Aris Infra

OrganizationPopular

OpenLAIR

aris-infra

ARIS (Auto-claude-code-research-in-sleep) infrastructure setup and configuration. Configures MCP servers for cross-model adversarial review, installs Python tools, and validates environment. Run this first before using any other ARIS skills. Use when: setting up ARIS, configuring review servers, "aris setup", "配置ARIS".

Overview

Publisher	OpenLAIR
Repository	`dr-claw`
Skill name	`aris-infra`
Stars	1.2K
Forks	136
Bundled files	18
License	MIT
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
18 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by OpenLAIR on GitHub. Read the source before you install it.

Installation

Install the Aris Infra AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/OpenLAIR/dr-claw/tree/main/skills/aris-infra
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/OpenLAIR/dr-claw.git /tmp/dr-claw
mkdir -p .claude/skills
cp -r /tmp/dr-claw/skills/aris-infra .claude/skills/aris-infra

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Aris Infra in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Aris Infra on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Aris Infra is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

ARIS Infrastructure Setup

Quick Start (One Command)

bash
bash skills/aris-infra/setup.sh

This interactive script will: check prerequisites → install dependencies → register skills → configure MCP reviewer server.

Manual Setup (if you prefer)

Overview

ARIS uses cross-model adversarial review — Claude Code executes research tasks while an external LLM (GPT-5.4, Gemini, or others) provides critical review. This avoids the "self-play blind spot" where a single model reviewing its own work produces predictable feedback.

Prerequisites

Python 3.10+
Claude Code CLI
At least one external LLM API key (OpenAI, Google Gemini, or MiniMax)

Step 1: Register MCP Servers

ARIS provides 5 MCP servers. Register the ones you need:

Core: Codex (GPT-5.4 Reviewer) — Recommended

bash
npm install -g @openai/codex
claude mcp add codex -s user -- codex mcp-server

Configure in ~/.codex/config.toml:

toml
model = "gpt-5.4"

Alternative: Generic LLM Chat (Any OpenAI-compatible API)

bash
claude mcp add llm-chat -s user -- python skills/aris-infra/mcp-servers/llm-chat/server.py

Environment variables:

LLM_API_KEY — API key
LLM_BASE_URL — API base URL (e.g., https://api.openai.com/v1)
LLM_MODEL — Model name (e.g., gpt-4o)
LLM_FALLBACK_MODEL — Fallback model on 504 errors

Alternative: Gemini Review

bash
claude mcp add gemini-review -s user -- python skills/aris-infra/mcp-servers/gemini-review/server.py

Environment variables:

GEMINI_API_KEY or GOOGLE_API_KEY — Google AI API key
GEMINI_REVIEW_MODEL — Model (default: gemini-2.5-pro)

Alternative: Claude Review (Cross-session)

bash
claude mcp add claude-review -s user -- python skills/aris-infra/mcp-servers/claude-review/server.py

Uses the claude CLI binary for reviews in a separate session.

Optional: MiniMax Chat

bash
claude mcp add minimax-chat -s user -- python skills/aris-infra/mcp-servers/minimax-chat/server.py

Environment variables:

MINIMAX_API_KEY — MiniMax API key
MINIMAX_MODEL — Model (default: MiniMax-M2.7)

Optional: Feishu/Lark Notifications

bash
claude mcp add feishu-bridge -s user -- python skills/aris-infra/mcp-servers/feishu-bridge/server.py

Environment variables:

FEISHU_APP_ID, FEISHU_APP_SECRET, FEISHU_USER_ID
BRIDGE_PORT — HTTP server port (default: 9100)

Step 2: Install Python Dependencies

bash
pip install httpx arxiv requests

Step 3: Verify Setup

bash
# Check MCP servers are registered
claude mcp list

# Test a tool call
# If using Codex: mcp__codex__codex should be available
# If using llm-chat: mcp__llm-chat__chat should be available

Available Workflows

After setup, use these one-click workflow skills:

Skill	Command	Description
`aris-idea-discovery`	`/aris-idea-discovery`	Full idea pipeline: literature → ideas → novelty → review → refine
`aris-experiment-bridge`	`/aris-experiment-bridge`	Implement experiments, deploy to GPU, collect results
`aris-auto-review-loop`	`/aris-auto-review-loop`	Multi-round cross-model adversarial review
`aris-paper-writing`	`/aris-paper-writing`	Plan → figures → write LaTeX → compile → improve
`aris-rebuttal`	`/aris-rebuttal`	Parse reviews → strategy → draft → stress test
`aris-research-pipeline`	`/aris-research-pipeline`	End-to-end: idea → experiments → review → paper

Bundled Resources

MCP Servers (`mcp-servers/`)

llm-chat/server.py — Generic OpenAI-compatible bridge
gemini-review/server.py — Gemini review with async jobs
claude-review/server.py — Claude Code CLI review bridge
minimax-chat/server.py — MiniMax-specific bridge
feishu-bridge/server.py — Feishu/Lark notification bridge

Python Tools (`tools/`)

arxiv_fetch.py — arXiv search and PDF download
semantic_scholar_fetch.py — Semantic Scholar search with filters
research_wiki.py — Persistent research knowledge base
watchdog.py — GPU training/download monitoring daemon

Templates (`templates/`)

RESEARCH_BRIEF_TEMPLATE.md — Research direction input
RESEARCH_CONTRACT_TEMPLATE.md — Active idea working document
EXPERIMENT_PLAN_TEMPLATE.md — Claim-driven experiment roadmap
EXPERIMENT_LOG_TEMPLATE.md — Structured experiment results
NARRATIVE_REPORT_TEMPLATE.md — Paper writing input
PAPER_PLAN_TEMPLATE.md — Claims-evidence matrix
IDEA_CANDIDATES_TEMPLATE.md — Compact top ideas
FINDINGS_TEMPLATE.md — Cross-stage discovery log

Troubleshooting

MCP server not found: Ensure claude mcp add was run with -s user flag
API key errors: Set environment variables in your shell profile (~/.zshrc or ~/.bashrc)
Python import errors: Run pip install httpx arxiv requests
Codex not installed: Run npm install -g @openai/codex

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Aris Infra AI skill do?

Why use Aris Infra on TypingMind?

Because you install it once and use it with any model. Aris Infra is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Aris Infra in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/OpenLAIR/dr-claw/tree/main/skills/aris-infra. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Aris Infra?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Aris Infra?

As many as you like. As long as a model supports skills, you can use Aris Infra with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Aris Infra AI skill free?

Yes. It is published on GitHub by OpenLAIR under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

ARIS Infrastructure Setup

Quick Start (One Command)

Manual Setup (if you prefer)

Overview

Prerequisites

Step 1: Register MCP Servers

Core: Codex (GPT-5.4 Reviewer) — Recommended

Alternative: Generic LLM Chat (Any OpenAI-compatible API)

Alternative: Gemini Review

Alternative: Claude Review (Cross-session)

Optional: MiniMax Chat

Optional: Feishu/Lark Notifications

Step 2: Install Python Dependencies

Step 3: Verify Setup

Available Workflows

Bundled Resources

MCP Servers (mcp-servers/)

Python Tools (tools/)

Templates (templates/)

Troubleshooting

Bundled files

Frequently asked questions

Related AI Skills

Set up your own AI workspace now

MCP Servers (`mcp-servers/`)

Python Tools (`tools/`)

Templates (`templates/`)