Synthesize customer feedback into actionable product insights using frameworks from 56 product leaders.
Works with
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
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionanalyzing-user-feedbackExecute the skills CLI command in your project's root directory to begin installation:
Fetches analyzing-user-feedback from refoundai/lenny-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate analyzing-user-feedback. Access via /analyzing-user-feedback in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
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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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Help the user extract actionable insights from customer feedback using techniques from 56 product leaders.
When the user asks for help analyzing feedback:
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.
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."
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.
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."
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."
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."
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."
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."
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."
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."
For all 64 insights from 56 guests, see references/guest-insights.md
Make data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
analyzing-user-feedback is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
We added analyzing-user-feedback from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Solid pick for teams standardizing on skills: analyzing-user-feedback is focused, and the summary matches what you get after install.
We added analyzing-user-feedback from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Solid pick for teams standardizing on skills: analyzing-user-feedback is focused, and the summary matches what you get after install.
Useful defaults in analyzing-user-feedback — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
I recommend analyzing-user-feedback for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
analyzing-user-feedback fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
analyzing-user-feedback fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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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