analyzing-user-feedback

refoundai/lenny-skills · updated Apr 8, 2026

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$npx skills add https://github.com/refoundai/lenny-skills --skill analyzing-user-feedback
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summary

Synthesize customer feedback into actionable product insights using frameworks from 56 product leaders.

  • Guides users through identifying patterns across multiple feedback channels (NPS, support, interviews, social) and clustering by behavioral pathways rather than demographics
  • Emphasizes distinguishing root causes from surface-level complaints, with techniques for uncovering what users don't explicitly state
  • Includes principles on prioritizing signal over noise, talking to churned us
skill.md

Analyzing User Feedback

Help the user extract actionable insights from customer feedback using techniques from 56 product leaders.

How to Help

When the user asks for help analyzing feedback:

  1. Understand their sources - Ask where feedback is coming from (NPS, support, sales, social, interviews)
  2. Help identify patterns - Assist in clustering feedback into themes and prioritizing by frequency and impact
  3. Challenge surface-level interpretations - Push them to find root causes, not just stated complaints
  4. Connect to action - Help translate insights into product decisions

Core Principles

Feedback is a river, not a lake

Shaun Clowes: "Really smart product managers are constantly swimming in a feedback river. Set up streams of user interview data, NPS, and competitor info to wash over you daily." Make feedback consumption continuous, not episodic.

Users lie (unintentionally)

Bret Taylor: "Taking what a customer says in a focus group is rarely correct. Practice intellectual honesty to distinguish surface-level complaints from root causes." When users say "price," they often mean "value."

Cluster, don't segment

Bob Moesta: "Instead of segmenting by demographics, we cluster by behavioral pathways. It's not one reason why people do things—it's sets of reasons." Look for the 'hire and fire' criteria for different user clusters.

Every support ticket is a product failure

Geoff Charles: "We literally have 'every support ticket is a failure of our product' posted on all channels. Share every negative review with the relevant PM and designer monthly."

The silent signals matter

Ramesh Johari: "There's a lot of information in ratings that are NOT left. The absence of a rating is often a strong signal of a mediocre experience users are too polite to report."

Filter the 80% noise

Jen Abel: "80% of feedback is noise based on legacy habits, 20% is gold that guides the future product. It's the founder's job to interpret what's 'the old way' versus real market needs."

Aggregate across all channels

Brian Balfour: "AI can analyze existing feedback AND identify knowledge gaps—what customers are NOT saying. Aggregate feedback from all sources into a centralized repository."

Talk to churned users

Uri Levine: "The most critical insights come from users who dropped out of the funnel, not those who succeeded. Interview users who churned to find the 'why' behind the failure."

Prioritize future users over vocal minorities

Tamar Yehoshua: "Don't over-index on people unhappy with your changes. Design for the bigger number of people who will use it tomorrow, not the vocal few complaining today."

Make insights stick

Yuhki Yamashata: "The goal is 'memification'—synthesize insights so they're catchy enough for execs to cite in meetings. Use real-world metaphors to explain complex concepts."

Questions to Help Users

  • "Where is your feedback coming from? Are you missing any channels?"
  • "Have you talked to churned users, or only happy customers?"
  • "What's the pattern behind these complaints—what's the root cause?"
  • "Are these requests from early adopters or from users stuck in old habits?"
  • "How will you act on this insight?"

Common Mistakes to Flag

  • Taking feedback literally - Users say they want X but often need Y
  • Only listening to vocal users - Silent majority may have different needs
  • Ignoring non-users - People who didn't convert have critical insights
  • Feedback hoarding - Insights trapped in silos don't help anyone
  • Hindsight bias - Don't dismiss research findings as "obvious" after the fact

Deep Dive

For all 64 insights from 56 guests, see references/guest-insights.md

Related Skills

  • Conducting User Interviews
  • Measuring Product-Market Fit
  • Prioritizing Roadmap
  • Setting OKRs & Goals
how to use analyzing-user-feedback

How to use analyzing-user-feedback on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add analyzing-user-feedback
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/refoundai/lenny-skills --skill analyzing-user-feedback

The skills CLI fetches analyzing-user-feedback from GitHub repository refoundai/lenny-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/analyzing-user-feedback

Reload or restart Cursor to activate analyzing-user-feedback. Access the skill through slash commands (e.g., /analyzing-user-feedback) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.452 reviews
  • Kaira Patel· Dec 16, 2024

    analyzing-user-feedback is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Advait Khan· Dec 16, 2024

    We added analyzing-user-feedback from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Omar Torres· Dec 12, 2024

    Solid pick for teams standardizing on skills: analyzing-user-feedback is focused, and the summary matches what you get after install.

  • Dhruvi Jain· Dec 8, 2024

    We added analyzing-user-feedback from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Sakura Abbas· Dec 4, 2024

    Solid pick for teams standardizing on skills: analyzing-user-feedback is focused, and the summary matches what you get after install.

  • Oshnikdeep· Nov 27, 2024

    Useful defaults in analyzing-user-feedback — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Advait Shah· Nov 23, 2024

    I recommend analyzing-user-feedback for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Evelyn Mensah· Nov 15, 2024

    analyzing-user-feedback fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Rahul Santra· Nov 7, 2024

    analyzing-user-feedback fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Arya Bhatia· Nov 7, 2024

    Keeps context tight: analyzing-user-feedback is the kind of skill you can hand to a new teammate without a long onboarding doc.

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