Anything2Skill: Compiling External Knowledge into Reusable Skills for Agents
2026-06-08 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors address a problem in retrieval-augmented generation (RAG) systems, which typically only retrieve pieces of information but don’t reuse learned skills. They propose Anything2Skill, a method that turns external knowledge into reusable and structured skills for agents to use directly. This approach organizes skills in a SkillBank, making agents better at following procedures without re-deriving them each time. Their experiments show that combining Anything2Skill with RAG improves task success compared to using RAG alone.
Retrieval-augmented generationProcedural knowledgeSkill extractionSkill taxonomySkillBankPlan-and-expandDeclarative evidenceAgent inferenceTask success rate
Authors
Qianjun Pan, Yutao Yang, Junsong Li, Jie Zhou, Kai Chen, Xin Li, Qin Chen, Liang He
Abstract
Retrieval-augmented generation (RAG) enables agents to access external knowledge at inference time, but it primarily retrieves fragmented declarative evidence, leaving agents to repeatedly infer task procedures from passages, manuals, examples, logs, or trajectories. This raises a fundamental question: can skills extracted from external knowledge bases be installed into an agent, enabling it to rapidly approximate domain expertise? In this paper, we propose Anything2Skill, a taxonomy-guided framework that compiles heterogeneous external knowledge into reusable, retrievable, and executable skills for agents. Given a corpus of knowledge records, \textsc{Anything2Skill} first decomposes each record into evidence windows and performs plan-and-expand skill extraction under a skill-tree prior. The extracted candidates are then converted into structured skill contracts that specify invocation conditions, contraindications, action moves, workflow steps, constraints, output specifications, supporting evidence, and confidence scores. To construct a deployable procedural memory, Anything2Skill manages the extracted skills in a persistent SkillBank through taxonomy-aware compilation, registry-level reconciliation, lifecycle tracking, versioned updates, and visible skill-tree projection. At inference time, agents retrieve both task-specific passages from the original knowledge base and relevant procedural skills from the SkillBank, allowing RAG to provide declarative evidence while compiled skills provide reusable procedural guidance. Experiments on qsv and GitHub-CLI show that Anything2Skill combined with RAG achieves 98.85\% and 94.10\% success rates, respectively, substantially outperforming RAG-only agents. These results suggest that compiling latent procedural knowledge into explicit skills is an effective way to extend retrieval-augmented agents from knowledge access toward capability reuse.