# Imagine Before Retrieval: Prospective Skill Retrieval for LLM Agents

arXiv: https://arxiv.org/abs/2609.01642 · HTML: https://mlsanity.com/paper/2609.01642/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Agent skill learning and evolution, one of 16 directions in LLM agents and tool use.
- Builds on 14 of its 23 references that are in the map.
- First posted on arXiv 28 Aug 2026.

## Placed in

- LLM agents and tool use / [Agent skill learning and evolution](https://mlsanity.com/agents/agent-skill-learning-and-evolution/)

## Builds on (most cited first, 14 of 14)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models](https://mlsanity.com/paper/2506.05176/) (2025)
- [Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://mlsanity.com/paper/2212.03533/) (2022)
- [C-Pack: Packed Resources For General Chinese Embeddings](https://mlsanity.com/paper/2309.07597/) (2023)
- [Precise Zero-Shot Dense Retrieval without Relevance Labels](https://mlsanity.com/paper/2212.10496/) (2022)
- [SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks](https://mlsanity.com/paper/2602.12670/) (2026)
- [How Well Do Agentic Skills Work in the Wild: Benchmarking LLM Skill Usage in Realistic Settings](https://mlsanity.com/paper/2604.04323/) (2026)
- [FIRST: Faster Improved Listwise Reranking with Single Token Decoding](https://mlsanity.com/paper/2406.15657/) (2024)
- [SkillRet: A Large-Scale Benchmark for Skill Retrieval in LLM Agents](https://mlsanity.com/paper/2605.05726/) (2026)
- [SkillFlow: Scalable and Efficient Agent Skill Retrieval System](https://mlsanity.com/paper/2504.06188/) (2025)
- [Group of Skills: Group-Structured Skill Retrieval for Agent Skill Libraries](https://mlsanity.com/paper/2605.06978/) (2026)
- [Task Decomposition-Guided Reranking for Adaptive Agent Skill Retrieval](https://mlsanity.com/paper/2607.06283/) (2026)

## Built on by (most cited first, 3 of 3)

- [SkillApt: Learning When to Activate Agent Skills from Counterfactual Evidence](https://mlsanity.com/paper/2609.26863/) (2026)
- [What Does a Skill Actually Do? Estimands and Evaluation Validity for Tool and Skill Use in LLM Agents: A Critical Review](https://mlsanity.com/paper/2609.33153/) (2026)
- [What Can Component-Replacement Evidence Establish? A Critical Scoping Review of Local Decisions in LLM Agents](https://mlsanity.com/paper/2609.39989/) (2026)

## Abstract (arXiv)

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.