# Skill Retrieval Augmentation for Agentic AI

arXiv: https://arxiv.org/abs/2604.24594 · HTML: https://mlsanity.com/paper/2604.24594/

## Key facts

- Cited by 44 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 42 of its 58 references that are in the map.
- First posted on arXiv 27 Apr 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, 30 of 42)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [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)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [Voyager: An Open-Ended Embodied Agent with Large Language Models](https://mlsanity.com/paper/2305.16291/) (2023)
- [ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs](https://mlsanity.com/paper/2307.16789/) (2023)
- [From Local to Global: A Graph RAG Approach to Query-Focused Summarization](https://mlsanity.com/paper/2404.16130/) (2024)
- [Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning](https://mlsanity.com/paper/2503.09516/) (2025)
- [Gorilla: Large Language Model Connected with Massive APIs](https://mlsanity.com/paper/2305.15334/) (2023)
- [Unsupervised Dense Information Retrieval with Contrastive Learning](https://mlsanity.com/paper/2112.09118/) (2021)
- [HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face](https://mlsanity.com/paper/2303.17580/) (2023)
- [C-Pack: Packed Resources For General Chinese Embeddings](https://mlsanity.com/paper/2309.07597/) (2023)
- [BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions](https://mlsanity.com/paper/2406.15877/) (2024)
- [Active Retrieval Augmented Generation](https://mlsanity.com/paper/2305.06983/) (2023)
- [Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents](https://mlsanity.com/paper/2304.09542/) (2023)
- [SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks](https://mlsanity.com/paper/2602.12670/) (2026)
- [ToolQA: A Dataset for LLM Question Answering with External Tools](https://mlsanity.com/paper/2306.13304/) (2023)
- [TheoremQA: A Theorem-driven Question Answering dataset](https://mlsanity.com/paper/2305.12524/) (2023)
- [MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning](https://mlsanity.com/paper/2205.00445/) (2022)
- [Unsupervised Real-Time Hallucination Detection based on the Internal States of Large Language Models](https://mlsanity.com/paper/2403.06448/) (2024)
- [Agent Skills: A Data-Driven Analysis of Claude Skills for Extending Large Language Model Functionality](https://mlsanity.com/paper/2602.08004/) (2026)
- [MedCalc-Bench: Evaluating Large Language Models for Medical Calculations](https://mlsanity.com/paper/2406.12036/) (2024)
- [LogicBench: Towards Systematic Evaluation of Logical Reasoning Ability of Large Language Models](https://mlsanity.com/paper/2404.15522/) (2024)
- [DRAGIN: Dynamic Retrieval Augmented Generation based on the Information Needs of Large Language Models](https://mlsanity.com/paper/2403.10081/) (2024)
- [MemoryBench: A Benchmark for Memory and Continual Learning in LLM Systems](https://mlsanity.com/paper/2510.17281/) (2025)
- [An Analysis of Fusion Functions for Hybrid Retrieval](https://mlsanity.com/paper/2210.11934/) (2022)
- [CHAMP: A Competition-level Dataset for Fine-Grained Analyses of LLMs' Mathematical Reasoning Capabilities](https://mlsanity.com/paper/2401.06961/) (2024)
- [Parametric Retrieval Augmented Generation](https://mlsanity.com/paper/2501.15915/) (2025)

