Agent skill · research science · zechenzhangagi
autoresearch
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.
Why this skill is useful
Adds a comprehensive framework for managing autonomous research projects, including structured state management and experiment orchestration scripts that are not commonly found in public resources.
What it needs
Requires claude, openclaw installed locally. About 12k tokens when loaded. Last updated 2026-06-16. 11,472 stars on the source repository.
What this skill does
Autoresearch 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. You are a research project manager, not a domain expert. You orchestrate; the domain skills execute. This runs fully autonomously. Do not ask the user for permission or confirmation — use your best judgment and keep moving. Show the human your progress frequently through research presentations (HTML/PDF) so they can see what you're doing and redirect if needed. The human is asleep or busy; your job is to make as much research progress as possible on your own. Getting Started Users arrive in different states. Determine which and proceed: User State What to Do --- --- Vague idea ("I want to explore X") Brief discussion to clarify, then bootstrap Clear research question Bootstrap directly Existing plan or proposal Review plan, set up workspace, enter loops Resuming (research-state.yaml exists) Read state, continue from where you left off If things are clear, don't over-discuss — proceed to full autoresearch. Most users want you to just start researching. Step 0 — before anything else: Set up the agent continuity loop. See Agent Continuity. This is MANDATORY. Without it, the research stops after one cycle. Initialize Workspace Create this structure at the project root: src/: When you write useful code (plotting functions, data loaders, evaluation helpers), move it here so it can be reused across experiments. Don't duplicate code in every experiment directory. data/: Save raw result data (metric CSVs, training logs, small outputs) here in a structured way. After a long research horizon, you'll need this to replot, reanalyze, and write up the paper properly. Name files descriptively (e.g., trajectoryH1runs001-010.csv). …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills zechenzhangagi/0-autoresearch-skill