Antigravity Adaptation Guide (ARIS Workflows)

Use ARIS research workflows in Google Antigravity — the agent-first AI IDE from Google DeepMind.

Antigravity natively supports SKILL.md files with the same YAML frontmatter + Markdown body format used by ARIS, making it one of the most natural hosts for ARIS workflows.

1. Key Differences: Claude Code vs Antigravity

Concept	Claude Code	Antigravity
Skill invocation	`/skill-name "args"` (slash command)	Agent auto-discovers from `description`; or read SKILL.md via `view_file`
Skill storage	`~/.claude/skills/skill-name/SKILL.md`	`~/.gemini/antigravity/skills/skill-name/SKILL.md` (global) or `<workspace>/.agents/skills/skill-name/SKILL.md` (project-local)
MCP servers	`claude mcp add ...`	`~/.gemini/settings.json` → `mcpServers` section
Project instructions	`CLAUDE.md` in project root	`GEMINI.md` in project root (equivalent)
Agent execution	Persistent CLI session, auto-compact	Editor sidebar + Manager View; multi-agent orchestration
File references	Auto-read from project	`view_file` tool; agent reads workspace files automatically
Long-running jobs	Single CLI session	Agent sessions with artifact-based checkpoints
Models available	Claude Opus 4.6 / Sonnet 4.6	Gemini 3.1 Pro (high), Claude Opus 4.6 (Thinking), GPT-OSS-120B

2. Model Selection

Antigravity supports multiple models as the executor (the model that runs ARIS workflows):

Model	Best for	Configuration
Claude Opus 4.6 (Thinking)	Complex reasoning, long pipelines, code generation	Model selector → `Claude Opus 4.6 (Thinking)`
Gemini 3.1 Pro (high)	Fast iteration, large context, Google ecosystem integration	Model selector → `Gemini 3.1 Pro` with reasoning effort set to `high`

Tip: Claude Opus 4.6 (Thinking) and Gemini 3.1 Pro (high) have different strengths. Claude Opus excels at step-by-step reasoning and code accuracy; Gemini 3.1 Pro has a larger context window and faster response times. Choose based on your workflow needs.

Model-Specific Notes

For Claude Opus 4.6 (Thinking):

Extended thinking mode is enabled by default — ideal for complex research reasoning
ARIS skill instructions will be followed very faithfully
May be slower on long review prompts but more thorough

For Gemini 3.1 Pro (high):

Larger context window (handles more project files at once)
Natively understands SKILL.md format (Google's own standard)
Set reasoning effort to high for best research quality — add to ~/.gemini/settings.json:
```
{
  "model": {
    "name": "gemini-3.1-pro-preview"
  }
}
```

3. Setup

3.1 Install skills

git clone https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep.git
cd Auto-claude-code-research-in-sleep

# Option A: Global install (available across all projects)
mkdir -p ~/.gemini/antigravity/skills
cp -r skills/* ~/.gemini/antigravity/skills/

# Option B: Project-local install (recommended for isolation)
mkdir -p /path/to/your/project/.agents/skills
cp -r skills/* /path/to/your/project/.agents/skills/

Important: Antigravity discovers skills from ~/.gemini/antigravity/skills/ (global) and <workspace>/.agents/skills/ (project-scoped). The agent sees skill names and descriptions at startup, then loads full SKILL.md content when relevant.

3.2 Set up Codex MCP in Antigravity (for review skills)

ARIS uses an external LLM (GPT-5.4 via Codex) as a critical reviewer. To enable this in Antigravity:

Install Codex CLI and authenticate:

npm install -g @openai/codex
codex login   # authenticate with your ChatGPT or API key

Add MCP server in Antigravity — edit ~/.gemini/settings.json:

{
  "mcpServers": {
    "codex": {
      "command": "codex",
      "args": ["mcp-server"]
    }
  }
}

Or for project-local scope, create .gemini/settings.json in your project root:

{
  "mcpServers": {
    "codex": {
      "command": "codex",
      "args": ["mcp-server"]
    }
  }
}

Restart Antigravity. Verify the MCP server connects — the agent will report available tools that include mcp__codex__codex and mcp__codex__codex-reply.

