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0-autoresearch

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Orchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization targets. The outer loop synthesizes results, identifies patterns, and steers research direction. Routes to domain-specific skills for execution, supports continuous agent operation via Claude Code /loop and OpenClaw heartbeat, and produces research presentations and papers. Use when starting a research project, running autonomous experiments, or managing a multi-hypothesis research effort.

When to use

• You have a research question explorable through experiments
• There's a measurable proxy metric for inner loop optimization
• The real contribution requires synthesis beyond the metric
• You want continuous autonomous research operation
• You have a specific one-off task (train a model, run eval, write a paper)
• No iterative experimentation needed

When not to use

• domain-specific execution tasks

Skill page

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