Auto-claude-code-research-in-sleep

# Auto-claude-code-research-in-sleep (ARIS ⚔️🌙)

💡 *Use ARIS in Claude Code / Cursor / Trae as a skill-based workflow, or get the full experience with the standalone CLI — enjoy any way you like!*

🤖 **AI agents:** Read [`AGENT_GUIDE.md`](AGENT_GUIDE.md) instead — structured for LLM consumption, not human browsing.

🔥 [**ARIS-Code CLI — 独立安装版**](docs/ARIS-Code-README_CN.md) · [English](docs/ARIS-Code-README_EN.md) | [⬇️ Download](https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/releases/latest)

> 📰 **ARIS-Code v0.4.4** (2026-04-20) — **Setup UX + reviewer routing fixes** (resolves [#158](https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/issues/158), [#162](https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/issues/162)) | `/setup` no longer forces Bearer for Anthropic + custom URL (fixes ModelScope / `code.newcli.com` etc.) | Provider-aware proxy URL hints | Stale state no longer leaks across provider switches | LlmReview smart fallback
>
> <details><summary>Previous versions</summary>
>
> **v0.4.3** (2026-04-17) — **Third-party Anthropic-compat proxy support** (Bedrock etc.) | Skip beta flags that proxies reject | Propagate custom base URL for `anthropic` provider | Credit [@screw-44](https://github.com/screw-44)
>
> **v0.4.2** (2026-04-17) — **Auto-compaction corruption fix** | Compaction summary preserved on OpenAI-compat executors | Shell-provided API keys no longer erased on launch
>
> **v0.4.1** (2026-04-15) — **Plan mode** (`/plan`) | Cooperative Ctrl+C interrupt | Auto-retry (429/5xx/network) | **Research Wiki** 📚 (persistent knowledge base) | **Self-Evolution** 🧬 (`/meta-optimize`) | Local models (LM Studio/Ollama) | 62 skills synced
>
> **v0.3.11** (2026-04-13) — Reviewer Anthropic-compatible mode (Claude via proxy)
>
> **v0.3.9** (2026-04-11) — Proxy/custom base URL (CCSwitch) | Local models (LM Studio/Ollama) | Windows (experimental)
>
> **v0.3.5** (2026-04-08) — **Research Wiki** (persistent papers/ideas/experiments/claims + relationship graph) | **Meta-Optimize** self-evolution (analyze logs → propose SKILL.md patches)
>
> **v0.3.0** (2026-04-03) — Multi-file memory index | Rich task system (TodoWrite) | `/plan` | Security hardening
>
> **v0.2.2** (2026-04-03) — `/plan` step-by-step planning | `/tasks` persistent tracking
>
> **v0.2.1** (2026-04-03) — Persistent Memory | Kimi K2.5 multi-turn fix | CJK cursor fix
>
> **v0.2.0** (2026-04-02) — Open source | Kimi + MiniMax + GLM support | Smart LlmReview routing | CI/CD
>
> **v0.1.0** (2026-04-02) — Initial release | Multi-executor & reviewer | 42 bundled skills
>
> </details>

<img src="docs/aris-code-banner.png" width="600" alt="ARIS-Code CLI">

![ARIS Logo](docs/aris_logo.svg)

![Hero](docs/hero_combined.svg)

[中文版 README](README_CN.md) | English

![Score Progression](docs/auto_review_score_curve.png)

> 🌙 **Let Claude Code do research while you sleep.** Wake up to find your paper scored, weaknesses identified, experiments run, and narrative rewritten — autonomously.
>
> 🪶 **Radically lightweight — zero dependencies, zero lock-in.** The entire system is plain Markdown files. No framework to learn, no database to maintain, no Docker to configure, no daemon to babysit. Every skill is a single `SKILL.md` readable by any LLM — swap Claude Code for [Codex CLI](skills/skills-codex/), [OpenClaw](docs/OPENCLAW_ADAPTATION.md), [Cursor](docs/CURSOR_ADAPTATION.md), [Trae](docs/TRAE_ARIS_RUNBOOK_EN.md), [Antigravity](docs/ANTIGRAVITY_ADAPTATION.md), Windsurf, or your own agent and the workflows still work. Fork it, rewrite it, adapt it to your stack.
>
> *💡 ARIS is a methodology, not a platform. What matters is the research workflow — take it wherever you go. 🌱*

[![Featured on PaperWeekly](https://img.shields.io/badge/Featured%20on-PaperWeekly-red?style=flat)](https://mp.weixin.qq.com/s/tDniVryVGjDkkkWl-5sTkQ) · [![PaperWeekly — MiniMax-M2.7](https://img.shields.io/badge/PaperWeekly-MiniMax--M2.7-red?style=flat)](https://mp.weixin.qq.com/s/KLFU74lAL2FAIc9K6i1Kqg) · [![Featured in awesome-agent-skills](https://img.shields.io/badge/Featured%20in-awesome--agent--skills-blue?style=flat&logo=github)](https://github.com/VoltAgent/awesome-agent-skills) · [![AI Digital Crew - Project of the Day](https://img.shields.io/badge/AI%20Digital%20Crew-Project%20of%20the%20Day%20(2026.03.14)-orange?style=flat)](https://aidigitalcrew.com) · [💬 Join Community](#-community) · [![Cite](https://img.shields.io/badge/📖_Cite_Us-BibTeX-green?style=flat)](#-citation)

