neurico

Research

Autonomous research framework that orchestrates AI agents (Claude Code, Codex, Gemini) to design, execute, analyze, and document scientific experiments. Takes a structured research idea (YAML with title, domain, hypothesis) and produces code, results, plots, LaTeX papers, and GitHub repositories.

QUICK START

How to use this skill

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Source SKILL.md: https://github.com/ChicagoHAI/NeuriCo/blob/HEAD/clawskill/SKILL.md

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NeuriCo

Autonomous AI research framework. Idea in, paper out.

Quick Reference


What it does	Takes a research idea (YAML) and autonomously runs the full research lifecycle: literature review, experiment design, code execution, analysis, paper writing, GitHub push
Input	YAML file with 3 required fields: `title`, `domain`, `hypothesis`
Output	Code (`src/`), results & plots (`results/`), LaTeX paper (`paper_draft/`), GitHub repo
Providers	Claude Code, Codex, Gemini (OAuth login, not API keys)
Install	`git clone https://github.com/ChicagoHAI/neurico && cd neurico && ./neurico setup`
Source	github.com/ChicagoHAI/neurico — Chicago Human+AI Lab (ChicagoHAI), University of Chicago
License	Apache 2.0

Requirements

Minimal (one of)

Option	What you need
Docker (recommended)	`git` + `docker`
Native	`git` + `python>=3.10` + `uv`

Resource

Access to at least one AI coding CLI (OAuth login required):

Claude Code (recommended)
Codex
Gemini CLI

What	Why
GitHub token (classic, `repo` scope)	Auto-creates repos and pushes results. Create here

Optional API Keys

Key	Purpose
`OPENAI_API_KEY`	LLM-based repo naming, IdeaHub fetching, paper-finder
`S2_API_KEY`	Semantic Scholar literature search via paper-finder
`OPENROUTER_KEY`	Multi-model access during experiments
`COHERE_API_KEY`	Improves paper-finder ranking (~7% boost)
`HF_TOKEN`	Hugging Face private models/datasets
`WANDB_API_KEY`	Weights & Biases experiment tracking

Setup Tiers

Basic: CLI login + GITHUB_TOKEN -- full NeuriCo functionality
Enhanced: + OPENAI_API_KEY -- LLM repo naming + IdeaHub support
Full: + S2_API_KEY (+ optional COHERE_API_KEY) -- paper-finder literature search

Installation

Docker (recommended)

The Docker image is a pre-configured environment with Python, Node.js, AI coding CLIs (Claude Code, Codex, Gemini), and a full LaTeX installation for paper compilation -- so you don't have to install any of these yourself. All experiments run inside this container; nothing is installed on your host system beyond the cloned repo. The image is built from the open-source Dockerfile and hosted on GitHub Container Registry.

git clone https://github.com/ChicagoHAI/neurico && cd neurico
./neurico setup     # pulls Docker image, configures API keys, walks through CLI login

Or step by step:

git clone https://github.com/ChicagoHAI/neurico && cd neurico
docker pull ghcr.io/chicagohai/neurico:latest
docker tag ghcr.io/chicagohai/neurico:latest chicagohai/neurico:latest
./neurico config    # configure API keys
claude              # login to AI CLI (one-time, on host)

Native

git clone https://github.com/ChicagoHAI/neurico && cd neurico
curl -LsSf https://astral.sh/uv/install.sh | sh
uv sync
cp .env.example .env   # edit: add your API keys
claude                  # login to AI CLI

Invocation

Fastest: Fetch from IdeaHub and run

./neurico fetch <ideahub_url> --submit --run --provider claude

Browse ideas at IdeaHub, copy the URL, and run the command above. NeuriCo fetches the idea, creates a GitHub repo, runs experiments, writes a paper, and pushes everything.

From a YAML file

./neurico submit path/to/idea.yaml
./neurico run <idea_id> --provider claude

Run options

Option	Description
`--provider claude\|gemini\|codex`	AI provider (default: claude)
`--no-github`	Run locally without GitHub integration
`--write-paper`	Generate LaTeX paper after experiments (default: on)
`--paper-style neurips\|icml\|acl\|ams`	Paper format (default: neurips)
`--private`	Create private GitHub repository

Input Format

Only 3 fields required:

idea:
  title: "Do LLMs understand causality?"
  domain: artificial_intelligence
  hypothesis: "LLMs can distinguish causal from correlational relationships"

Optional fields: background (papers, datasets, code references), methodology (approach, steps, baselines, metrics), constraints (compute, time, memory, budget), expected_outputs, evaluation_criteria.

Full schema: ideas/schema.yaml

Output Format

workspace/<repo-name>/
  src/            # Python experiment code
  results/        # Metrics, plots, models
  paper_draft/    # LaTeX paper (with --write-paper)
  logs/           # Execution logs
  artifacts/      # Models, checkpoints
  .neurico/       # Original idea spec

Results are automatically pushed to the GitHub repo created during submission.

Supported Domains

Domain	Examples
Artificial Intelligence	LLM evaluation, prompt engineering, AI agents
Machine Learning	Training, evaluation, hyperparameter tuning
Data Science	EDA, statistical analysis, visualization
NLP	Language model experiments, text analysis
Computer Vision	Image processing, object detection
Reinforcement Learning	Agent training, policy evaluation
Systems	Performance benchmarking, optimization
Theory	Algorithmic analysis, proof verification
Scientific Computing	Simulations, numerical methods

Configuration

./neurico config      # Interactive API key configuration
./neurico setup       # Full setup wizard
./neurico shell       # Interactive shell inside container
./neurico help        # Show all commands

Environment variables go in .env (copy from .env.example). See README for details.

Security

No secrets are uploaded. API keys and tokens stay local in your .env file and are never committed, pushed, or sent anywhere beyond the APIs they authenticate with. Sensitive environment variables are explicitly filtered out from all subprocess calls and sanitized from logs.
Experiments run inside Docker. The container is isolated from your host system. The only host directories mounted are your config, templates, and workspace output folder.
Open source. The entire codebase, including the Dockerfile and install script, is publicly auditable on GitHub.
Built by ChicagoHAI — the Human+AI Lab at the University of Chicago.