Skill‑V is a verifiable self‑evolving skill library for interactive agents that represents skills as versioned, falsifiable contracts linking semantic intent to observable behavior. The system uses task failures to add new skills and contract‑outcome disagreements to revise existing skill boundaries, ensuring revisions preserve semantic constraints and pass non‑regression tests on historical evidence. An applicability‑aware filter excludes confidently inapplicable skill candidates, leading to high success rates on ALFWorld (95.3%) and WebShop (85.9%) while keeping the library more compact than growth‑only baselines.
arXiv:2610.10088v1 Announce Type: cross Abstract: Self-evolving agents distill task-solving experience into skills for future reuse, but these skills can encode incorrect procedures or non-transferab...
arXiv:2607. 10113v1 Announce Type: new Abstract: Large language model agents increasingly store reusable procedures outside the model.
arXiv:2606. 20659v2 Announce Type: replace Abstract: Agent skills encode reusable procedural knowledge for large language model (LLM) agents, and existing benchmarks show that such skills can improve task-level performance.
arXiv:2606. 14239v1 Announce Type: new Abstract: Agent skills are structured procedural packages that guide frozen LLM agents in specialized workflows.
arXiv:2606. 09316v1 Announce Type: new 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.
arXiv:2607. 01874v1 Announce Type: new Abstract: Skills are becoming a reusable operational layer for LLM agents, encoding SOPs, domain rules, tool workflows, scripts, and validation routines.
arXiv:2606. 01139v1 Announce Type: new Abstract: Agent skills are procedural artifacts that enable LLM agents to execute workflows, verify constraints, and recover from failures.
arXiv:2608. 15165v1 Announce Type: new Abstract: Large language model (LLM) agents can continually improve without parameter updates by converting historical experience into reusable procedural knowledge.
arXiv:2608. 06880v1 Announce Type: new Abstract: General-purpose skills promise reusable procedural knowledge for language agents, yet semantic relevance does not guarantee execution utility: a retrieved skill may encode assumptions that conflict with the current task, execution environment, or other retrieved skills.
arXiv:2609.07255v1 Announce Type: new Abstract: Language-model agents increasingly rely on skills: reusable procedural knowledge for reasoning, tool use, and interaction. Existing work studies how sk...
arXiv:2608. 06891v1 Announce Type: new Abstract: Agent skills provide reusable procedural knowledge that helps agents solve specialized tasks.
arXiv:2608. 11888v1 Announce Type: new Abstract: Agent skills are the de facto mechanism for extending LLM agents with reusable guidance.