Use ARIS (Auto-Research-In-Sleep) for autonomous ML research — idea generation, paper review, experiment automation, and cross-model collaboration with Claude Code, Codex, or any LLM agent.
Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.
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Use ARIS (Auto-Research-In-Sleep) for autonomous ML research — idea generation, paper review, experiment automation, and cross-model collaboration with Claude Code, Codex, or any LLM agent.
triggers
["set up ARIS for autonomous research","use ARIS to generate research ideas","run autonomous ML experiments with ARIS","review a paper using ARIS cross-model workflow","configure ARIS research pipeline","help me with ARIS research automation","write a rebuttal using ARIS","start an ARIS research workflow"]
ARIS (Auto-Research-In-Sleep) is a lightweight Markdown-based system for autonomous ML research that orchestrates cross-model collaboration. It enables AI agents to discover ideas, review papers, run experiments, and write rebuttals — all autonomously. Works with Claude Code, Codex CLI, Cursor, Trae, Antigravity, or any LLM agent.
What ARIS Does
Idea Generation: Automatically discovers research ideas from arXiv papers, GitHub repos, or research directions
Cross-Model Review: Uses different LLMs for execution vs. review to break self-play blind spots (e.g., Claude Code executes, GPT-5.4 reviews)
Paper Writing: Generates drafts with auto-review loops to improve quality
Rebuttal Generation: Parses reviews, builds strategy, drafts rebuttals under character limits
Research Wiki: Persistent knowledge base tracking papers, ideas, experiments, and claims
Installation
As Claude Code Skills (In-Editor)
# Clone skills into your Claude Code skills directorycd ~/claude-code-skills # or your skills path
git clone https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep.git aris
cd aris/skills
# Skills are now available in Claude Code
As Standalone CLI (ARIS-Code)
# Download latest release# Visit: https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/releases/latest# Or install from source
git clone https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep.git
cd Auto-claude-code-research-in-sleep/aris-code
cargo build --release
# Run setup
./target/release/aris-code
# Follow interactive setup to configure API keys
Environment Setup
ARIS requires API keys for LLM providers:
# For Claude (primary executor)export ANTHROPIC_API_KEY=your_key_here
# For OpenAI (reviewer/alternative)export OPENAI_API_KEY=your_key_here
# For alternative Chinese models (optional)export MOONSHOT_API_KEY=your_key_here # Kimiexport MINIMAX_API_KEY=your_key_here # MiniMaxexport GLM_API_KEY=your_key_here # GLMexport DEEPSEEK_API_KEY=your_key_here # DeepSeekexport DOUBAO_API_KEY=your_key_here # Doubao
Key Commands
Standalone CLI Commands
# Interactive setup
/setup
# Run full research pipeline
/research-pipeline "your research direction"# Run with reference paper and base repo
/research-pipeline "improve method X" -- ref paper: https://arxiv.org/abs/2406.04329, base repo: https://github.com/org/project
# Generate rebuttal from reviews
/rebuttal "paper/ + reviews" -- venue: ICML, character limit: 5000
# Plan mode (step-by-step breakdown)
/plan "implement transformer variant"# Research Wiki operations
/wiki add paper <arxiv_url>
/wiki add idea "your idea description"
/wiki query "search term"
/wiki export# Meta-optimization (self-improvement)
/meta-optimize
# Task management
/tasks
/tasks add "task description"
/tasks complete <id>
# Help and info
/help
/models # List available models
Claude Code Skill Commands
When using ARIS as Claude Code skills, trigger workflows with natural language:
"Use ARIS to review this paper: https://arxiv.org/abs/2406.04329"
"Generate research ideas about discrete diffusion models"
"Run the research pipeline for improving attention mechanisms"
"Help me write a rebuttal for these ICML reviews"
Core Workflows
1. Full Research Pipeline
End-to-end autonomous research:
/research-pipeline "factorized gap in discrete diffusion LMs"
ARIS reads the paper → finds weaknesses → uses that specific codebase → generates targeted improvements.
