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  "text": "Agent skills look great in demos.\n\nHand them a curated toolbox, and they shine.\n\nBut what happens when the agent has to find the right skill from a large, unfiltered collection on its own?\n\nNew research benchmarks LLM skill usage in realistic settings and finds that performance gains degrade consistently as conditions become more realistic, with pass rates approaching no-skill baselines.\n\nThe fix is to introduce query-specific skill refinement, which substantially recovers lost performance. On Terminal-Bench 2.0, this approach improved Claude Opus 4.6's pass rate from 57.7% to 65.5%.\n\nAs skill and tool ecosystems grow, agents won't have curated toolboxes handed to them.\n\nThey'll face noisy, overlapping, and irrelevant options.\n\nPaper: https://t.co/Dm7JxredRI\n\nLearn to build effective AI agents in our academy: https://t.co/LRnpZN7L4c",
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