arbor

arbor is a Model training & evaluation skill for AI agents, published by K-Dense-AI in the scientific-agent-skills repository on GitHub. Skillspace rates arbor C (74/100) and it passed the safety scan; the rating is based on static analysis only.

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.

Grade

C

Quality

74 / 100

Safety

Passed

GitHub stars

44,723

Unproven: rated by static analysis only, not yet compared head-to-head.

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