Custom [Claude Code](https://docs.anthropic.com/en/docs/claude-code) skills for autonomous ML research workflows. These skills orchestrate **cross-model collaboration** — Claude Code drives the research while an external LLM (via [Codex MCP](https://github.com/openai/codex)) acts as a critical reviewer. 🔀 **Also supports [alternative model combinations](#-alternative-model-combinations) (Kimi, LongCat, DeepSeek, etc.) — no Claude or OpenAI API required.** For example, [MiniMax-M2.7 + GLM-5 or GLM-5 + MiniMax-M2.7](docs/MiniMax-GLM-Configuration.md). 🤖 **[Codex CLI native](skills/skills-codex/)** — full skill set also available for OpenAI Codex. 🖱️ **[Cursor](docs/CURSOR_ADAPTATION.md)** — works in Cursor too. 🖥️ **[Trae](docs/TRAE_ARIS_RUNBOOK_EN.md)** — ByteDance AI IDE. 🚀 **[Antigravity](docs/ANTIGRAVITY_ADAPTATION.md)** — Google's agent-first IDE. 🆓 **[Free tier via ModelScope](docs/MODELSCOPE_GUIDE.md) — zero cost, zero lock-in.**

> 💭 **Why not self-play with a single model?** Using Claude Code subagents or agent teams for both execution and review is technically possible, but tends to fall into **local minima** — the same model reviewing its own patterns creates blind spots.
>
> *Think of it like adversarial vs. stochastic bandits: a single model self-reviewing is the stochastic case (predictable reward noise), while cross-model review is adversarial (the reviewer actively probes weaknesses the executor didn't anticipate) — and adversarial bandits are fundamentally harder to game.*
>
> 💭 **Why two models, not more?** Two is the minimum needed to break self-play blind spots, and 2-player games converge to Nash equilibrium far more efficiently than n-player ones. Adding more reviewers increases API cost and coordination overhead with diminishing returns — the biggest gain is going from 1→2, not 2→4.
>
> Claude Code's strength is fast, fluid execution; Codex (GPT-5.4 xhigh) is slower but more deliberate and rigorous in critique. These complementary styles — **speed × rigor** — produce better outcomes than either model talking to itself.
>
> 🧿 **Want the strongest possible reviewer?** Add `— reviewer: oracle-pro` to any skill to route reviews through **GPT-5.4 Pro** via [Oracle MCP](https://github.com/steipete/oracle). Pro-level reasoning for proof verification, experiment auditing, and final stress tests. Works with API key or free browser mode. [Setup →](#-optional-gpt-54-pro-via-oracle)

## 🎯 More Than Just a Prompt

> These are full pipelines — you can also use each workflow independently. Already have an idea? Skip to Workflow 1.5. Have results? Jump to Workflow 3. Got reviews? Jump to Workflow 4. Want persistent memory? Enable [Research Wiki](#-research-wiki--persistent-research-memory). See [Quick Start](#-quick-start) for all commands and [Workflows](#-workflows) for the full breakdown.

**Basic mode** — give ARIS a research direction, it handles everything:

```
/research-pipeline "factorized gap in discrete diffusion LMs"
```

**🔥 Targeted mode** — got a paper you want to improve? Give ARIS the paper + the code:

```
/research-pipeline "improve method X" — ref paper: https://arxiv.org/abs/2406.04329, base repo: https://github.com/org/project
```

ARIS reads the paper → finds its weaknesses → clones the codebase → generates ideas that specifically fix *those* weaknesses with *that* code → runs experiments → writes your paper. Like telling a research assistant: *"read this paper, use this repo, find what's missing, and fix it."*

> Mix and match: `ref paper` only = "what can be improved?", `base repo` only = "what can I build with this code?", both = "improve *this* paper using *this* code."

**🔥 Rebuttal mode** — reviews just dropped? Don't panic. ARIS reads every concern, builds a strategy, and drafts a rebuttal that's grounded, structured, and under the character limit:

```
/rebuttal "paper/ + reviews" — venue: ICML, character limit: 5000
```

| Parameter | Default | What it does |
|-----------|---------|-------------|
| `venue` | `ICML` | Target venue (ICML/NeurIPS/ICLR/CVPR/ACL/AAAI/ACM) |
| `character limit` | — | **Required.** Hard character limit for rebuttal text |
| `quick mode` | `false` | Stop after parsing + strategy (Phase 0-3). See what reviewers want before drafting |
| `auto experiment` | `false` | Auto-run supplementary experiments via `/experiment-bridge` when reviewers ask for new evidence |
| `max stress test rounds` | `1` | How many times GPT-5.4 xhigh stress-tests the draft |
| `max followup rounds` | `3` | Per-reviewer follow-up round limit |

Three safety gates — rebuttal will NOT finalize if any fails:
- 🔒 **No fabrication** — every claim maps to paper/review/user-confirmed result
- 🔒 **No overpromise** — every promise is user-approved
- 🔒 **Full coverage** — every reviewer concern is tracked

Two outputs: `PASTE_READY.txt` (exact char count, paste to venue) + `REBUTTAL_DRAFT_rich.md` (extended version for manual editing).

**After acceptance** — your paper is in, now prepare the presentation:

```
/paper-slides "paper/"     # → Beamer PDF + PPTX + speaker notes + Q&A prep
/paper-poster "paper/"     # → A0/A1 poster PDF + editable PPTX + SVG
```

> *💡 From idea to paper to podium — one toolchain. 🌱*

## 🏆 Papers Built with ARIS

| Paper | Score | Venue | Author | Stack |
|-------|:-----:|-------|--------|-------|
| CS Paper | **8/10** "clear accept" | CS Conference | [@DefanXue](https://github.com/DefanXue)