dueling-autoresearch
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 artifac
npx skills add gaasher/Agent-Loop-Skills --skill dueling-autoresearch --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Dueling Autoresearch Loop Two lanes work the **same** objective in parallel and **race the same metric** — by default a **classical/algorithmic** lane against an **ML/learned** lane (the lanes are user-named). Each lane runs its own analysis-first iteration via `roles/TrackAgent.md`, **confined to its lane**. Every round both lanes post to a shared `duel_log.md` scoreboard and may **borrow ideas** across the lane boundary — but each stays in its lane. The feedback signal is the shared `<metric>` on a shared eval: if the classical lane wins, that is a real result. Lanes support **mixed code locations** — a `codebase` lane edits existing repo files, a `sandbox` lane authors its own code — and an **eval-parity gate** keeps the scores comparable. You are the orchestrator: each round you advance both lanes, update the scoreboard, and keep both honest. Do not pause for permission once the loop is running. ## When to use Use this to race two genuinely different approaches on one metric and keep them honest against the same eval — classical vs learned, two model families, two query strategies. Default to spawning both lanes in parallel and letting the scoreboard drive cross-lane idea bor
- When to use
- Setup
- The loop (duel)
- Ledger
- Constraints
What does the dueling-autoresearch skill do?
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 artifac
How do I install it?
Run `npx skills add gaasher/Agent-Loop-Skills --skill dueling-autoresearch --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From gaasher/Agent-Loop-Skills, a repository with 146 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.