/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 the following MCP servers. Register the ones you need:

Core: Codex reviewer (capability-gated) — Recommended

npm install -g @openai/codex
claude mcp add codex -s user -- python3 "$(pwd)/skills/aris-infra/mcp-servers/codex-exec/server.py"

Inspect any existing registration before replacing it. Authenticate Codex separately. Workflow M pins the requested model/effort and read-only sandbox per call; see [reviewer capability gates](shared-references/reviewer-routing.md). Do not assume that registration proves a model is available.

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/`)

• `codex-exec/server.py` — Codex MCP bridge over current `codex exec`
• `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`

Workflow M: opt-in self-evolution

See [setup and safety](../../docs/aris-self-evolution.md) before enabling logging. `/aris-meta-optimiz