Skill Retrieval Augmentation for Agentic AI

• URL: http://arxiv.org/abs/2604.24594v1
• Date: Mon, 27 Apr 2026 15:19:59 GMT
• Title: Skill Retrieval Augmentation for Agentic AI
• Abstract summary: In existing agent systems, the dominant strategy for incorporating skills is to enumerate available skills within the context window.<n>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.<n>SRA-Bench is the first benchmark for evaluation of the full SRA pipeline, covering skill retrieval, skill incorporation, and end-task execution.
• Score: 28.71123853744993
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

Related papers

• Coalition-Aware Skill Reliability for Self-Evolving Agents [49.43736235959317]
  We investigate whether accumulated skills in an agent's skill bank actually make positive mechanistic contributions.<n>We show that coalition pollution, where bank-level gains conceal negative coalition-level skill contributions, and cross-domain utility reversal, where source-beneficial skills reverse their effects after transfer.<n>These findings motivate two reliability interventions: coalition-aware skill selection during skill accumulation and label-free skill masking after transfer.
  arXiv  Detail & Related papers  (2026-08-23T21:35:22Z)
• SkillAudit: From Fixed-Suite Benchmarking to Skill-Centered Assessment [10.850946672552277]
  We introduce SkillAudit, an end-to-end framework for skill-centered assessment.<n>It generates a comprehensive, multi-dimensional evaluation report spanning utility, efficiency/cost, and safety.<n>After scanning top-ranked real-world skill packages, we found that over 7% of skills are at risky status.
  arXiv  Detail & Related papers  (2026-06-21T17:40:05Z)
• A Framework for Evaluating Agentic Skills at Scale [0.4509560087514856]
  Agent skills are structured, reusable knowledge artifacts that augment agent capabilities.<n>No reusable methodology exists for evaluating an individual skill.<n>We present an evaluation framework that lets a skill author construct realistic tasks.
  arXiv  Detail & Related papers  (2026-06-16T11:46:56Z)
• SkillComposer: Learning to Evolve Agent Skills for Specification and Generalization [99.18305770138677]
  Agent skills are reusable strategies that guide agent reasoning and action.<n>Current skill construction methods treat the problem as one-shot extraction.<n>We introduce SkillComposer, a framework that decomposes skill construction into three learnable operations.
  arXiv  Detail & Related papers  (2026-06-04T12:16:08Z)
• OpenSkillEval: Automatically Auditing the Open Skill Ecosystem for LLM Agents [15.598856888948093]
  We present textscOpenSkillEval, an automatic evaluation framework for both skill-augmented agent systems and the skills themselves.<n>Instead of relying on static benchmarks, textscOpenSkillEval automatically constructs realistic task instances from evolving real-world artifacts.<n>Using more than 600 dynamically generated task instances and 30 open-source skills, we conduct a systematic evaluation of state-of-the-art models and agent frameworks.
  arXiv  Detail & Related papers  (2026-05-22T14:09:41Z)
• SkillGen: Verified Inference-Time Agent Skill Synthesis [60.927977774369516]
  SkillGen is a framework that synthesizes a single auditable skill from trajectories generated by a base agent.<n>It identifies reusable success patterns, recurring failure modes, and behaviors that appear in nearby successes but are missing from failures.<n>A key novelty in SkillGen is that we model agent skills as interventions to empirically verify the net effect of skills on the overall performance.
  arXiv  Detail & Related papers  (2026-05-09T19:24:11Z)
• SkillRet: A Large-Scale Benchmark for Skill Retrieval in LLM Agents [8.628588436048952]
  We introduce SkillRet, a large-scale benchmark for skill retrieval in LLM agents.<n>It provides 63,259 training samples and 4,997 evaluation queries with disjoint skill pools.<n>Off-the-shelf models struggle on realistic large-scale skill libraries, and prior skill-retrieval models still leave substantial headroom.
  arXiv  Detail & Related papers  (2026-05-07T06:18:11Z)
• From Context to Skills: Can Language Models Learn from Context Skillfully? [56.5857437218136]
  Ctx2Skill is a self-evolving framework that autonomously discovers, refines, and selects context-specific skills.<n>It can be plugged into any language model to obtain better context learning capability.
