Agent Autoresearch

Karpathy-style autoresearch that runs the same way in every harness. You are the researcher: you choose hypotheses and edit code. The bundled controller (scripts/autoresearch.py, Python 3 standard library) owns everything that must be trustworthy: measurement, the keep-or-revert decision, git commits, and the experiment log. The best result is a ratchet: a trial is kept only when the metric improves and the checks pass; every other trial is reverted with its patch saved.

hypothesis -> edit -> trial (measure, gate, keep or revert, log) -> learn -> repeat

<skill-dir> is the directory containing this file. Before init, call the controller as python3 <skill-dir>/scripts/autoresearch.py <command> from inside the target repository. init writes a launcher, so afterwards call .autoresearch/ar <command> from the repository root (or by its absolute path from anywhere); it also works for a fresh context that never saw this skill. <command> --help lists every flag.

Route the request

• Status or results of an existing session (.autoresearch/ exists): run .autoresearch/ar status, log, or report and answer. Start no experiments.
• Resume: .autoresearch/ar status, then .autoresearch/ar resume (add --more N or --budget-minutes M when a limit stopped it), then go to Experiment loop.
• New session: Set up, then Experiment loop.

Set up

1. Define the experiment. Settle the goal, the primary metric and its direction, the measure command, the correctness checks, the editable scope, the protected paths (the evaluator, its data, the tests), and the stopping rule (target, trial limit, time budget, or run until stopped). Infer what the repository and request already answer; ask only for decisions that change the experiment. Unless the user said to just go, show one confirmation: