Help users objectively assess where their product stands on the PMF spectrum by triangulating qualitative feedback, quantitative retention benchmarks, and organic growth signals.
npx skills add https://github.com/RefoundAI/lenny-skills --skill measuring-pmf
Transition from pushing your product to feeling the market pull it out of you.
Help the user with measuring product-market fit using insights from 16 guests and posts across Lenny's Podcast and Newsletter.
Jag Duggal: "We rarely scale a project, a product we've launched, until we know the Sean Ellis score and we know that it's hit a threshold that we find really compelling."
Use an objective threshold like the Sean Ellis score to validate fit before scaling. Set a strict target of at least 40 percent of users feeling very disappointed if the product disappeared.
Raaz Herzberg: "We really felt the type of questions change, right? Silly. The call sounded like, again, "How are you pricing this, or when can we start doing a POV?" I think naturally, as human beings, you have a bias to look for affirmation, versus a bias for what you don't want to hear."
True fit is revealed when customer feedback shifts from polite interest to urgent, practical questions about pricing and implementation. Treat generic interest as a negative signal.
Rahul Vohra: "You have to deliberately not act on the feedback of many of your early users, and this is at the same time as listening to people intensely and building what people want."
Ignore feedback from most users to focus exclusively on the segment that would be very disappointed without your product. Double down on what that specific cohort loves.
From "How to kickstart and scale a consumer business—Step 5: RETAIN: Iterate until enough people stick around": "Do whatever is required to get to product-market fit. Including changing out people, rewriting your product, moving into a different market, telling customers no when you don’t want to, telling customers yes when you don’t want to, raising that fourth round of highly dilutive venture capital—whatever is required."
Iterating toward fit requires an obsessive focus on retention signals. Be prepared to rewrite the product or switch markets if your cohort retention curves do not eventually flatten.
Grant Lee: "And then we'd look at signups, and you'd get that initial spike in signups, and then they sort of flatten out. We were still getting new users every day, but it was clear we didn't have strong word of mouth. There wasn't strong organic virality."
Measure the delta between temporary launch spikes and your long-term organic baseline. True fit is characterized by sustained word-of-mouth growth rather than marketing-driven peaks.
Adam Grenier: "Start by assuming you no longer have product market fit, because you had product market fit in a different market. It's a different market now, so you have to start over."
Assume previous product-market fit is lost when entering a significantly changed macroeconomic environment. Shifts in the market can fundamentally alter your customer base and their needs.
Todd Jackson: "We've published dozens of articles on the First Round Review, and we have found a very consistent set of patterns, demand satisfaction, and efficiency. But the interesting thing is that you don't go for all three of them from the very beginning."
Finding fit is a sequential journey that starts with demand, moves to satisfaction, and finally focuses on efficiency. Do not try to optimize for efficiency until you have proven satisfaction.
See references/artifacts.md for the full list with details.
For all 50 sourced insights from 16 guests, see references/guest-insights.md
Combine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall.
Combine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall.
Combine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall.
| Automate Twitter/X with posting, engagement, and user management via inference.sh CLI. social media automation, x automation, tweet scheduler, twitter integration, post tweet, twitter post, x post, send tweet
| Query the Sequence Read Archive (SRA), retrieve scientific publications, and analyze genomics metadata using the SRAgent toolkit. Supports accession conversion (GSE→SRX→SRR), BigQuery metadata queries, manuscript downloads from multiple sources, and scRNA-seq technology identification. Use when working with SRA/GEO datasets, finding publications, or analyzing single-cell sequencing experiments.
Framework for building competitive landscape decks — market positioning, competitor deep-dives, comparative analysis, strategic synthesis. Use when the user asks for a competitive landscape, competitor analysis, peer comparison, market positioning assessment, strategic review, or investment memo deck. Also triggers on "who are the competitors to X", "benchmark X against peers", "build a market map", or any request to systematically evaluate competitive dynamics across an industry.
> Analyzes unit economics by product or service using PayPal merchant insights and QuickBooks cost data, benchmarks against inflation and cost changes, and shows pricing-scenario data (e.g. "a 5% increase historically correlates with ~3% volume drop"). Surfaces analysis only — does not recommend a price. Use when the user asks about raising prices, pricing, margin analysis, what to charge, whether costs are eating into profit, or how a price change might affect their business. Trigger even if the user doesn't say "margin" explicitly — phrases like "am I making enough?", "should I charge more?", or "my costs are going up" all call for this skill.
Define a dataset's metadata profile — infer a Frictionless Table Schema from its data, add Data Package metadata (license, sources, keywords), and write it into datasets.json so the showcase renders a typed field table. Extend or customize via the L0-L3 profile ladder. Use when a registered dataset needs field types, constraints, or catalog metadata before publishing.
Take refoundai/measuring-pmf 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.