Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add cnfeat/top-pm-skills --skill analyzing-user-feedbackgit clone --depth 1 https://github.com/cnfeat/top-pm-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/cnfeat/top-pm-skills/analyzing-user-feedback)<a href="https://agentmods.dev/skills/cnfeat/top-pm-skills/analyzing-user-feedback"><img src="https://agentmods.dev/badge/skills/cnfeat/top-pm-skills/analyzing-user-feedback/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/cnfeat/top-pm-skills/analyzing-user-feedback"><img src="https://agentmods.dev/badge/skills/cnfeat/top-pm-skills/analyzing-user-feedback.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00049 | $0.00880 |
| Opus 5 | $0.00024 | $0.00440 |
| Sonnet 5 | $0.00010 | $0.00176 |
| Haiku 4.5 | $0.00005 | $0.00088 |
Grade A, and why
analyzing-user-feedback scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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:
- Understand their sources - Ask where feedback is coming from (NPS, support, sales, social, interviews)
- Help identify patterns - Assist in clustering feedback into themes and prioritizing by frequency and impact
- Challenge surface-level interpretations - Push them to find root causes, not just stated complaints
- 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."
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 77 lines · 49 tokens per session scan A b6b5243653d8
analyzing-user-feedback is a skill published in the GitHub repository cnfeat/top-pm-skills (48 stars, last pushed 2mo ago), licensed MIT. It adds 49 tokens to every session and 880 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…