Provides guidance for interpreting and manipulating neural network internals using nnsight with optional NDIF remote execution. Use when needing to run interpretability experiments on massive models (70B+) without local GPU resources, or when working with any PyTorch architecture.
autoresearch
autoresearch is an open-source productivity skill for Claude Code, published by Orchestra-Research. Its author describes it as: “Orchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization targets. The outer loop synthesizes results, identifies pa…”. The project has 12k stars on GitHub and is available under the MIT license. Add it to your setup with `/plugin marketplace add Orchestra-Research/AI-Research-SKILLs`.
What autoresearch does
Autonomous research orchestration for AI coding agents. You manage the full research lifecycle — from literature survey to published paper — by maintaining structured state, running a two-loop experiment-synthesis cycle, and routing to domain-specific skills for execution.
Installation
Add autoresearch to your agent with:
/plugin marketplace add Orchestra-Research/AI-Research-SKILLsAlways review a skill's source before installing it. This command comes from the skill's public repository; the linked repo is the source of truth for exact setup steps.
What's inside
The SKILL.md for autoresearch is organised into these sections:
- Getting Started
- Initialize Workspace
- The Two-Loop Architecture
- Research is Non-Linear
- Bootstrap: Literature and Hypotheses
- The Inner Loop
- Route to Domain Skills
- Track the Experiment Trajectory
- The Outer Loop
- Deciding Direction
- findings.md Is Your Project Memory
- Agent Continuity (MANDATORY — Set Up First)
When to use it
Reach for autoresearch when you want productivity help from Claude Code without writing the same instructions every session. Load the skill and the agent picks it up automatically for relevant tasks.
Strengths
- Clear MIT license — safe to read and adapt
- Ships in Orchestra-Research/AI-Research-SKILLs, an established project with 11,807 GitHub stars
- Declares compatibility with Claude Code
- Actively maintained (recent commits)
Topics
Frequently asked questions
- What does autoresearch do?
- Orchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization targets. The outer loop synthesizes results, identifies patterns, and steers research direction. Routes to domain-specific skills for execution, supports continuous agent operation via Claude Code /loop and OpenClaw heartbeat, and produces research presentations and papers. Use when starting a research project, running autonomous experiments, or managing a multi-hypothesis research effort.
- How do I install autoresearch?
- Run /plugin marketplace add Orchestra-Research/AI-Research-SKILLs in your agent, then reload your skills. Review the source at https://github.com/Orchestra-Research/AI-Research-SKILLs before installing.
- Is autoresearch free to use?
- Yes. autoresearch is free and open source under the MIT license, so you can read, run, and adapt it within that license's terms.
- Which agents does autoresearch work with?
- autoresearch declares compatibility with Claude Code. Agent Skills use a portable SKILL.md format, so other compatible clients can usually load it too.
- Where does autoresearch come from?
- autoresearch ships inside Orchestra-Research/AI-Research-SKILLs, a repository that contains 41 catalogued skills in total. The repository's 11,807 GitHub stars apply to that whole collection, not to this skill on its own.
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