tournament-autoresearch
Use when the user wants an autonomous ML research loop that pressure-tests competing ideas before spending compute — several research subagents each propose one architecture change, a self-calibrating Judge critiques them against a rubric, the proposers refine, and the Judge picks the single change to run. The Judge learns to pick better over time by scoring its own predictions against realized metric deltas, recording predicted-vs-realized in a calibration ledger and refining its working rubric. The result is an experiment ledger where each iteration's change won a de-biased tournament. Not f
npx skills add gaasher/Agent-Loop-Skills --skill tournament-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.
# Tournament Autoresearch Loop An ML autoresearch loop whose single "form a hypothesis" step is replaced by an **idea tournament**. The artifact is an experiment ledger; the feedback signal is the **realized `<metric>` delta** of the change that won the tournament. Each iteration `<n>` ResearchAgents propose competing architecture changes, a **Judge** critiques and ranks them, the proposers refine, and the Judge selects **one** change to run. The Judge is the orchestrator and **self-calibrates**: it scores its predictions against realized results, so it learns which kinds of ideas actually pay off. The experiment mechanics (snapshot → run → mandatory analysis → keep/revert) match the sibling `ml-autoresearch` loop. ## When to use Use this for open-ended ML experimentation where competing ideas should be vetted before compute is spent and the picker should improve over time. **You are the Judge**: adopt `roles/Judge.md` and spawn the proposers with `roles/ResearchAgent.md`. Default to `<n>` competing proposers with one refine round; widen `<n>` or add rounds when ideas are converging too fast. Not for running a single pre-decided experiment, and not for analysis-only exploration ove
- When to use
- Setup
- The loop
- Ledger
- Constraints
What does the tournament-autoresearch skill do?
Use when the user wants an autonomous ML research loop that pressure-tests competing ideas before spending compute — several research subagents each propose one architecture change, a self-calibrating Judge critiques them against a rubric, the proposers refine, and the Judge picks the single change to run. The Judge learns to pick better over time by scoring its own predictions against realized metric deltas, recording predicted-vs-realized in a calibration ledger and refining its working rubric. The result is an experiment ledger where each iteration's change won a de-biased tournament. Not f
How do I install it?
Run `npx skills add gaasher/Agent-Loop-Skills --skill tournament-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.