Effective skill identification is crucial for LLM agents to reuse task-solving experience for complex task execution, which however remains challenging as agents must accurately identify useful skills from a large skill gallery and invoke them on demand. Existing studies have explored various strategies for skill retrieval and invocation, but largely overlook the natural discrepancy between task descriptions and skill documents. To be specific, our empirical study reveals that task descriptions mainly specify task objectives, whereas skill documents describe the required capabilities and procedural guidance for achieving the objectives. Such a so-called Task--Skill Misalignment (TSM) problem would cause desirable skills to be neglected during retrieval or remain unselected during invocation, thus hindering task execution. As a remedy, inspired by human prospective cognition, we propose SkillDreamer, a prospective skill identification framework to mitigate TSM. In brief, SkillDreamer first infers execution-required capabilities and imagines their realizations as pseudo skills, thereby translating task objectives into execution-oriented guidance for skill retrieval. As execution proceeds, SkillDreamer reevaluates the capabilities required by emerging subtasks to determine which skills should be invoked, thereby aligning skill invocation with evolving execution needs. Extensive experiments on three representative agent skill benchmarks not only verify the effectiveness of SkillDreamer in mitigating TSM, but also demonstrate its generalizability across diverse retrievers and LLM agents. The code will be released upon acceptance.

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