OpenClaw-Skill: Collective Skill Tree Search for Agentic Large Language Models

Equipping Large Language Model (LLM) agents with effective skills is crucial for solving complex tasks in real-world systems such as OpenClaw. In this work, we aim to develop a framework that automatically constructs reusable skills to enhance LLM agents in tool use, multi-step reasoning, and dynamic environment interaction. To this end, we propose Collective Skill Tree Search (CSTS), a novel tree-search-based skill construction framework that constructs structured, diverse, and generalizable trees of skills. The core idea of CSTS is to leverage collective intelligence to jointly search, identify, and compose effective skills through two iterative phases: Collective Skill Node Generation (CSN-Gen) and Collective Skill Node Assessment (CSN-Assess). CSN-Gen exploits collective knowledge from multiple models to explore diverse candidate skills for each subtask, enabling comprehensive skill discovery. CSN-Assess employs multiple models as judges to evaluate and select skill nodes using two scoring mechanisms: (1) collective quality scoring, which aggregates independent evaluations to obtain a robust estimate of skill effectiveness, and (2) collective transferability scoring, which explicitly verifies whether a skill generalizes across different models. With CSTS, we construct comprehensive trees of skills together with skill-augmented training data, enabling models to effectively learn and utilize procedural knowledge. Furthermore, we introduce Collective Skill Reinforcement Learning (CSRL), which actively selects multiple relevant skills from the tree to broaden solution-space exploration and avoid being trapped by a single skill or homogeneous suboptimal solutions. As a result, our trained model, OpenClaw-Skill, exhibits strong agentic capabilities in long-horizon planning, tool use, and generalization on challenging benchmarks.