Autoresearch

Autonomous optimization is only useful when it behaves like disciplined research, not benchmark gambling.

This skill runs a strict experiment loop:

• clarify the target before changing code
• write down hypotheses before testing them
• measure with repeated trials, not one-off wins
• record every experiment, including failures
• generate local reports and graphs that are not checked in

Core Principles

1. No silent scope inference

If the request is vague, stop and ask a short up-front Q&A before starting. Do not silently invent the target workload, correctness bar, or tradeoffs.

2. One experiment = one hypothesis