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At the AI Engineer World's Fair on July 1, 2026, Philipp Schmid, a Staff Engineer on the Gemini API and agents team at Google DeepMind, asked a room full of engineers a simple question: who uses skills with their coding agents? Every hand went up. Who has evals for those skills? Almost none. That gap is the entire talk, which Schmid also wrote up on his own blog, philschmid.de/testing-skills . His team indexed the skills ecosystem through SkillsBench and found the same pattern everywhere: people ship a SKILL.md file after two manual test runs and move on. It looks fine in a demo. It quietly corrupts outputs in production, because bad skills don't crash. They just make the agent confidently wrong. If you've already read our breakdown of Matt Pocock's Claude Code skills library , think of this as the other half of that story: what happens once you've installed a skill and it's actually running against real prompts. Key Takeaways SkillsBench indexed 47,000+ un...

You sit down with Claude Code. You've got a plan. Three hours in, the context window is full, the conversation is tangled, and you've built half of the wrong thing. That's not a Claude problem. It's a workflow problem — and it's exactly what Matt Pocock designed his open-source skills library to fix. Pocock, best known as the creator of Total TypeScript, published these skills straight from his own .claude directory with a clear pitch: "Skills for Real Engineers." No bloated process frameworks. No opinionated orchestration. Just small, composable slash commands you can hack and extend. In June 2026, the mattpocock/skills repo has 150,773 GitHub stars and 13,032 forks — accumulated in under 5 months since its February 2026 release. That's the kind of adoption that tells you developers are hitting the same walls and finding the same fixes. This post covers the three productivity skills: grill-me , handoff , and teach . Key Takeaways As of June 2026, th...

  Watched a video by DSquaredLabs on " No more re-explaining: Give your AI agent a memory graph " — here's what stuck with me and why I think this three-tool system is worth your attention If you work on a large codebase with AI coding tools, you already know the ritual. Open a new chat, paste your file structure, explain your stack, describe how the modules connect — and repeat the whole thing tomorrow. It is not a minor inconvenience. It is a compounding productivity tax that gets worse as your project grows. This article breaks down a three-tool system designed to fix exactly that. If you want the full walkthrough with a live demo on a real monorepo, the video at the bottom covers everything in depth — but the core concepts are worth understanding before you dive in. The Problem With How Most Developers Use AI Today Most AI-assisted coding workflows are missing three things that every good engineering team actually has: A map of the codebase A process for how w...