2. Targeted Idea Discovery
Generate ideas from specific sources:
# In Claude Code, reference the skill"""
Use Workflow 1: DiscoverPaper skill to find ideas from:
- arXiv search: "vision transformers"
- GitHub repo: https://github.com/google-research/vision_transformer
- Local paper: ./papers/vit_analysis.pdf
"""
ARIS will:
Parse papers/code
Extract key insights
Generate 5-10 novel ideas
Route to external reviewer
Return scored, critiqued ideas
3. Paper Review Loop
Multi-round review with automated improvements:
# Standalone CLI
/review-paper paper_draft.md --rounds 3
# In Claude Code"Review this draft and improve it through 3 rounds: ./draft.md"
Review process:
External LLM critiques (e.g., GPT-5.4)
Claude Code addresses weaknesses
Repeat until score plateau or max rounds
Final output with score progression graph
4. Rebuttal Generation
Parse reviews and draft rebuttal:
/rebuttal "paper_dir/" -- venue: ICML, character limit: 5000
reviewer:oracle-pro# GPT-5.4 Pro via Oracle MCP (strongest)reviewer:gpt-5.4# GPT-5.4 standardreviewer:claude-opus# Claude for reviewreviewer:auto# Smart routing based on executor
Research Wiki Configuration
Enable persistent memory across sessions:
# In CLI
/setup
# Enable "Research Wiki" option# Or set in config
wiki:
enabled: true
path: ~/.aris/wiki/
auto_commit: true# Git commit after each change
export HTTP_PROXY=http://localhost:8080
export HTTPS_PROXY=http://localhost:8080
# Or in /setup# Select provider → Configure proxy URL
Custom API endpoints:
# For Anthropic-compatible proxies (Bedrock, etc.)
/setup
# Provider: Anthropic# Custom base URL: https://bedrock.amazonaws.com/anthropic/
Local models (LM Studio/Ollama):
/setup
# Select "LM Studio / Ollama (Local)"# Base URL: http://localhost:1234/v1# Model: local-model-name
Code Examples
Example 1: Idea Discovery from Paper
# skill: workflow-1-discover-paper.md"""
I want to discover ideas from this paper:
https://arxiv.org/abs/2305.14342 (Transformer-XL)
Focus on: attention mechanism improvements
"""# ARIS will:# 1. Download and parse paper# 2. Extract key insights about XL attention# 3. Generate 5-10 novel ideas# 4. Send to GPT-5.4 for review# 5. Return scored ideas with critiques
Output structure:
## Discovered Ideas (Reviewed)### Idea 1: Factorized Relative Position Embeddings (Score: 8.5/10)**Core insight:** XL uses dense relative position matrix — factorize it.
**Reviewer critique (GPT-5.4):**- ✅ Novelty: High (not explored in XL paper)
- ✅ Feasibility: Doable (standard tensor decomposition)
- ⚠️ Impact: Need to verify on long sequences
- ⚠️ Risk: May hurt performance if rank too low
**Next steps:** Implement SVD-based factorization, benchmark on PG-19
---
### Idea 2: Learnable Decay for Relative Attention (Score: 7.2/10)
...
Example 2: Experiment Automation
# skill: workflow-2-run-experiment.md"""
Clone https://github.com/kimiyoung/transformer-xl
Implement Idea 1 (factorized position embeddings)
Run on enwik8 benchmark
Compare with baseline
"""# ARIS will:# 1. Clone repo# 2. Create experiment branch# 3. Modify model code (e.g., pytorch_modules/rel_multihead_attn.py)# 4. Set up training config# 5. Run experiment# 6. Parse results# 7. Generate comparison report
Generated experiment code:
# aris_experiments/factorized_rel_pos/model_patch.pyimport torch
import torch.nn as nn
classFactorizedRelativeAttention(nn.Module):
def__init__(self, d_model, n_heads, rank=64):
super().__init__()
self.d_model = d_model
self.n_heads = n_heads
self.rank = rank
# Factorized position embeddings: (seq_len, d_model) ≈ U @ V^Tself.U = nn.Parameter(torch.randn(2048, rank)) # max_seq_lenself.V = nn.Parameter(torch.randn(d_model, rank))
defforward(self, q, k, v, pos_emb):
# Compute relative position on-the-fly
rel_pos = self.U @ self.V.t() # (2048, d_model)# ... rest of attention logic
# skill: workflow-3-write-paper.md"""
Write a paper about the factorized position embeddings experiment.
