Use when the user wants two approaches raced head-to-head on a single shared metric — e.g. a classical/algorithmic lane vs an ML/learned lane, or any two strategies for the same task. Each lane runs its own analysis-first research loop confined to its lane, the lanes share a scoreboard and may borrow ideas across the boundary without abandoning their identity, and a shared eval keeps the head-to-head honest; loops until interrupted, reporting the current leader. Not for improving a single approach in isolation (use a single-track research loop), and not for picking between two finished artifacts in one shot (that is a one-time comparison).
$npx -y skills add gaasher/agent-loop-skills --skill dueling-autoresearchInstalls into the current project.
Run `npx skills use "https://github.com/gaasher/agent-loop-skills" --skill "gaasher/agent-loop-skills/dueling-autoresearch"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/gaasher/agent-loop-skills" that are relevant to the current task. Run `npx skills add "https://github.com/gaasher/agent-loop-skills"` and select the relevant skills, then follow their instructions.