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AutoResearchWithEyes
Let Claude Code do research while you sleep. Wake up to find your paper scored, weaknesses identified, experiments run, and narrative rewritten — autonomously.
A Claude Code plugin for autonomous ML research workflows. Orchestrates cross-model collaboration — Claude Code drives the research while an external LLM (via Codex MCP) acts as a critical reviewer. Also supports alternative model combinations (e.g., GLM + GPT, GLM + MiniMax) — no Claude API required.
Why cross-model? A single model reviewing its own output creates blind spots. Two complementary models — Claude Code for fast execution, GPT-5.4 xhigh for rigorous critique — produce better outcomes than either alone. Going from 1 to 2 models is the biggest gain; adding more gives diminishing returns.
Quick Start
# 1. Clone and enter the project
git clone https://github.com/llv23/AutoResearchWithEyes.git
cd AutoResearchWithEyes
# 2. Set up Codex MCP (for cross-model review)
npm install -g @openai/codex
codex auth login
# Codex MCP auto-configures from .mcp.json when running in the project directory
# 3. Launch Claude Code — skills and commands are auto-discovered
claude
# Run any workflow command:
> /autor.idea-discovery "your research direction" # Plain text → ./autor.idea_discovery/
> /autor.idea-discovery path/to/autor.idea_discovery/task_spec.md # Spec file → path/to/autor.idea_discovery/
> /autor.auto-review-loop # Review → fix → re-review overnight
> /autor.paper-writing "NARRATIVE_REPORT.md" # Narrative → polished PDF
> /autor.research-pipeline "your research direction" # Full end-to-end pipeline
Each command produces a self-contained output folder — the input spec, intermediate results, final reports, and live status tracking are all organized in one place:
path/to/
└── autor.idea_discovery/ # Self-contained output folder
├── task_spec.md # Copy of input (portability & reproducibility)
├── task_status.md # Live status, decisions, linked idea rankings
├── LITERATURE_SURVEY.md # Phase 1: landscape summary with paper table
├── IDEA_REPORT.md # Phase 5: ranked ideas + experiment plan
└── REVIEW_<topic>_<date>.md # Phase 4: external reviewer feedback
task_status.md is updated after each phase — it tracks pipeline progress, key decisions, and includes a ranked idea table where each idea links to its full description in IDEA_REPORT.md.
See click-through/case-0/ for a complete worked example.
Output Folder Conventions
All workflow commands follow the same self-contained output pattern: the output folder includes task_spec.md (input), task_status.md (live status with linked rankings), and all generated results — making it portable, shareable, and reproducible.
| Command | Input | Output Folder |
|---|---|---|
/autor.idea-discovery path/to/spec.md | spec.md | path/to/autor.idea_discovery/ |
/autor.idea-discovery "plain text" | (inline) | ./autor.idea_discovery/ |
/autor.idea-discovery spec.md — output: custom/ | spec.md | custom/ |
Override the output folder with
— output: path/to/output/appended to any command.
See Setup for full details.
Features
- 10 composable skills — atomic building blocks: literature search, idea generation, novelty check, experiments, paper writing
- 4 workflow commands — orchestrate skills + agents into end-to-end pipelines (
/autor.idea-discovery,/autor.auto-review-loop,/autor.paper-writing,/autor.research-pipeline) - 2 specialized agents —
research-reviewer(senior ML reviewer via Codex MCP) andpaper-improver(2-round auto-improvement) - Cross-model collaboration — Claude Code executes, GPT-5.4 xhigh reviews. Adversarial, not self-play
- Centralized configuration — all constants in
CLAUDE.md, override per-invocation with inline arguments - Venue templates — bundled template directories with fallback resolution:
TEMPLATE_DIR/VENUE/→ bundled → error with instructions - GPU deployment — auto rsync, screen sessions, multi-GPU parallel experiments, live monitoring
- Flexible models — default Claude x GPT-5.4, also supports GLM + GPT, GLM + MiniMax
