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 idea-validationgit 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/idea-validation)<a href="https://agentmods.dev/skills/refoundai/lenny-skills/idea-validation"><img src="https://agentmods.dev/badge/skills/refoundai/lenny-skills/idea-validation/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/idea-validation"><img src="https://agentmods.dev/badge/skills/refoundai/lenny-skills/idea-validation.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 | $0.00033 | $0.01270 |
| Opus 5 | $0.00016 | $0.00635 |
| Sonnet 5 | $0.00007 | $0.00254 |
| Haiku 4.5 | $0.00003 | $0.00127 |
Grade A, and why
idea-validation 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 9d 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 — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Idea Validation
Stop building things people don't want by moving from opinion to evidence-based development.
Help the user with idea validation using insights from 31 guests and posts across Lenny's Podcast and Newsletter.
How to Help
- Clarify the Core Hypothesis - Help the user define the specific problem, target audience, and the most 'risky' assumptions using the Founding Hypothesis Scorecard.
- Select a Validation Path - Guide the user through choosing between the 'Listening Path', 'Manual Path', or 'Self-Serve Path' based on their proximity to the problem.
- Design Low-Fidelity Experiments - Recommend specific 'Wizard of Oz' or 'Fake Door' tests to simulate the solution without building a backend.
- Evaluate Signal Quality - Help users distinguish between polite interest and real market pull by looking for financial commitment or high-effort usage.
Core Principles
First-Principles De-risking
Bangaly Kaba: "Someone says, 'Hey, you know what? This would be great to build.' And you go pull data to go justify why that would be great to build. Call that identify, justify, execute. First you have to really understand from first principles what is actually going on."
True de-risking starts with understanding the core problem from first principles before looking for data to justify a specific solution.
Maintaining Prototype Momentum
Grant Lee: "We would have an idea in the morning, come up with some sort of functional prototype, recruit a bunch of people that are legitimately good prospective users, but have zero skin in the game, ship fast so people can start playing with it. In the afternoon, we're already running pretty full scale experiment."
Build functional prototypes within hours of conceiving an idea to maintain speed and identify usability flaws before wasting development cycles.
Direct User Feedback Loops
Gustaf Alstromer: "If I drill down what makes companies fail, it's quite simple. It's just like they don't talk to users, which means they don't find product market fit. And if they don't find product market fit, nothing else really matters."
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.
- 9d ago First seen · 80 lines · 33 tokens per session scan A 06c9dff0ad5c
idea-validation is a skill published in the GitHub repository RefoundAI/lenny-skills (1,311 stars, last pushed 1mo ago), licensed MIT. It adds 33 tokens to every session and 1,270 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
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.