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AI EngineeringPython

Exploratory Autoresearch

by gaasher

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

What Exploratory Autoresearch does

Exploratory Autoresearch runs an autonomous ML research loop that alternates analysis with scheduled swings, merges, and exploits. It is used for open-ended experiments where you want broad exploration first and a stagnation guard to prevent getting stuck in small-step tuning.

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

Frequently asked about Exploratory Autoresearch

  • What else does gaasher publish alongside Exploratory Autoresearch?

    Exploratory 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 Exploratory 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 Exploratory Autoresearch compare to other AI Engineering skills?

    Exploratory Autoresearch ranks #3328 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 Exploratory Autoresearch against them. Open each page to compare what they document and how they install.

More from gaasher/Agent-Loop-Skills

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

See all 25 skills →
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Alpha Evolve

Alpha Evolve evolves a model or program with parallel SEARCH/REPLACE mutations, cascade-evaluated runs, and a MAP-Elites archive across islands. Use it for bounded, diversity-preserving search over code or ML experiments rather than a single refine loop.
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Anomaly Investigation

Anomaly Investigation diagnoses a known data anomaly by forming candidate causes, testing them against the data, and eliminating those the evidence refutes. Use it when you already have a spike, drop, or outlier and need the confirmed root cause and supporting evidence.
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Blue Team

Blue Team patches a target against a concrete set of failing cases, one root-cause class at a time, while checking that previously passing cases still pass. Use it for red-team failure catalogues or CI test failures when you want the fix loop to stop only when regressions are closed.
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Claim Verify Loop

Claim Verify Loop checks each discrete claim in a results draft against the underlying dataset, then stress-tests it for outliers, confounds, and subgroup effects. Use it before publishing when a data-backed draft needs adversarial verification and revision.
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Data Analysis Loop

Data Analysis Loop performs iterative exploratory analysis on a dataset, testing one hypothesis at a time and only keeping findings that reproduce with a meaningful effect size. Use it for open-ended discovery when every claim needs a computed number behind it.
Data
16619
⚔️
1h ago

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.
AI Engineering
16619

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AI-First Engineering

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Agentic OS codifies a persistent multi-agent operating system architecture for Claude Code. Use it when building a multi-agent workflow with file-based memory, slash commands, and scheduled automation that survives session restarts.
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