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"text": "// Continual Learning from Experience and Skills //\n\nSkills are so good when you combine them properly with MCP & CLIs.\n\nI have found that Skills can significantly improve tool usage of my coding agents.\n\nThe best way to improve them is to regularly document improvements, patterns, and things to avoid.\n\nSelf-improving skills don't work that well (yet).\n\nCheck out this related paper on the topic:\n\nIt introduces XSkill, a dual-stream continual learning framework.\n\nAgents distill two types of reusable knowledge from past trajectories: experiences for action-level tool selection, and skills for task-level planning and workflows.\n\nBoth are grounded in visual observations.\n\nDuring accumulation, agents compare successful and failed rollouts via cross-rollout critique to extract high-quality knowledge. During inference, they retrieve and adapt relevant experiences and skills to the current visual context.\n\nEvaluated across five benchmarks with four backbone models, XSkill consistently outperforms baselines. On Gemini-3-Flash, the average success rate jumps from 33.6% to 40.3%. Skills reduce overall tool errors from 29.9% to 16.3%.\n\nAgents that accumulate and reuse knowledge from their own trajectories get better over time without parameter updates.\n\nI have now seen two papers this week with similar ideas.\n\nPaper: https://t.co/YXrHcJ6Zim\n\nLearn to build effective AI agents in our academy: https://t.co/1e8RZKs4uX",
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