## Built on by (most cited first, 30 of 44)

- [A Comprehensive Survey on Agent Skills: Taxonomy, Techniques, and Applications](https://mlsanity.com/paper/2605.07358/) (2026)
- [WikiSkill: Compiling Agent Experience into Persistent Knowledge for Skill Evolution](https://mlsanity.com/paper/2608.27454/) (2026)
- [SKILLC: Learning Autonomous Skill Internalization in LLM Agents via Contrastive Credit Assignment](https://mlsanity.com/paper/2605.27899/) (2026)
- [SkCC: Portable and Secure Skill Compilation for Cross-Framework LLM Agents](https://mlsanity.com/paper/2605.03353/) (2026)
- [Agent Skill Evaluation and Evolution: Frameworks and Benchmarks](https://mlsanity.com/paper/2606.11435/) (2026)
- [Skill Is Not Document: Query-Conditioned Compatibility for LLM Agent Skill Routing](https://mlsanity.com/paper/2606.03565/) (2026)
- [Not All Skills Help: Measuring and Repairing Agent Knowledge](https://mlsanity.com/paper/2606.15390/) (2026)
- [Generative Skill Composition for LLM Agents](https://mlsanity.com/paper/2606.32025/) (2026)
- [Dynamic Agent Skills: A Lifecycle Survey and Taxonomy of Evolving Skill Libraries](https://mlsanity.com/paper/2607.10113/) (2026)
- [Enhancing Judgment Document Generation via Agentic Legal Information Collection and Rubric-Guided Optimization](https://mlsanity.com/paper/2605.02011/) (2026)
- [Benchmarking LLM-as-a-Judge for Long-Form Output Evaluation](https://mlsanity.com/paper/2606.01629/) (2026)
- [Right Family, Wrong Skill: Evaluating Risk Exposure in Agent Skill Retrieval](https://mlsanity.com/paper/2606.10388/) (2026)
- [Task Decomposition-Guided Reranking for Adaptive Agent Skill Retrieval](https://mlsanity.com/paper/2607.06283/) (2026)
- [SkillSight: Calibrating Generic Content Bias for Skill Retrieval](https://mlsanity.com/paper/2607.18785/) (2026)
- [Field-Aware Agent Skill Retrieval](https://mlsanity.com/paper/2608.02880/) (2026)
- [Beyond Task-Only Matching: Personalized Skill Routing with Counterfactual Evaluation](https://mlsanity.com/paper/2608.28241/) (2026)
- [Skill or Skip? Learning Selective Skill Invocation in Agentic Tasks via Dual-Granularity Preference Learning](https://mlsanity.com/paper/2606.00510/) (2026)
- [SkillReason: Reasoning-Enhanced Agent Skill Retrieval for Implicit User Requests](https://mlsanity.com/paper/2608.08640/) (2026)
- [From Relevance to Execution Utility: Reward-Aware Dynamic Execution Gating for Skill-Based LLM Agents](https://mlsanity.com/paper/2608.09168/) (2026)
- [When Tool-Backed Skill Retrieval Fails: Source-Style Collapse in Executable Capability Retrieval](https://mlsanity.com/paper/2608.16502/) (2026)
- [Optimal Skill Selection for LLM Agents with Provable Bicriteria Guarantees](https://mlsanity.com/paper/2608.19993/) (2026)
- [Coalition-Aware Skill Reliability for Self-Evolving Agents](https://mlsanity.com/paper/2608.22610/) (2026)
- [Adaptive Multi-Resolution Procedural Knowledge Compression for Large Language Models](https://mlsanity.com/paper/2606.12203/) (2026)
- [SkillTrace: Traversing a Query-Skill Graph for Composable LLM Agents](https://mlsanity.com/paper/2608.02356/) (2026)
- [SkillZip: Contract-Preserving Graph Compression for Scalable Agent Skill Libraries](https://mlsanity.com/paper/2608.05604/) (2026)
- [SkillAligner: Treating Retrieved Skills as Adaptable Drafts at Execution Time](https://mlsanity.com/paper/2608.06880/) (2026)
- [SkillGate: Training In-Policy Skill Selection in Long-Horizon Agents](https://mlsanity.com/paper/2608.18852/) (2026)
- [COBRA-Skills: Contextual Bandit-Guided Evolution for Agent Skill Optimization](https://mlsanity.com/paper/2609.11682/) (2026)
- [The Router Within: Eliciting Native Skill Routing from a Frozen LLM](https://mlsanity.com/paper/2609.15982/) (2026)
- [A Wrong Turn Does Not Ruin the Journey: Deviation-Guided Skill Self-Evolution for LLM Agents](https://mlsanity.com/paper/2609.29154/) (2026)

## Abstract (arXiv)

As large language models (LLMs) evolve into agentic problem solvers, they increasingly rely on external, reusable skills to handle tasks beyond their native parametric capabilities. In existing agent systems, the dominant strategy for incorporating skills is to explicitly enumerate available skills within the context window. However, this strategy fails to scale: as skill corpora expand, context budgets are consumed rapidly, and the agent becomes markedly less accurate in identifying the right skill. To this end, this paper formulates Skill Retrieval Augmentation (SRA), a new paradigm in which agents dynamically retrieve, incorporate, and apply relevant skills from large external skill corpora on demand. To make this problem measurable, we construct a large-scale skill corpus and introduce SRA-Bench, the first benchmark for decomposed evaluation of the full SRA pipeline, covering skill retrieval, skill incorporation, and end-task execution. SRA-Bench contains 5,400 capability-intensive test instances and 636 manually constructed gold skills, which are mixed with web-collected distractor skills to form a large-scale corpus of 26,262 skills. Extensive experiments show that retrieval-based skill augmentation can substantially improve agent performance, validating the promise of the paradigm. At the same time, we uncover a fundamental gap in skill incorporation: current LLM agents tend to load skills at similar rates, regardless of whether a gold skill is retrieved or whether the task actually requires external capabilities. This shows that the bottleneck in skill augmentation lies not only in retrieval but also in the base model's ability to determine which skill to load and when external loading is actually needed. These findings position SRA as a distinct research problem and establish a foundation for the scalable augmentation of capabilities in future agent systems.