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Analyzing User Feedback

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cnfeat
analyzing-user-feedback

Help users synthesize and act on customer feedback. Use when someone is analyzing NPS responses, processing support tickets, reviewing user research, synthesizing feedback from multiple channels, or trying to identify patterns in customer input.

Overview

Publishercnfeat
Repositorytop-pm-skills
Skill nameanalyzing-user-feedback
Stars
55
Forks
6
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by cnfeat on GitHub. Read the source before you install it.

Installation

Install the Analyzing User Feedback AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/cnfeat/top-pm-skills/tree/main/参考skill/lenny-skills-main%20(2)/lenny-skills-main/skills/analyzing-user-feedback
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Use it in TypingMind

Enable Analyzing User Feedback in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Analyzing User Feedback on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Analyzing User Feedback is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Analyzing User Feedback AI skill do?

Help users synthesize and act on customer feedback. Use when someone is analyzing NPS responses, processing support tickets, reviewing user research, synthesizing feedback from multiple channels, or trying to identify patterns in customer input.

Why use Analyzing User Feedback on TypingMind?

Because you install it once and use it with any model. Analyzing User Feedback is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Analyzing User Feedback in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cnfeat/top-pm-skills/tree/main/参考skill/lenny-skills-main%20(2)/lenny-skills-main/skills/analyzing-user-feedback. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Analyzing User Feedback?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Analyzing User Feedback?

As many as you like. As long as a model supports skills, you can use Analyzing User Feedback with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Analyzing User Feedback AI skill free?

Yes. It is published on GitHub by cnfeat under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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