Help users de-risk new product bets by testing core assumptions, building low-fidelity prototypes, and gathering high-signal evidence before investing heavy engineering resources.
npx skills add https://github.com/RefoundAI/lenny-skills --skill 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.
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.
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.
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."
Achieve product-market fit by talking to customers directly and immediately to learn if you are building something people actually want.
Oji Udezue: "So the zone of benefit works as a framework because people will not pay for things that don't either really shrink the workflow that they're doing or doesn't give them superpower. So the same amount of time, but twice as much output. But the most important thing is that it has to be noticeable."
A problem is only worth building for if your solution offers a visible and massive compression of the existing customer workflow.
See references/artifacts.md for the full list with details.
For all 33 sourced insights from 31 guests, see references/guest-insights.md
Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.
Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup
Use when implementing any feature or bugfix, before writing implementation code
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always
Expert guidance for systematic backtesting of trading strategies. Use when developing, testing, stress-testing, or validating quantitative trading strategies. Covers "beating ideas to death" methodology, parameter robustness testing, slippage modeling, bias prevention, and interpreting backtest results. Applicable when user asks about backtesting, strategy validation, robustness testing, avoiding overfitting, or systematic trading development.
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Take refoundai/idea-validation 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.