Lenny Skills is a collection of product-management and engineering workflows for Claude Code and other AI agents, covering areas such as strategy, research, planning, shipping, growth, and hiring. Each skill gives an agent specialized guidance, frameworks, checklists, or templates for product work, and the catalogue contains many of these skills.
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 RefoundAI/lenny-skills --skill analyzing-user-feedbackgit clone --depth 1 https://github.com/RefoundAI/lenny-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/refoundai/lenny-skills/analyzing-user-feedback)<a href="https://agentmods.dev/skills/refoundai/lenny-skills/analyzing-user-feedback"><img src="https://agentmods.dev/badge/skills/refoundai/lenny-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/refoundai/lenny-skills/analyzing-user-feedback"><img src="https://agentmods.dev/badge/skills/refoundai/lenny-skills/analyzing-user-feedback.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.00028 | $0.01295 |
| Opus 5 | $0.00014 | $0.00647 |
| Sonnet 5 | $0.00006 | $0.00259 |
| Haiku 4.5 | $0.00003 | $0.00129 |
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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyzing User Feedback
Transform raw signals into actionable insights by scaling empathy and synthesis.
Help the user with analyzing user feedback using insights from 19 guests and posts across Lenny's Podcast and Newsletter.
How to Help
- Categorize signals - Help the user group disparate feedback into themes or segments based on user influence and frequency.
- Assess representativeness - Determine if feedback reflects a vocal minority or a broad user need using representation frameworks.
- Set up dogfooding - Design internal processes to experience friction firsthand through audits and mandatory usage programs.
- Apply AI synthesis - Guide the user in using LLMs to process large datasets like transcripts, reviews, and support tickets.
Core Principles
Experiential Empathy
Jeff Weinstein: "We show up four to eight people total pretend to be some company with some outcome problem. Rule one is you do not work at Stripe and rule two is we're not here to solve any problems. This is just about practicing empathy for the customer."
Build deeper empathy by having internal teams experience product friction firsthand without the distraction of immediate problem solving.
Mandatory Service Participation
Keith Yandell: "We have a program called WeDash, where, four times, a year all employees are required to go do deliveries. And I love doing it. I do it more than four times a year, and I usually take my daughters with me."
Require every employee to perform the core service of the business to build authentic empathy and surface operational bugs.
Creator Mindset Immersion
Maya Prohovnik: "If they talk to users all the time, they see the data, but all of them, once they finally start doing their podcast, they're like, I get it. Something clicked and now I feel like I really understand what they need. And I guess building tools for creators is similar to building a B2B product where you really have to understand business, it's their livelihood."
What ships with it
2 files 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 · 81 lines · 28 tokens per session scan A 8a03e1018f24
analyzing-user-feedback is a skill published in the GitHub repository RefoundAI/lenny-skills (1,315 stars, last pushed 1mo ago), licensed MIT. It adds 28 tokens to every session and 1,295 once invoked, about $0.0001 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
llama-cpp
Run LLM inference with llama.cpp on CPU, Apple Silicon, AMD/Intel GPUs, or NVIDIA — plus GGUF model conversion and quantization (2–8 bit with K-quants and imatrix). Covers CLI, Python bindings, OpenAI-compatible server, and Ollama/LM Studio integration. Use for edge deployment, M1/M2/M3/M4 Macs, CUDA-less…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior. 4-phase root cause investigation — NO fixes without understanding the problem first.
github-auth
Set up GitHub authentication for the agent using git (universally available) or the gh CLI. Covers HTTPS tokens, SSH keys, credential helpers, and gh auth — with a detection flow to pick the right method automatically.
ideation
Generate project ideas through creative constraints. Use when the user says 'I want to build something', 'give me a project idea', 'I'm bored', 'what should I make', 'inspire me', or any variant of 'I have tools but no direction'. Works for code, art, hardware, writing, tools, and anything that can be made.
ocr-and-documents
Extract text from PDFs and scanned documents. Use webextract for remote URLs, pymupdf for local text-based PDFs, marker-pdf for OCR/scanned docs. For DOCX use python-docx, for PPTX see the powerpoint skill.
notion
Notion API for creating and managing pages, databases, and blocks via curl. Search, create, update, and query Notion workspaces directly from the terminal.