From 36% to 100%: How Self-Improving Agents Write Their Own Skills — Rafal Wilinski, Runlayer

This post contributes to the field of AI agent development by proposing a system where agents can autonomously learn and generate new skills by distilling knowledge from their successful and failed execution traces. It highlights the limitations of current skill development (developer-centric, time-consuming, client-dependent) and introduces 'Skills Over MCP' (Multi-Cloud Platform) as a distribution layer. By analyzing agent traces, particularly failures, the system creates reusable skills that significantly improve task success rates (e.g., from 36% to 100%), fostering an organizational knowledge flywheel that retains expertise even as underlying models evolve.