arbor
适合需要大规模试验优化且希望减少人工干预的团队。
可能产生大量实验与计算开销，需注意资源与过拟合风险。
用假设树精炼法自动化多轮实验以改进代码、训练配方或数据流水线。
把代码、训练配方、agent、数据流水线或 prompt 放进一个自动化优化循环，用 Hypothesis Tree Refinement 多轮实验并评估，逐步提升目标指标又尽量不算学上 dev 集。适合想做长期系统化调优、跑大量实验筛选或比 baseline 的场景。特点是能自主试验、选优并减少过拟合风险。
▸ 展开 SKILL.md 英文原文
Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.
帮我安装这个 skill：https://raw.githubusercontent.com/K-Dense-AI/scientific-agent-skills/main/skills/arbor/SKILL.mdcurl -fsSL "https://raw.githubusercontent.com/K-Dense-AI/scientific-agent-skills/main/skills/arbor/SKILL.md"# Arbor — Autonomous Optimization via Hypothesis Tree Refinement ## Overview This skill runs an **Autonomous Optimization (AO)** loop: starting from an existing artifact and a measurable objective, improve it through many rounds of experiment and evaluation — without step-by-step human supervision and without overfitting to the feedback signal. It's the right tool when the bottleneck isn't writing one good change, but *organizing dozens of trials* so that lessons accumulate instead of evaporating. It implements **Hypothesis Tree Refinement (HTR)** from *Arbor* (Jin et al., 2026). The key idea: keep the research state in a persistent **hypothesis tree** rather than in conversation history.