---
name: ARIS Infra
slug: aris-infra
category: AI Engineering
description: ARIS Infra sets up the ARIS research environment by registering MCP review servers, installing Python dependencies, and checking prerequisites. Use it first when configuring ARIS or adding review models.
github: "https://github.com/OpenLAIR/dr-claw/tree/main/skills/aris-infra"
language: JavaScript
stars: 1047
forks: 116
install: "npx degit https://github.com/OpenLAIR/dr-claw/tree/main/skills/aris-infra ~/.claude/skills/aris-infra"
installs_to: ~/.claude/skills/aris-infra
source_path: skills/aris-infra/SKILL.md
collection_size: 25
category_size: 2451
collection_url: "https://dirskills.com/collections/OpenLAIR/dr-claw"
added: 2026-08-21T05:14:02.042Z
last_synced: 2026-08-21T05:14:02.042Z
canonical_url: "https://dirskills.com/skills/aris-infra"
---

# ARIS Infra

ARIS Infra sets up the ARIS research environment by registering MCP review servers, installing Python dependencies, and checking prerequisites. Use it first when configuring ARIS or adding review models.

**Install:**

```bash
npx degit https://github.com/OpenLAIR/dr-claw/tree/main/skills/aris-infra ~/.claude/skills/aris-infra
```

## README

# 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`