What if a skill could improve itself? I built one. Then let it loose on its own source code for 10 rounds. The result: 52 versions. 109 experiments. 100 adversarial eval prompts conquered. Zero discards. (I'm not making this up, check in comments) Meet the Autoresearch Loop skill. Based on Karpathy's autoresearch methodology, it systematically improves anything measurable: other skills, n8n workflows, system prompts, SOPs, business processes. The loop is simple: Define "better" → Lock everything else → Change one thing → Test → Keep or discard → Repeat The execution is not. Why is it so good? We ran it against itself across 10 rounds. Each round we wrote 10 new, harder eval prompts targeting gaps the skill didn't cover yet. Each round it started near 0% and climbed to 100%. By the end it handles things like: • Emergency production hotfixes mid-loop • Multi-stakeholder metric conflicts • LLM-as-judge scoring drift • Artifact forking when scope grows too broad • Campaign cost/ROI tracking across months • When to abandon a loop entirely vs retire an artifact 462 lines. Every single one battle-tested against a prompt specifically designed to break it. This is the skill that makes other skills better. The proof is in the methodology: it improved itself 52 times, until we ran out of ways to challenge it. Download it at give it a try with any of your current skills!

Here is the security guide I promised, packed with everything I've learned over the last few weeks about safely deploying and managing your own OpenClaw instance. Enjoy! https://is-your-openclaw-safe-he-qjg7nak.gamma.site/

Hey everyone 👋 I owe you an apology. I've been MIA for a while and I know it. Honestly? I've been deep in the 8th dimension building with OpenClaw and completely lost track of time. You know how it goes. You start setting up one AI agent, then it's two, then you're orchestrating a team of eight and suddenly two months have disappeared. But I'm back. And I'm bringing everything I learned with me. I'm starting a new series here where I'll share everything I've picked up (and everything I'm still figuring out) about setting up OpenClaw instances from scratch. The real stuff. Not the "it works in the docs" version, but the "I broke it six times before it worked" version. Things like: 🧠 How to give your agents actual memory that survives restarts ⚡️ Running everything on a $5/month server (no GPU needed) 🤖 Orchestrating multiple agents that actually talk to each other 🔧 The gotchas nobody warns you about 💰 Keeping costs stupid low while running production agents I already published my first resource — a full memory system setup you can install on any OpenClaw agent in 5 minutes: https://clawhub.ai/autosolutionsai-didac/agent-memory-setup More coming soon. If you're building with OpenClaw or thinking about it, stick around. This is going to be a fun ride. Let me know in the comments what you'd want me to cover first 👇

Hi guys! Is AMA happening today?

Hello @Didac Fernandez just saw your post on LinkedIn about Clawdbot security guide. Will you be sharing here?