Autoresearch
/SKILLCoordonne des projets de recherche en IA autonomes de bout en bout à l'aide d'une architecture à deux boucles. La boucle interne effectue des itérations expérimentales rapides avec des objectifs
name: autoresearch
description: 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.
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [Autonomous Research, Two-Loop Architecture, Experiment Orchestration, Research Synthesis, Project Management]
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:
{project}/
├── research-state.yaml # Central state tracking
├── research-log.md # Decision timeline
├── findings.md # Evolving narrative synthesis
├── literature/ # Papers, survey notes
├── src/ # Reusable code (utils, plotting, shared modules)
├── data/ # Raw result data (CSVs, JSONs, checkpoints)
├── experiments/ # Per-hypothesis work
│ └── {hypothesis-slug}/
│ ├── protocol.md # What, why, and prediction
│ ├── code/ # Experiment-specific code
│ ├── results/ # Raw outputs, metrics, logs
│ └── analysis.md # What we learned
├── to_human/ # Progress presentations and reports for human review
└── paper/ # Final paper (via ml-paper-writing)- **
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.,trajectory_H1_runs001-010.csv). Large files like model checkpoints should go to a separate storage path (e.g.,/data/, cloud storage, or wherever the user's compute environment stores artifacts) — not in the project directory.
Initialize research-state.yaml, research-log.md, and findings.md from templates/. Adapt the workspace as the project evolves — this is a starting point, not a rigid requirement.
The Two-Loop Architecture
This is the core engine. Everything else supports it.
BOOTSTRAP (once, lightweight)
Scope question → search literature → form initial hypotheses
INNER LOOP (fast, autonomous, repeating)
Pick hypothesis → experiment → measure → record → learn → next
Goal: run constrained experiments with clear measurable outcomes
OUTER LOOP (periodic, reflective)
Review results → find patterns → update findings.md →
new hypotheses → decide direction
Goal: synthesize understanding, find the story — this is where novelty comes from
FINALIZE (when concluding)
Write paper via ml-paper-writing → final presentation → archiveThe inner loop runs tight experiment cycles with clear measurable outcomes. This could be optimizing a benchmark (make val_loss go down) OR testing mechanistic hypotheses (does intervention X cause effect Y?). The outer loop steps back to ask: what do these results mean?
