The best skills come from analyzing your full usage history, finding repeated correction patterns, and writing guidance that steers the model away from documented failures. Writing a skill once and never revisiting it is almost always wrong — treat skills like living documents.
Where this was said
At 16:28 · chapter starts 14:20
Matt Pocock's missed AIE talk — posted online instead — becomes the launching pad for one of the episode's most technically dense segments. His core argument: don't let AI write your Claude Code skills. The reason is elegant and non-obvious. [1] — Theo "The fundamental problem with letting AI write your Claude Code skills is that models write what they already do by default. Skills are supp…" 14:00 The entire purpose of a skill is to give the model behavior it doesn't have by default. When the AI writes the skill itself, it naturally gravitates toward behaviors it already exhibits, creating elaborate no-ops stuffed with filler like 'be really thorough' — instructions the model would follow anyway. Ben admits he's guilty of this pattern himself. The right workflow, Theo argues, is to go through your full model usage history, find every time you had to correct the model, categorize those failures, and write skills that specifically redirect the model away from those documented failures. Both hosts then open their .clod directories live and compare skill counts — Ben has 8 in his global directory and over 100 in his Hermes agent (after pruning 50), while Theo has trimmed aggressively to just a handful. The discussion covers workflow skills, framework-specific skills (Svelte, Effect), and the important distinction between things that belong in Agents.md versus a full skill.
Ben's Hermes personal agent had well over 100 skills even after he deleted about 50, illustrating how quickly skill files accumulate.
With AI coding tools like Cursor, Bhanu replicates an existing free tool for a new keyword in under 5 minutes. What used to be a multi-day build is now a lunch-break task.
Ahrefs, SiteGPT, Cal.com, PostHog, Datafast, Sibyl AI, Bento, Feather, Featurepace, Mintlify, Cloud Code, ChartMogul — Bhanu runs his entire business solo with these 12 tools.
PropGPT runs on React Native with TypeScript and Python for ML, Neon for the database, RevenueCat and Superwall for monetization. LLM costs are $20/month, data APIs $100/month, and after $10K in monthly marketing spend, margins sit at roughly 50%.
Game apps keep users engaged long enough for ads to pay off. Tool apps don't — so if you're building a utility, ads are almost always the wrong call and subscriptions are your only real lever.
Inside SEO Bot, a single button labelled 'Boost My Domain Rating' routes users directly to Listing Bot. That one interaction converts a user of one tool into a user of two — without any marketing cost.
Directory listings are a powerful but underrated growth channel — but only if your product is genuinely interesting enough to earn the click. AI products have a natural advantage here because they're easy to package in a compelling, clickable way.
The fastest path to Twitter growth isn't volume — it's forming sharp opinions about how the platform works and sharing them immediately. People cluster around those who understand the rules and say so out loud.
Sam had no coding knowledge, so he used ChatGPT voice mode to generate his entire codebase and copy-pasted it into Notepad. A friend later introduced him to Cursor, and he never looked back.
Copy days of Discord chat history, paste it into ChatGPT, and ask it to list recurring pain points. The ones that come up most often are your best product bets.
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