User Feedback System Skill for Claude
Builds feedback collection systems using Superhuman's PMF framework and YC's "talk to users" methodology. Use when implementing NPS surveys, scheduling user interviews, or measuring product-market fit.
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/menkesu/awesome-pm-skills --skill user-feedback-systemThe instruction itself
10 sections, as written by the authorThe Feedback Loop
When This Skill Activates
Claude uses this skill when:
- Building feedback collection
- Measuring product-market fit
- Implementing user research
- Creating feedback systems
Core Frameworks
1. Superhuman PMF Survey
The Question:
> "How would you feel if you could no longer use [product]?"
> - Very disappointed
> - Somewhat disappointed
> - Not disappointed
PMF Threshold:
- >40% "very disappointed" = strong PMF
- <40% = need to improve
2. Talk to Users (YC)
The Approach:
- Talk to users weekly (minimum)
- Ask open-ended questions
- Watch them use product
- Focus on jobs-to-be-done
Action Templates
Template: Feedback System
# Feedback Collection System
## In-App Surveys
**PMF Survey:**
- Trigger: After 2 weeks of use
- Question: "How disappointed if couldn't use?"
- Follow-up: "What's the main benefit?"
**NPS Survey:**
- Trigger: Quarterly
- Question: "How likely to recommend (0-10)?"
- Follow-up: "Why that score?"
## User Interviews
**Cadence:** [Weekly/biweekly]
**Participants:** [How selected]
**Format:**
- 30-minute calls
- Watch them use product
- Ask "why?" 5 times
## Feature Requests
**Collection:**
- In-app widget
- Support tickets
- User interviews
**Tracking:**
- Count: [number of requests]
- Priority: [using RICE]
- Status: [planned/not planned]
Quick Reference
💬 Feedback Checklist
Collect:
- [ ] PMF survey implemented
- [ ] NPS tracking
- [ ] User interview cadence
- [ ] Feature request system
Act:
- [ ] Feedback reviewed weekly
- [ ] Patterns identified
- [ ] Prioritized using RICE
- [ ] Closed loop (tell users what you built)
Key Quotes
YC:
> "Talk to users. All the time. Until it feels like you're doing it too much."
Superhuman:
> "Product-market fit isn't a feeling. It's a number: 40% very disappointed."
Other skills for the same job
different authors, same section of the catalogue
Assists in writing high-quality content by conducting research, adding citations, improving hooks, iterating on outlines, and providing real-time feedback on each section. Transforms your writing process from solo effort to collaborative partnership.
Identifies high-quality leads for your product or service by analyzing your business, searching for target companies, and providing actionable contact strategies. Perfect for sales, business development, and marketing professionals.
Use this skill to query your Google NotebookLM notebooks directly from Claude Code for source-grounded, citation-backed answers from Gemini. Browser automation, library management, persistent auth. Drastically reduced hallucinations through document-only responses.
Efficient database search tool for bioRxiv preprint server. Use this skill when searching for life sciences preprints by keywords, authors, date ranges, or categories, retrieving paper metadata, downloading PDFs, or conducting literature reviews.
Query and analyze scholarly literature using the OpenAlex database. This skill should be used when searching for academic papers, analyzing research trends, finding works by authors or institutions, tracking citations, discovering open access publications, or conducting bibliometric analysis across 240M+ scholarly works. Use for literature searches, research output analysis, citation analysis, and academic database queries.
Access USPTO APIs for patent/trademark searches, examination history (PEDS), assignments, citations, office actions, TSDR, for IP analysis and prior art searches.
Multiagent AI system for scientific research assistance that automates research workflows from data analysis to publication. This skill should be used when generating research ideas from datasets, developing research methodologies, executing computational experiments, performing literature searches, or generating publication-ready papers in LaTeX format. Supports end-to-end research pipelines with customizable agent orchestration.
Automated LLM-driven hypothesis generation and testing on tabular datasets. Use when you want to systematically explore hypotheses about patterns in empirical data (e.g., deception detection, content analysis). Combines literature insights with data-driven hypothesis testing. For manual hypothesis formulation use hypothesis-generation; for creative ideation use scientific-brainstorming.
How to use it
Take menkesu/user-feedback-system from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.