experiment-controller

You can automate reproducible experiment execution by processing IDEA.md and METHOD.md inputs to clone repositories, install dependencies, run training, and incrementally log metrics to a final EXPERIMENT.md. This ensures an end-to-end auditable workflow for research publication.

Automated git-based environment setup works by cloning repositories, applying dependency planners, and configuring the claw-shell execution environment. It verifies integrity and establishes checkpoints to ensure the research workflow is fully reproducible before execution begins.

Yes, you can use this workflow for iterative ablation studies. It supports executing multiple experiments and ablations while logging configurations, metrics, and observations incrementally to EXPERIMENT.md, maintaining per-run metadata for reproducibility.

The best way to create a peer-review-ready audit trail is to use a workflow that logs per-run metadata, checkpoints, and integrity verification across all experiments. This accumulates results into a single EXPERIMENT.md for publication-ready reporting.

Yes, the experiment automation workflow requires literature and GitHub search to identify suitable experiment schemes and open-source implementations. It confirms the scheme with the user before proceeding to repository setup and execution.

Using markdown files for experiment scheme confirmation requires finalized IDEA.md and METHOD.md documents before execution. The workflow depends on these static inputs to drive automated environment setup, meaning incomplete markdown specifications will halt the research workflow.