3.3 Alternative reviewer MCP (no OpenAI API)

If you don't have an OpenAI API key, use the llm-chat MCP server with any OpenAI-compatible API (DeepSeek, GLM, MiniMax, Kimi, etc.):

Create a virtual environment and install the required dependency:

cd /path/to/Auto-claude-code-research-in-sleep
python3 -m venv .venv
.venv/bin/pip install -r mcp-servers/llm-chat/requirements.txt

Add MCP server — edit ~/.gemini/settings.json. Both paths must be absolute:

{
  "mcpServers": {
    "llm-chat": {
      "command": "/path/to/Auto-claude-code-research-in-sleep/.venv/bin/python3",
      "args": ["/path/to/Auto-claude-code-research-in-sleep/mcp-servers/llm-chat/server.py"],
      "env": {
        "LLM_BASE_URL": "https://api.deepseek.com/v1",
        "LLM_API_KEY": "your_key",
        "LLM_MODEL": "deepseek-chat"
      }
    }
  }
}

Restart Antigravity. The llm-chat MCP should appear in available tools.

See LLM_API_MIX_MATCH_GUIDE.md for tested provider configurations.

3.4 Project instructions (GEMINI.md)

Antigravity uses GEMINI.md (equivalent to Claude Code's CLAUDE.md) for project-specific instructions. Create this file in your project root:

## GPU Server (for auto-experiments)

- SSH: `ssh my-gpu-server` (key-based auth, no password)
- GPU: 4x A100
- Conda env: `research` (Python 3.10 + PyTorch)
- Activate: `eval "$(/opt/conda/bin/conda shell.bash hook)" && conda activate research`
- Code directory: `/home/user/experiments/`
- Use `screen` for background jobs: `screen -dmS exp0 bash -c '...'`

## Research Project

- Topic: [your research topic]
- Target venue: ICLR/NeurIPS/ICML
- Key files: NARRATIVE_REPORT.md, idea-stage/IDEA_REPORT.md

4. How to Invoke Skills

Antigravity discovers ARIS skills via the YAML description field in each SKILL.md. There are three approaches:

Approach A: Natural language (recommended — Antigravity auto-discovers)

Simply describe what you want in the chat. Antigravity matches your intent to installed skills:

Run the auto review loop for "factorized gap in discrete diffusion LMs".

If ARIS skills are installed (§3.1), Antigravity will automatically discover and activate the auto-review-loop skill.

Approach B: Explicit skill reference

Ask the agent to read a specific SKILL.md:

Read the file skills/auto-review-loop/SKILL.md and follow its instructions.
Topic: "factorized gap in discrete diffusion LMs".

Or if installed globally:

Read ~/.gemini/antigravity/skills/auto-review-loop/SKILL.md and execute it.
Topic: "factorized gap in discrete diffusion LMs".

Approach C: Direct prompt (one-off use)

Copy the relevant workflow instructions directly into the chat. Best for quick, one-time use.

5. Workflow Mapping

Workflow 1: Idea Discovery

Claude Code:

/idea-discovery "your research direction"

Antigravity equivalent:

Run the full idea discovery pipeline for "your research direction".

Follow these sub-skills in sequence:
1. Read and execute skills/research-lit/SKILL.md — literature survey
2. Read and execute skills/idea-creator/SKILL.md — brainstorm ideas
3. Read and execute skills/novelty-check/SKILL.md — verify novelty
4. Read and execute skills/research-review/SKILL.md — critical review
5. Read and execute skills/research-refine-pipeline/SKILL.md — refine method + plan experiments

Tip: If the context gets long, run each phase as a separate agent task in Antigravity's Manager View. Pass results via files (e.g., idea-stage/IDEA_REPORT.md, refine-logs/FINAL_PROPOSAL.md).

Workflow 1.5: Experiment Bridge

Claude Code:

/experiment-bridge

Antigravity equivalent:

Read and execute skills/experiment-bridge/SKILL.md.
Read refine-logs/EXPERIMENT_PLAN.md and implement the experiments.
Deploy to GPU via skills/run-experiment/SKILL.md.

Workflow 2: Auto Review Loop

Claude Code:

/auto-review-loop "your paper topic"

Antigravity equivalent:

Read and execute skills/auto-review-loop/SKILL.md.
Run the auto review loop for "your paper topic".
Read project narrative docs, memory files, experiment results.
Use MCP tool mcp__codex__codex for external review.

Important: If using the llm-chat MCP instead of Codex, replace mcp__codex__codex with mcp__llm-chat__chat. Or use the adapted skill: skills/auto-review-loop-llm/SKILL.md.

Workflow 3: Paper Writing

Claude Code:

/paper-writing "NARRATIVE_REPORT.md"

Antigravity equivalent:

Read and execute skills/paper-writing/SKILL.md.
Input: NARRATIVE_REPORT.md in project root.

