ML Autoresearch Loop

ML Autoresearch Loop 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

agent-skillsagentic-loopsagentic-workflowsai-agentsanthropicautoresearchclaudeclaude-codedata-analysisliterature-reviewllm-agentsmachine-learningml-autoresearchopen-sourceprompt-engineeringred-teamingscientific-writingskillssubagents

Install in seconds

Install ML Autoresearch Loop

Copy ML Autoresearch Loop 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/ml-autoresearch ~/.claude/skills/ml-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/ml-autoresearch/SKILL.md in gaasher/Agent-Loop-Skills
Installs to: ~/.claude/skills/ml-autoresearch
Collection: One of 25 skills cataloged from this repository
Category: AI Engineering — 3670 skills

What ML Autoresearch Loop does

ML Autoresearch Loop runs an autonomous machine learning research cycle where each experiment is analyzed before the next change is made. Use it for open-ended model improvement with one change per run, optionally grounded in literature.

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

Documentation

README

Excerpt

ML Autoresearch Loop

This loop is analysis-first: every experiment is followed by a diagnostic pass that examines what happened inside the model, and the next change is a hypothesis grounded in that evidence — not a guess. The feedback signal is <metric> read from the run log; the analysis is the spine that decides what to change. A <literature> dial (on/off) optionally grounds each change in prior work via the sibling literature-search skill. One change per iteration, so each metric move is attributable.

You are the researcher. Do not pause to ask for permission once the loop is running.

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

Frequently asked about ML Autoresearch Loop

What else does gaasher publish alongside ML Autoresearch Loop?
ML Autoresearch Loop 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 ML Autoresearch Loop 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 ML Autoresearch Loop compare to other AI Engineering skills?
ML Autoresearch Loop ranks #3332 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 ML Autoresearch Loop against them. Open each page to compare what they document and how they install.