Coordinate autonomous optimization of an artifact against a measurable objective.
npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill arbor-jasrajtulsi
Please help me install this Agent Skill. Skill: arbor Source: https://github.com/jasrajtulsi/GRAD-SCOPE/tree/main/.claude/skills/arbor Command: npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill arbor-jasrajtulsi
Arbor helps you improve a real artifact through many experiment-and-evaluate cycles without losing track of what was tried, what failed, and what actually transferred to held-out evaluation.
Use the arbor skill to define your objective, separate dev and test evaluators, and iteratively test isolated hypotheses until the best artifact is confirmed.
High-intent search queries and answers about installing and using this skill.
Use a hypothesis tree to organize competing optimization ideas so each experiment has a clear claim. This structure tracks what was tried, what failed, and what transferred to held-out evaluation during long-horizon artifact tuning.
Dev/test evaluator separation uses dev scores to guide the hypothesis search and test scores to admit only transferable improvements. This held-out merge gating ensures optimizations generalize beyond the dev set.
Test changes in isolated executor worktrees so experiments do not clobber one another. This isolation protects the main artifact until a hypothesis passes held-out merge gating and is admitted.
Yes, isolated executor worktrees support pipeline refinement and harness improvement by testing changes independently. This prevents experiments from clobbering one another during long-horizon code tuning.
Evidence backpropagation feeds evaluation results back into the hypothesis tree to guide future search directions. This ensures the optimization process learns from both successful and failed experiments to refine the artifact efficiently.
Use a hypothesis tree for model training when you need long-horizon optimization with clear claims for each experiment. It is essential when tracking competing ideas, isolating changes, and ensuring improvements transfer to held-out evaluation.
dralkhCoordinate iterative artifact improvement against measurable objectives with hypothesis trees and held-out gating.
bugrabilgeDesign benchmark test suites for evaluating LLM agent reliability.
microsoftHarvest transcripts, mine patterns, and test SKILL.md edits against a held-out gate.
MesferAliVisualize machine learning training metrics and model graphs with TensorBoard.