Title: "Efficient Transformers via Factorized Relative Attention"
Target venue: ICML 2026
Sections: Abstract, Introduction, Method, Experiments, Conclusion
"""# ARIS will:# 1. Generate initial draft# 2. Send to GPT-5.4 for review# 3. Address critiques (e.g., "add ablation study")# 4. Re-review# 5. Repeat for N rounds or until score plateau
Review loop:
## Round 1 Review (GPT-5.4, Score: 6.5/10)**Strengths:**- Clear motivation (long-context efficiency)
- Solid experimental results
**Weaknesses:**- Missing ablation on rank hyperparameter
- No comparison with Linear Attention baseline
- Introduction lacks related work on efficient Transformers
**Suggestions:**1. Add Table 2: Rank ablation (r=16,32,64,128)
2. Cite Performer, Linformer in related work
3. Add wall-clock time comparison
---
## Round 2 Review (GPT-5.4, Score: 8.1/10)**Improvements:**
✅ Added rank ablation (Table 2)
✅ Expanded related work
✅ Wall-clock benchmarks included
**Remaining issues:**- Figure 3 caption unclear
- Conclusion should mention future work
---
## Round 3 Review (GPT-5.4, Score: 8.8/10)**Near-ready:** Minor edits only. Ready for submission.
Example 4: Rebuttal Generation
# reviews.txt contains reviewer comments# paper/ directory has the submitted paper
/rebuttal "paper/ reviews.txt" -- venue: ICML, character limit: 5000
Generated rebuttal structure:
# Rebuttal to ICML Reviews**Character count: 4847 / 5000**## Response to Reviewer 1 (Score: 6 → ?)**Q1: "Rank ablation missing — how does r affect performance?"**
We thank the reviewer for this suggestion. We have added Table 2 (Appendix) showing rank ablation (r=16,32,64,128). Key findings: r=64 is optimal (PPL 1.08, speed +52%), r=128 matches baseline quality (PPL 1.06) but slower, r=32 degrades PPL to 1.15. We will include this in the camera-ready.
**Q2: "No comparison with Linear Attention methods."**
Valid point. We added Performer and FNet baselines (Table 3). Our method outperforms Performer by 0.04 PPL while being 20% faster due to factorization locality. Updated draft attached.
---
## Response to Reviewer 2 (Score: 7 → ?)**Q1: "Scalability to 100k+ sequences unclear."**
We have run additional experiments on PG-19 (100k context). Results: our method maintains +45% speedup with PPL degradation <0.05. Memory scales O(r·L) vs O(L²) for standard XL. Will add to Section 4.3.
---
**Common concern (R1, R3): "Related work incomplete."**
We have expanded Section 2 to cite Linformer, Performer, Longformer, and BigBird. Table 1 now includes complexity comparison.
---
**Summary:** We address all major concerns with new experiments and expanded analysis. We believe these changes strengthen the paper significantly.
# Read existing paper → find gaps → generate fixes
paper_url = "https://arxiv.org/abs/2406.04329"
base_repo = "https://github.com/org/paper-code"# ARIS extracts weaknesses from paper
weaknesses = extract_weaknesses(paper_url)
# Generate ideas that specifically address those weaknesses
ideas = generate_targeted_ideas(weaknesses, base_repo)
# Run experiments with the exact codebase from the paper
results = run_experiments(ideas, base_repo)
Pattern 4: Cross-Model Review Chain
# Use different models for different stages
executor = "claude-opus-4.7"# Fast, creative execution
reviewer_1 = "gpt-5.4"# Rigorous critique
reviewer_2 = "oracle-pro"# Final stress test
draft = generate_draft(executor)
critique_1 = review(draft, reviewer_1)
improved = revise(draft, critique_1, executor)
critique_2 = review(improved, reviewer_2)
final = revise(improved, critique_2, executor)
Troubleshooting
API Key Issues
# Error: "ANTHROPIC_API_KEY not found"export ANTHROPIC_API_KEY=your_key_here
# Verify in CLI
/setup
# Check "Current configuration" section
Model Availability
# List available models for your API keys
/models
# Error: "Model not available"# Solution: Check /setup → verify API key → select available model
Rate Limits
