg/ai-workflow. Synthesize user feedback from multiple channels and identify patterns to inform product decisions.

When should I use Customer Feedback Analyzer?

Customer Feedback Analyzer fits situations like: analyzing feedback; prioritizing feature requests; conducting NPS surveys; understanding user sentiment.

How do I install Customer Feedback Analyzer in Claude Code?

Run `npx skills add nicepkg/ai-workflow --skill customer-feedback-analyzer -a claude-code`. Or copy the skill folder (workflows/product-manager-workflow/.claude/skills/customer-feedback-analyzer in nicepkg/ai-workflow) into .claude/skills/customer-feedback-analyzer in your project. Claude Code loads it when a task matches its description.

How do I install Customer Feedback Analyzer in Codex?

Run `npx skills add nicepkg/ai-workflow --skill customer-feedback-analyzer -a codex`. Or copy the skill folder (workflows/product-manager-workflow/.claude/skills/customer-feedback-analyzer in nicepkg/ai-workflow) into .agents/skills/customer-feedback-analyzer in your project. Codex loads it when a task matches its description.

Can I use Customer Feedback Analyzer in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add nicepkg/ai-workflow --skill customer-feedback-analyzer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/customer-feedback-analyzer, .gemini/skills/customer-feedback-analyzer, .github/skills/customer-feedback-analyzer and .opencode/skills/customer-feedback-analyzer in your project.

What does Customer Feedback Analyzer need to run?

SKILL.md names no scripts, command-line tools or credentials: Customer Feedback Analyzer is instructions for the agent only.

Does Customer Feedback Analyzer access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Customer Feedback Analyzer safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Customer Feedback Analyzer use?

Customer Feedback Analyzer is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Customer Feedback Analyzer use?

About 2.5k tokens (SKILL.md is roughly 9.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Customer Feedback Analyzer?

Skills that share tags, products or a category with Customer Feedback Analyzer: Customer Feedback Triage (borghei/Claude-Skills, 881 stars), Review Analysis (liangdabiao/amazon-sorftime-research-MCP-skill, 946 stars), Bggg Data Amazon (binggandata/bggg-skills, 603 stars) and Zsxq (unnoo/zsxq-skill, 304 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Customer Feedback Analyzer?

nicepkg (a GitHub organization) maintains it in nicepkg/ai-workflow, which has 285 GitHub stars. The repository holds 61 skills in this directory. The repository was last updated on January 20, 2026.

Source: nicepkg/ai-workflow on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.