Sub-skills to execute in sequence:
1. Read and execute skills/paper-plan/SKILL.md — outline + claims-evidence matrix
2. Read and execute skills/paper-figure/SKILL.md — generate plots and tables
3. Read and execute skills/paper-write/SKILL.md — write LaTeX sections
4. Read and execute skills/paper-compile/SKILL.md — build PDF
5. Read and execute skills/auto-paper-improvement-loop/SKILL.md — review and polish

Full Pipeline

For the full pipeline (/research-pipeline), leverage Antigravity's multi-agent capability to run stages in parallel where possible:

Stage	What to do	Output files
1	Idea Discovery: `skills/idea-discovery/SKILL.md` + your direction	`idea-stage/IDEA_REPORT.md`, `refine-logs/FINAL_PROPOSAL.md`, `refine-logs/EXPERIMENT_PLAN.md`
2	Experiment Bridge: `skills/experiment-bridge/SKILL.md`	Experiment scripts, results
3	Auto Review Loop: `skills/auto-review-loop/SKILL.md`	`review-stage/AUTO_REVIEW.md`
4	Paper Writing: `skills/paper-writing/SKILL.md` + `NARRATIVE_REPORT.md`	`paper/` directory

Each stage reads the previous stage's output files, so context carries forward across agent sessions.

Note: Stage 4 expects a NARRATIVE_REPORT.md — see NARRATIVE_REPORT_EXAMPLE.md for the expected format.

6. MCP Tool Calls

ARIS skills reference MCP tools by name. These work identically in Antigravity once configured:

ARIS MCP tool	What it does	Required MCP server
`mcp__codex__codex`	Send prompt to GPT-5.4	Codex
`mcp__codex__codex-reply`	Continue conversation thread	Codex
`mcp__llm-chat__chat`	Send prompt to any OpenAI-compatible model	llm-chat
`mcp__zotero__*`	Search Zotero library	zotero (name may vary)
`mcp__obsidian-vault__*`	Search Obsidian vault	obsidian-vault (name may vary)

7. State Files & Recovery

ARIS workflows persist state to files for crash recovery. These work identically in Antigravity:

File	Purpose	Written by
`review-stage/REVIEW_STATE.json`	Auto-review loop progress	`auto-review-loop`
`review-stage/AUTO_REVIEW.md`	Cumulative review log	`auto-review-loop`
`idea-stage/IDEA_REPORT.md`	Ranked ideas with pilot results	`idea-discovery`
`PAPER_PLAN.md`	Paper outline + claims-evidence matrix	`paper-plan`
`refine-logs/FINAL_PROPOSAL.md`	Refined method proposal	`research-refine`
`refine-logs/EXPERIMENT_PLAN.md`	Experiment roadmap	`experiment-plan`
`refine-logs/EXPERIMENT_TRACKER.md`	Run-by-run execution status	`experiment-plan`

If an Antigravity agent session ends mid-workflow, start a new session and reference the state file:

Read skills/auto-review-loop/SKILL.md, then read review-stage/REVIEW_STATE.json and review-stage/AUTO_REVIEW.md.
Resume the auto review loop from the saved state.

8. GPU Server Setup

Add your server info to GEMINI.md in your project root (equivalent to CLAUDE.md):

## Remote Server

- SSH: `ssh my-gpu-server` (key-based auth, no password)
- GPU: 4x A100
- Conda env: `research` (Python 3.10 + PyTorch)
- Activate: `eval "$(/opt/conda/bin/conda shell.bash hook)" && conda activate research`
- Code directory: `/home/user/experiments/`
- Use `screen` for background jobs

Then invoke:

Read skills/run-experiment/SKILL.md and GEMINI.md.
Deploy the training script to the remote GPU server.

9. Antigravity-Specific Advantages

Antigravity provides several unique capabilities that enhance ARIS workflows:

Multi-Agent Orchestration

Use Antigravity's Manager View to run multiple ARIS stages simultaneously:

Agent 1: Literature survey (Workflow 1, Stage 1)
Agent 2: Running experiments on GPU (Workflow 1.5)
Agent 3: Reviewing and iterating on prior results (Workflow 2)

Browser Integration

Antigravity includes a built-in browser. Useful for:

Previewing generated charts/figures from /paper-figure
Testing web-based arXiv searches during /research-lit
Viewing compiled PDF from /paper-compile

Artifact System

Antigravity's artifact system (implementation plans, walkthroughs) maps naturally to ARIS outputs:

idea-stage/IDEA_REPORT.md → implementation plan artifact
review-stage/AUTO_REVIEW.md → walkthrough artifact
PAPER_PLAN.md → implementation plan artifact

