tournament-autoresearch

tournament-autoresearch is a Model training & evaluation skill for AI agents, published by gaasher in the Agent-Loop-Skills repository on GitHub. Skillspace rates tournament-autoresearch D (66/100) and it passed the safety scan; the rating is based on static analysis only.

Use when the user wants an autonomous ML research loop that pressure-tests competing ideas before spending compute — several research subagents each propose one architecture change, a self-calibrating Judge critiques them against a rubric, the proposers refine, and the Judge picks the single change to run. The Judge learns to pick better over time by scoring its own predictions against realized metric deltas, recording predicted-vs-realized in a calibration ledger and refining its working rubric. The result is an experiment ledger where each iteration's change won a de-biased tournament. Not for running a single pre-decided experiment, and not for analysis-only exploration — for one hypothesis proposed and run per iteration without competition, use the sibling ml-autoresearch loop.

Grade

D

Quality

66 / 100

Safety

Passed

GitHub stars

132

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

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