autoresearch

Controlled keep or reject experiments require a baseline score for your starting artifact, a single editable surface per iteration to isolate cause and effect, and committed metrics with explicit threshold, plateau, or max-iteration stop rules.

Baseline-first scoring in experiment design is the process of evaluating and recording the performance of your original artifact before proposing any variations. This locked baseline provides the control metric needed to measure whether a single iterative change should be kept or rejected.

To isolate cause and effect during ablation testing, restrict modifications to one declared editable surface per iteration loop. This single-change discipline ensures score variations are directly attributable to that specific modification.

Yes, you can use autoresearch for benchmarking agentic SEO skills, prompts, and content structures. It applies a controlled iteration loop with committed metrics and quality gates to evaluate and improve a single editable skill file per run.

You should not use a single-change-per-iteration experimentation loop if you need to modify multiple surfaces simultaneously or if you lack a defined editable artifact. The method requires one committed editable surface and locked metrics up front to function correctly.