aris-infra

name	aris-infra
description	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".
license	MIT
metadata	{"author":"wanshuiyin/ARIS","version":"1.0.0","repository":"https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep"}
allowed-tools	Bash, Read, Write, Edit, Glob, Grep

ARIS Infrastructure Setup

Quick Start (One Command)

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

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

Configure in ~/.codex/config.toml:

model = "gpt-5.4"

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

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

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)

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

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

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

pip install httpx arxiv requests

Step 3: Verify Setup

# 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