SkillOpt: Systematic Text-Space Optimizer for Self-Evolving Agent Skills
SkillOpt introduces a principled optimization framework for agent skills, treating the skill document as an external trainable state analogous to model weights. A separate optimizer model converts scored rollouts into bounded edits (add/delete/replace) on a skill document, accepting only edits that improve held-out validation scores. Evaluated across six benchmarks, seven target models, and three execution harnesses (direct chat, Codex, Claude Code), SkillOpt achieves best or tied performance on all 52 evaluated cells, lifting GPT-5.5 no-skill accuracy by up to +24.8 points inside the Codex agentic loop. Optimized skill artifacts also transfer across model scales and execution environments without further optimization.
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Systematic Study of Model-Generated Agent Skills Across the Full Skill Lifecycle
This paper presents a utility-grounded evaluation framework for model-generated agent skills, covering the full lifecycle of experience generation, skill extraction, and skill consumption across five agentic task domains. The authors find that while such skills are beneficial on average, they exhibit non-trivial negative transfer, and that skill utility is independent of model scale or baseline task strength. A key finding is that strong extractors are not necessarily strong consumers and vice versa. The work culminates in a 'meta-skill' that guides extraction toward utility-correlated features, consistently improving skill quality and reducing negative transfer.
SkillGenBench: Benchmarking Skill Generation Pipelines for LLM Agents
SkillGenBench is a new benchmark designed to evaluate the ability of LLM agents to generate correct, reusable, and executable skills from raw repositories and documents, rather than merely using pre-provided skills. It covers two generation regimes (task-conditioned and task-agnostic) and two procedural sources (repository-grounded and document-grounded), with standardized execution-based evaluation protocols. Experiments across multiple skill-generation methods reveal substantial performance variation and distinct failure modes depending on source type. The benchmark aims to establish skill generation as an independent research problem within agent systems.
SkillKit: Portable Skills Layer for AI Coding Agents
SkillKit is an open-source TypeScript project that provides a portable skills abstraction for AI coding agents, enabling installation, translation, and sharing of skills across tools like Claude Code, Cursor, Codex, GitHub Copilot, and 40+ others. The project has accumulated 1,112 stars with 32 added today, indicating moderate community traction. It targets the interoperability gap between the growing ecosystem of AI coding assistants.
agent-skills: Secure Validated Skill Registry for AI Coding Agents
A TypeScript-based open-source skill registry designed to extend AI coding agents including Claude Code, Cursor, GitHub Copilot, and Antigravity with validated, reusable capabilities. The project provides a structured way to add skills to multiple coding agent platforms with a focus on security and validation. It is gaining notable traction with 3,767 total stars and 225 stars added today.
MUSE-Autoskill: Self-Evolving LLM Agents via Skill Lifecycle Management
MUSE-Autoskill introduces a skill-centric agent framework where LLM agents continuously create, store, manage, evaluate, and refine reusable skills across tasks. The system adds skill-level memory that accumulates per-skill experience over time, enabling more effective reuse and cross-agent transfer. Experiments on SkillsBench show improvements in task success, efficiency, and reuse compared to static skill approaches.
SkillWeaver: Compositional Skill Routing for LLM Agents via Decompose-Retrieve-Compose
Researchers introduce SkillWeaver, a framework for compositional skill routing in LLM agents that decomposes complex queries into atomic sub-tasks, retrieves matching skills from a large library, and composes an executable DAG plan. The paper formalizes the Compositional Skill Routing problem and introduces CompSkillBench, a benchmark of 300 compositional queries over 2,209 real MCP server skills across 24 categories. A key finding is that task decomposition quality is the primary bottleneck, with standard LLM decomposition reaching only 34.2% category recall; the proposed Iterative Skill-Aware Decomposition (SAD) method improves decomposition accuracy from 51.0% to 67.7% in a single iteration. The framework also reduces context window consumption by over 99% compared to naive skill-stuffing approaches.
MOSS: Self-Evolving Agents via Source-Level Code Rewriting
MOSS is a system enabling autonomous agents to self-evolve by rewriting their own source code rather than being limited to text-mutable artifacts like prompts or skill files. The system anchors each evolution cycle to production-failure evidence, delegates code modification to an external coding-agent CLI, and verifies candidates by replaying failures in ephemeral trial workers before promoting via consent-gated container swap with rollback. On the OpenClaw benchmark, MOSS improves a four-task mean grader score from 0.25 to 0.61 in a single cycle without human intervention. The authors argue source-level adaptation is strictly more general than text-layer evolution, being Turing-complete and immune to long-context drift.
SKIM: Adaptive soft-token compression for procedural skills in LLM workflows
Researchers introduce SKIM (SKIll coMpression), a multi-resolution soft token compression framework targeting procedural knowledge (skills/workflows) rather than factual documents. SKIM compresses reusable natural language skills to 30–60% of their original token length while preserving task performance, reducing prefill cost and latency when skills are repeatedly invoked. The method adapts compression depth to skill complexity and supports offline compression for frequently updated community skills.