# ARIS auto-retries on 429/5xx errors# Default: 3 retries with exponential backoff# To adjust (in skill YAML):
retry:
max_attempts: 5
backoff_multiplier: 2.0
initial_delay: 1.0# seconds
Cross-Model Review Not Working
# Error: "Reviewer returned empty response"# Common cause: reviewer API key missing# Check:echo$OPENAI_API_KEY# For gpt-5.4 reviewerecho$ANTHROPIC_API_KEY# For claude reviewer# Fix:
/setup
# Configure reviewer provider separately
Experiment Failures
# Error: "Git clone failed"# Solution: Check repo URL, network, auth# Error: "Experiment timeout"# Solution: Increase timeout in skill YAML:
experiment:
timeout: 7200# seconds (default: 3600)# Error: "CUDA out of memory"# Solution: Add batch size reduction to experiment config:
training:
batch_size: 16# reduce from default
gradient_accumulation: 4
Wiki Corruption
# Error: "Wiki index corrupted"# Solution: Rebuild indexcd ~/.aris/wiki
rm graph.json
/wiki rebuild
# Or reset wiki entirelyrm -rf ~/.aris/wiki
/setup # Re-enable wiki
Character Limit in Rebuttal
# Error: "Rebuttal exceeds character limit"# ARIS auto-compresses, but if still over:# 1. Use quick mode to see strategy first
/rebuttal "reviews" -- character limit: 5000, quick mode: true# 2. Manually edit strategy.md to remove low-priority responses# 3. Re-run with edited strategy
/rebuttal "reviews" -- character limit: 5000, resume: strategy.md
Local Model Connection
# Error: "Cannot connect to LM Studio"# Solution: # 1. Start LM Studio server# 2. Check port (default: 1234)
curl http://localhost:1234/v1/models # Should return model list# 3. In /setup:# Provider: LM Studio# Base URL: http://localhost:1234/v1# Model: <model-name-from-curl>
Permission Issues (Standalone CLI)
# Error: "Permission denied" when running experiments# Solution: ARIS prompts before executing tools# Check permission mode:
/setup
# Tool Permission: Prompt (recommended) | Auto-allow | Deny# Or set in config:
security:
tool_permission: prompt # always ask before running code
Advanced Features
Research Wiki (Persistent Memory)
Track papers, ideas, experiments across sessions:
# Add paper
/wiki add paper https://arxiv.org/abs/2305.14342
# Add idea
/wiki add idea "Factorized relative position embeddings for Transformer-XL"# Link idea to paper
/wiki link idea:1 paper:1 "inspired_by"# Query relationships
/wiki query "ideas from Transformer-XL paper"# Export to Markdown
/wiki export ./research_notes/
Relationship types:
inspired_by: idea → paper
improves_on: idea → idea
validated_by: idea → experiment
contradicts: claim → claim
Meta-Optimization (Self-Improvement)
ARIS analyzes its own logs and proposes skill improvements:
## Detected Pattern: Review scores often plateau at 8.0**Root cause:** Reviewer prompt lacks specific grading rubric.
**Proposed fix:**```diff
--- skills/workflow-3-write-paper.md
+++ skills/workflow-3-write-paper.md
@@ -15,6 +15,12 @@
Review this paper draft and provide:
- Score (1-10)
- Strengths (3-5 points)
- Weaknesses (3-5 points)
+
+**Grading rubric:**
+- 9-10: Publication-ready, minor edits only
+- 7-8: Strong work, needs revisions
+- 5-6: Major issues, needs rework
+- 1-4: Fundamental flaws
Apply this patch? (y/n): y
### Plan Mode
Break down complex tasks before execution:
```bash
/plan "implement a vision transformer from scratch and train on CIFAR-10"
# ARIS generates step-by-step plan:
# 1. Set up environment (PyTorch, datasets)
# 2. Implement patch embedding layer
# 3. Implement transformer encoder blocks
# 4. Implement classification head
# 5. Write training loop
# 6. Run experiments with different configs
# 7. Analyze results
# Execute plan:
/execute-plan
Integration with Other Tools
Cursor Integration
# In Cursor settings.json:
{
"cursor.aiRules": [
"Use ARIS skills from ~/claude-code-skills/aris/skills/",
"For research tasks, trigger ARIS workflows",
"For paper review, use workflow-3-write-paper.md"
]
}
