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AI EngineeringPython

Tournament Autoresearch

by gaasher

Tournament Autoresearch is an AI Engineering skill for Claude Code, published by gaasher in Agent-Loop-Skills.

166 stars19 forkson gaasher/Agent-Loop-SkillsAdded 2026/09/08+2% in starsRepository updated 2026/06/30
View source on GitHubDownload repo ZIP
agent-skillsagentic-loopsagentic-workflowsai-agentsanthropicautoresearchclaudeclaude-codedata-analysisliterature-reviewllm-agentsmachine-learningml-autoresearchopen-sourceprompt-engineeringred-teamingscientific-writingskillssubagents
Install in seconds
Install Tournament Autoresearch
Copy Tournament Autoresearch into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.
terminal
npx degit https://github.com/gaasher/Agent-Loop-Skills/tree/main/loops/tournament-autoresearch ~/.claude/skills/tournament-autoresearch

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/gaasher/Agent-Loop-Skills.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file
loops/tournament-autoresearch/SKILL.md in gaasher/Agent-Loop-Skills
Installs to
~/.claude/skills/tournament-autoresearch
Collection
One of 25 skills cataloged from this repository
Category
AI Engineering — 3670 skills

What Tournament Autoresearch does

Tournament Autoresearch runs a multi-agent ML research loop where several ideas compete, a judge ranks them against a rubric, and the winner is the only change tried. It is used for open-ended experimentation when you want to pressure-test hypotheses before spending compute.

Tournament Autoresearch is cataloged under AI Engineering on DirSkills. Tournament Autoresearch comes from a repository tagged agent-skills, agentic-loops, agentic-workflows, ai-agents and anthropic.

Documentation

README

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

This is the opening of the README. Read the full README on GitHub.

Frequently asked about Tournament Autoresearch

  • What else does gaasher publish alongside Tournament Autoresearch?

    Tournament Autoresearch is one of 25 skills that DirSkills catalogs from gaasher/Agent-Loop-Skills, the repository it ships in. Its siblings there include Alpha Evolve, Anomaly Investigation and Blue Team. Each one is a separate skill with its own page in this directory, installs the same way Tournament Autoresearch does, and is maintained by gaasher in that same repository. The rest of the collection is listed on the gaasher/Agent-Loop-Skills page.

  • How does Tournament Autoresearch compare to other AI Engineering skills?

    Tournament Autoresearch ranks #3354 by stars among the 3670 AI Engineering skills in this catalog. The most-starred ones next to it are Architecture Decision Records, AI-First Engineering and Agentic OS. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of Tournament Autoresearch against them. Open each page to compare what they document and how they install.

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Tournament Autoresearch is one of 25 skills cataloged on DirSkills from gaasher/Agent-Loop-Skills.

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Dueling Autoresearch

Dueling Autoresearch runs two different approaches against the same metric in parallel and keeps a shared scoreboard. Use it when you want an analysis-first head-to-head between lanes such as classical versus learned methods.
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