Token导航 LogoToken导航TokenDH.com
研究检索敏感数据clawhub未标认证来源可访问clear审计提醒

neuriconeurico 分析

Agent Skill

neurico 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

6,674

周安装

270

GitHub Stars

公开资料未说明

下载量

2,095
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:neurico(neurico 分析)
来源仓库:https://github.com/laoliu5280/neurico
安装命令:
openclaw skills install neurico
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install neurico

简介

neurico 是自主研究框架,协调多个 AI Agent 执行科学实验全流程。

  • 适用于 OpenClaw 中开展设计、执行、分析与记录型科研任务。
  • 支持多模型协作,自动分配子任务并汇总结果。neurico 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 使用前需配置各 Agent 的凭证与权限,避免安全风险。
  • 建议查看 SOUL.md 模板了解实验设计与复盘机制。

SKILL.md

name
neurico
version
0.2.0
description
>
tags

NeuriCo

Autonomous AI research framework. Idea in, paper out.

Quick Reference

What it doesTakes a research idea (YAML) and autonomously runs the full research lifecycle: literature review, experiment design, code execution, analysis, paper writing, GitHub push
InputYAML file with 3 required fields: title, domain, hypothesis
OutputCode (src/), results & plots (results/), LaTeX paper (paper_draft/), GitHub repo
ProvidersClaude Code, Codex, Gemini (OAuth login, not API keys)
Installgit clone https://github.com/ChicagoHAI/neurico && cd neurico && ./neurico setup
Sourcegithub.com/ChicagoHAI/neurico — Chicago Human+AI Lab (ChicagoHAI), University of Chicago
LicenseApache 2.0

Requirements

Minimal (one of)

OptionWhat you need
Docker (recommended)git + docker
Nativegit + python>=3.10 + uv

Resource

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

Recommended

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

Optional API Keys

KeyPurpose
OPENAI_API_KEYLLM-based repo naming, IdeaHub fetching, paper-finder
S2_API_KEYSemantic Scholar literature search via paper-finder
OPENROUTER_KEYMulti-model access during experiments
COHERE_API_KEYImproves paper-finder ranking (~7% boost)
HF_TOKENHugging Face private models/datasets
WANDB_API_KEYWeights & 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

OptionDescription
`--provider claude\gemini\codex`AI provider (default: claude)
--no-githubRun locally without GitHub integration
--write-paperGenerate LaTeX paper after experiments (default: on)
`--paper-style neurips\icml\acl\ams`Paper format (default: neurips)
--privateCreate 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

DomainExamples
Artificial IntelligenceLLM evaluation, prompt engineering, AI agents
Machine LearningTraining, evaluation, hyperparameter tuning
Data ScienceEDA, statistical analysis, visualization
NLPLanguage model experiments, text analysis
Computer VisionImage processing, object detection
Reinforcement LearningAgent training, policy evaluation
SystemsPerformance benchmarking, optimization
TheoryAlgorithmic analysis, proof verification
Scientific ComputingSimulations, 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.

适合场景

01

调用多模型

02

代码和文本生成

03

Agent 推理流程

04

OpenRouter 模型接入

能力概览

能力 1

统一调用多种 LLM

能力 2

支持 Claude、Gemini、Kimi 等模型

能力 3

适合聊天、代码和推理任务

能力 4

可作为 Agent 模型调用入口

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

81.34%
按下载量换算1,704

安全审计

VirusTotal

可疑

ClawScan

通过

Static analysis

通过

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

继续浏览同类 Skills