exploratory-autoresearch

exploratory-autoresearch is a Model training & evaluation skill for AI agents, published by gaasher in the Agent-Loop-Skills repository on GitHub. Skillspace rates exploratory-autoresearch D (55/100) 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 explores the space broadly rather than hill-climbing one approach. A temperature scheduler replaces the usual hypothesis step: it forces several wild, diverse swings (full rewrites, different architectures/regimes) early, then enters an adaptive phase that picks swing / merge / exploit per iteration — with a hard stagnation guard that bans further small-step exploits once they run too long, forcing a pivot back to a swing or merge. Tracks an approaches.md registry and a move_type per iteration; analyses every run before the next move. One change per iteration; loops forever until interrupted. Not for the standard analysis-first ml-autoresearch (which lets analysis alone choose each change), one-off training runs, or sweeps.

Grade

D

Quality

55 / 100

Safety

Passed

GitHub stars

132

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