  arXiv  Detail & Related papers  (2026-04-30T09:53:15Z)
• How Well Do Agentic Skills Work in the Wild: Benchmarking LLM Skill Usage in Realistic Settings [29.3744517703302]
  We study the first comprehensive study of skill utility under progressively challenging realistic settings.<n>Our findings reveal that performance gains degrade consistently as settings become more realistic.<n>We show that query-specific refinement substantially recovers lost performance when the initial skills are of reasonable relevance and quality.
  arXiv  Detail & Related papers  (2026-04-06T00:10:30Z)
• SkillAttack: Automated Red Teaming of Agent Skills through Attack Path Refinement [66.44008181092832]
  LLM-based agent systems increasingly rely on agent skills sourced from open registries to extend their capabilities.<n>We introduce SkillAttack, a framework that verifies skill vulnerability exploitability through adversarial prompting.
  arXiv  Detail & Related papers  (2026-04-05T06:25:11Z)
• SKILL0: In-Context Agentic Reinforcement Learning for Skill Internalization [61.37478254738943]
  Inference-time skill augmentation is fundamentally limited.<n>We introduce SKILL0, an in-context reinforcement learning framework designed for skill internalization.<n>SKILL0 achieves substantial improvements over the standard RL baseline.
  arXiv  Detail & Related papers  (2026-04-02T17:03:05Z)
• EvoSkills: Self-Evolving Agent Skills via Co-Evolutionary Verification [85.3692584167951]
  Anthropic proposes the concept of skills for LLM agents to tackle multi-step professional tasks.<n>A tool is a single, self-contained function, whereas a skill is a structured bundle of interdependent multi-file artifacts.<n>EvoSkills is a self-evolving skills framework that enables agents to autonomously construct complex, multi-file skill packages.
  arXiv  Detail & Related papers  (2026-04-02T06:43:20Z)
• SkillCraft: Can LLM Agents Learn to Use Tools Skillfully? [67.69996753743129]
  We introduce SkillCraft, a benchmark explicitly stress-test agent ability to form and reuse higher-level tool compositions.<n> SkillCraft features realistic, highly compositional tool-use scenarios with difficulty scaled along both quantitative and structural dimensions.<n>We propose a lightweight evaluation protocol that enables agents to auto-compose atomic tools into executable Skills, cache and reuse them inside and across tasks.
  arXiv  Detail & Related papers  (2026-02-28T15:44:31Z)
• SkillNet: Create, Evaluate, and Connect AI Skills [159.47504178122156]
  SkillNet is an open infrastructure designed to create, evaluate, and organize AI skills at scale.<n>Our infrastructure integrates a repository of over 200,000 skills, an interactive platform, and a versatile Python toolkit.
  arXiv  Detail & Related papers  (2026-02-26T14:24:02Z)
• Agent Skills for Large Language Models: Architecture, Acquisition, Security, and the Path Forward [5.124116559484265]
  The transition from monolithic language models to modular, skill-equipped agents marks a defining shift in how large language models (LLMs) are deployed in practice.<n>Rather than encoding all procedural knowledge within model weights, agent skills enable dynamic capability extension without retraining.<n>This survey provides a comprehensive treatment of the agent skills landscape, as it has rapidly evolved during the last few months.
  arXiv  Detail & Related papers  (2026-02-12T21:33:25Z)
• Reinforcement Learning for Self-Improving Agent with Skill Library [14.717149089634718]
  Large Language Model (LLM)-based agents have demonstrated remarkable capabilities in complex reasoning and multi-turn interactions.<n>One promising approach is implementing skill libraries that allow agents to learn, validate, and apply new skills.<n>We propose a Reinforcement Learning (RL)-based approach to enhance agents' self-improvement capabilities with a skill library.
  arXiv  Detail & Related papers  (2025-12-18T21:58:19Z)
• A Theory for Emergence of Complex Skills in Language Models [56.947273387302616]
  A major driver of AI products today is the fact that new skills emerge in language models when their parameter set and training corpora are scaled up. This paper takes a different approach, analysing emergence using the famous (and empirical) Scaling Laws of LLMs and a simple statistical framework.
  arXiv  Detail & Related papers  (2023-07-29T09:22:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.