AI技能为何时灵时不灵
给AI智能体加「技能包」，本意是让它更靠谱。但新研究拆开看：技能真正起作用，不是因为它给AI补了知识，而是把混乱的执行过程变成稳定的操作步骤——这类情况占65.7%，而「注入新知识」只占4.5%。可一旦技能库变大，AI找对技能的能力就崩：库从5个扩到100个，实际命中率从29.6%掉到3.3%。也就是说，技能本身没问题，但「检索」成了新瓶颈，而且技能在场景不匹配、假设太脆时照样翻车。这不是你明天能用的技巧，但它解释了为什么AI「会但不够稳」，也提醒：与其堆技能，不如先解决怎么让AI在关键时刻选对技能。
📄 原文摘要(英文)
Skills have emerged as a practical and effective approach for enhancing LLM agents at inference time through structured packages of knowledge. However, existing evaluations largely measure whether skills improve aggregated task success, leaving a more fundamental question underexplored: \textbf{When do skills help, why do they work, and where do they fail?} Through controlled experiments across various benchmarks, agent harnesses and LLMs, we isolate the effects of representation, outcome annotation, retrieval difficulty, and cross-framework robustness of skills. To further answer this question, we design a contrastive study that combines controlled quantitative experiments with paired trajectory analysis. We normalize 8,135 trial records from controlled experiments and retain 238 valid unique labels from 240 open-coded records. We consolidate these observations into a taxonomy of three high-level categories and twelve skill-use modes: skills work when noisy trajectories become procedural anchors that stabilize execution. Skills improve over Workflow Memory by 6.06 points in matched comparisons. Procedural anchoring accounts for 65.7\% of skill cases, versus 4.5\% for explicit knowledge injection, showing that skills stabilize action rather than inject missing facts. Retrieval is a separate bottleneck: as pools grow from 5 to 100, actual-use precision falls from 29.6\% to 3.3\%. Confusable distractors impair offline identification, yet downstream success remains stable; exact ground-truth invocation is neither sufficient nor necessary. Skills fail under brittle assumptions, incompatible contexts, or insufficient adaptation. These findings move evaluation beyond aggregate success rates and guide reliable self-evolving agents.