Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add instructions/jameszor/antigravity/claude-mdgit clone --depth 1 https://github.com/JamesZor/antigravityWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/jameszor/antigravity/claude-md)<a href="https://agentmods.dev/instructions/jameszor/antigravity/claude-md"><img src="https://agentmods.dev/badge/instructions/jameszor/antigravity/claude-md.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.01768 | $0.01768 |
| Opus 5 | $0.00884 | $0.00884 |
| Sonnet 5 | $0.00354 | $0.00354 |
| Haiku 4.5 | $0.00177 | $0.00177 |
Grade A, and why
antigravity CLAUDE.md scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- **SKILL.md files use relative script paths** (`uv run scripts/x.py`), but a worker's cwd is its own job dir, not the skill dir. `skill_preamble()` therefore injects each skill's **absolute** path. Without it, every scr How it starts
The opening of the file, as written. The whole thing — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
What this is
An MCP (Model Context Protocol) server that exposes the Antigravity CLI (agy, Google's Gemini agent) as a set of tools usable from Claude Code, Claude Desktop, Cursor, and Windsurf. The animating idea: Claude is the Conductor/frontier model, and agy/Gemini is a cheaper executing agent to which Claude offloads bulky, token-heavy work (web research, codebase indexing, commit-message generation, cross-model review) to keep Claude's own context lean. See the antigravity_workflow and antigravity_research_recipe MCP prompts (in antigravity_mcp/prompts.py) for the operating philosophy.
The implementation lives in the antigravity_mcp/ package; main.py is a thin entrypoint. Layout: app.py (the shared FastMCP instance), agy.py (subprocess/model-tier helpers + the pure skill layer, no MCP — unit-testable), jobs.py (filesystem-backed background jobs + _start_batch fan-out scaffolding), delegation.py, research.py, architect.py, skills.py, codegit.py, prompts.py, and server.py (side-effect imports that register everything + main()).
Three multi-step pipelines build on this: deep-research (research_fanout → research_status → collect_digests, plus the /deep-research skill and antigravity_research_recipe prompt); architect/build (propose_design_questions → review_fanout/research_fanout → draft_design_doc, plus the /architect skill and antigravity_build_recipe prompt) — a Spec-Driven loop (Requirements → Design → Tasks → Implement) for creating/improving codebases; and science (propose_science_plan → science_fanout → research_status → collect_digests, plus the /science-research skill and antigravity_science_recipe prompt). All three share the same fan-out scaffolding (_start_batch) and batch collectors (research_status/collect_digests are batch-generic — they read any sub_NN/subreport.md + manifest.json, so review and science batches reuse them unchanged).
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 58 lines · 1,768 tokens per session scan A 26976149e8bf
antigravity CLAUDE.md is an instructions file published in the GitHub repository JamesZor/antigravity (0 stars, last pushed 1mo ago), licensed MIT. It adds 1,768 tokens to every session, about $0.0088 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other instructions, from other repositories
AionUi AGENTS.md
AGENTS.md instructions for iOfficeAI/AionUi, covering aionui - project guide, code conventions, file & directory structure, naming and ui library & icons.
opencodex AGENTS.md
AGENTS.md instructions for lidge-jun/opencodex, covering agents.md, what this project is, repository layout, optional subsystems stay off the core path and the devlog directory.
InvestSkill GEMINI.md
Gemini CLI instructions for yennanliu/InvestSkill, covering investskill — gemini cli setup & usage guide, installation & setup, quick start, navigate to the investskill directory and start gemini cli (loads gemini.md automatically).
mcp-devtools CLAUDE.md
Claude Code instructions for sammcj/mcp-devtools, covering claude.md, commands, build and run, testing and code quality.
agent-guardrails-template CLAUDE.md
Claude Code instructions for TheArchitectit/agent-guardrails-template, covering project guidelines, 0. navigation maps (read first), 0.1 agent-gdui-2026 initialization context, 1. context & setup and 2. token-saving rules (strict).
chimera-agent AGENTS.md
AGENTS.md instructions for brcampidelli/chimera-agent, covering working in this repository, where the rules live, hard rules, verifying and python — the whole suite.