Knowledge Persistence

Antigravity's knowledge system retains context across conversations:

Past review findings from /auto-review-loop are available in future sessions
Experiment configurations and results persist in knowledge items
Literature survey results can be referenced without re-running

10. Limitations & Workarounds

Limitation	Workaround
No native `/skill-name` slash commands	Use natural language (auto-discovery) or explicit `read SKILL.md` references
Skills reference `$ARGUMENTS`	Replace with your actual arguments in the prompt
SKILL.md files use `/skill-name` to call sub-skills	Tell the agent to read and execute the sub-skill SKILL.md files explicitly
`allowed-tools` not enforced	Antigravity's agent has access to all configured tools by default — not a problem in practice
`CLAUDE.md` references in skills	Antigravity reads `GEMINI.md` instead — rename or copy `CLAUDE.md` to `GEMINI.md`, or tell the agent to read both
Context window varies by model	Claude Opus 4.6: similar to Claude Code. Gemini 3.1 Pro: larger window. Both handle full pipelines well. Break into stages if needed

11. Quick Reference

# Literature survey
Read skills/research-lit/SKILL.md and search for papers on "discrete diffusion models".

# Idea discovery (full pipeline)
Read skills/idea-discovery/SKILL.md and run idea discovery for
"factorized gap in discrete diffusion LMs".

# Single deep review
Read skills/research-review/SKILL.md and review this research:
[describe your work or point to files].

# Auto review loop
Read skills/auto-review-loop/SKILL.md and run the auto review loop.
Topic: "your paper topic".

# Paper writing
Read skills/paper-writing/SKILL.md and write the paper from NARRATIVE_REPORT.md.

# Run experiment
Read skills/run-experiment/SKILL.md and GEMINI.md.
Deploy: python train.py --lr 1e-4 --epochs 100

12. Summary: Claude Code → Antigravity Migration Checklist

Install skills to ~/.gemini/antigravity/skills/ or <project>/.agents/skills/
Configure MCP servers in ~/.gemini/settings.json
Copy CLAUDE.md content to GEMINI.md (or keep both)
Select model: Claude Opus 4.6 (Thinking) or Gemini 3.1 Pro (high)
Use natural language or explicit skill references instead of /slash-commands
Verify MCP tools are available (codex or llm-chat)
Run a quick test: Read skills/research-review/SKILL.md and review my project

Provide feedback

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Antigravity Adaptation Guide (ARIS Workflows)

1. Key Differences: Claude Code vs Antigravity

2. Model Selection

Model-Specific Notes

3. Setup

3.1 Install skills

3.2 Set up Codex MCP in Antigravity (for review skills)

3.3 Alternative reviewer MCP (no OpenAI API)

3.4 Project instructions (GEMINI.md)

4. How to Invoke Skills

Approach A: Natural language (recommended — Antigravity auto-discovers)

Approach B: Explicit skill reference

Approach C: Direct prompt (one-off use)

5. Workflow Mapping

Workflow 1: Idea Discovery

Workflow 1.5: Experiment Bridge

Workflow 2: Auto Review Loop

Workflow 3: Paper Writing

Full Pipeline

6. MCP Tool Calls

7. State Files & Recovery

8. GPU Server Setup

9. Antigravity-Specific Advantages

Multi-Agent Orchestration

Browser Integration

Artifact System

Knowledge Persistence

10. Limitations & Workarounds

11. Quick Reference

12. Summary: Claude Code → Antigravity Migration Checklist

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Antigravity Adaptation Guide (ARIS Workflows)

1. Key Differences: Claude Code vs Antigravity

2. Model Selection

Model-Specific Notes

3. Setup

3.1 Install skills

3.2 Set up Codex MCP in Antigravity (for review skills)

3.3 Alternative reviewer MCP (no OpenAI API)

3.4 Project instructions (GEMINI.md)

4. How to Invoke Skills

Approach A: Natural language (recommended — Antigravity auto-discovers)

Approach B: Explicit skill reference

Approach C: Direct prompt (one-off use)

5. Workflow Mapping

Workflow 1: Idea Discovery

Workflow 1.5: Experiment Bridge

Workflow 2: Auto Review Loop

Workflow 3: Paper Writing

Full Pipeline

6. MCP Tool Calls

7. State Files & Recovery

8. GPU Server Setup

9. Antigravity-Specific Advantages

Multi-Agent Orchestration

Browser Integration

Artifact System

Knowledge Persistence

10. Limitations & Workarounds

11. Quick Reference

12. Summary: Claude Code → Antigravity Migration Checklist