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  "text": "// OpenClaw-Skill: Searching a Tree of Agent Skills //\n\nIf you build reusable skill libraries for your agents, this one is worth your time.\n\nEquipping LLM agents with effective skills is most of the battle in real systems, and most skill-induction work distills one trajectory at a time into a flat pile of single-shot heuristics.\n\nSearching a tree of candidate skills looks like a better way to get composition and coverage than greedy distillation.\n\nOpenClaw-Skill uses a collective signal to jointly generate, identify, and compose skill nodes across two iterative phases. The output is a structured tree of skills built for diversity and generalization rather than a flat list.\n\nPaper: https://t.co/ZUmd9yHrJs\n\nLearn to build effective AI agents in our academy: https://t.co/1e8RZKs4uX",
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