antigravity-manager

aradotso/trending-skills · updated Apr 8, 2026

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$npx skills add https://github.com/aradotso/trending-skills --skill antigravity-manager
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Skill by ara.so — Daily 2026 Skills collection.

skill.md

Antigravity Manager

Skill by ara.so — Daily 2026 Skills collection.

Antigravity Manager is a professional AI account manager and proxy gateway. It takes Google (Gemini) and Anthropic (Claude) web session tokens and exposes them as standard API endpoints (OpenAI-compatible, Anthropic-native, and Gemini-native formats) with intelligent multi-account rotation, quota tracking, and automatic failover.

Key capabilities:

  • Multi-account management with real-time quota dashboards
  • Protocol conversion: web sessions → OpenAI / Anthropic / Gemini API
  • Auto-rotation on 429/401 errors (millisecond failover)
  • Model routing and remapping
  • Desktop app (Tauri v2 + React + Rust) or headless Docker/server mode

Installation

Option A: One-line script (Linux/macOS)

curl -fsSL https://raw.githubusercontent.com/lbjlaq/Antigravity-Manager/v4.1.30/install.sh | bash

Install a specific version:

curl -fsSL https://raw.githubusercontent.com/lbjlaq/Antigravity-Manager/v4.1.30/install.sh | bash -s -- --version 4.1.30

Dry run (preview without installing):

curl -fsSL https://raw.githubusercontent.com/lbjlaq/Antigravity-Manager/v4.1.30/install.sh | bash -s -- --dry-run

Option B: Windows (PowerShell)

irm https://raw.githubusercontent.com/lbjlaq/Antigravity-Manager/main/install.ps1 | iex

Option C: Homebrew (macOS / Linuxbrew)

brew tap lbjlaq/antigravity-manager https://github.com/lbjlaq/Antigravity-Manager
brew install --cask antigravity-tools

Option D: Docker (recommended for servers/NAS)

docker run -d --name antigravity-manager \
  -p 8045:8045 \
  -e API_KEY=$ANTIGRAVITY_API_KEY \
  -e WEB_PASSWORD=$ANTIGRAVITY_WEB_PASSWORD \
  -e ABV_MAX_BODY_SIZE=104857600 \
  -v ~/.antigravity_tools:/root/.antigravity_tools \
  lbjlaq/antigravity-manager:latest

Docker Compose:

# docker-compose.yml
version: "3.8"
services:
  antigravity:
    image: lbjlaq/antigravity-manager:latest
    container_name: antigravity-manager
    restart: unless-stopped
    ports:
      - "8045:8045"
    environment:
      - API_KEY=${ANTIGRAVITY_API_KEY}
      - WEB_PASSWORD=${ANTIGRAVITY_WEB_PASSWORD}
      - ABV_MAX_BODY_SIZE=104857600
    volumes:
      - ~/.antigravity_tools:/root/.antigravity_tools
docker compose up -d
docker logs antigravity-manager          # view logs / recover forgotten keys

Option E: Manual download

Download from GitHub Releases:

  • macOS: .dmg (Apple Silicon + Intel)
  • Windows: .msi or portable .zip
  • Linux: .deb, .rpm, or .AppImage

Authentication / Security Model

Antigravity uses two separate credentials:

Credential Env var Config key Purpose
API Key API_KEY api_key Authenticates AI API calls (Authorization: Bearer ...)
Web Password WEB_PASSWORD admin_password Logs into the management web UI

Scenario A — only API_KEY set:

  • Web UI login: use API_KEY
  • API calls: use API_KEY

Scenario B — both set (recommended):

  • Web UI login: WEB_PASSWORD only (API Key rejected for login)
  • API calls: API_KEY only

Recover credentials:

docker logs antigravity-manager
# or
grep -E '"api_key"|"admin_password"' ~/.antigravity_tools/gui_config.json

API Endpoints

The proxy server runs on port 8045 by default.

OpenAI-compatible (works with any OpenAI SDK)

POST http://localhost:8045/v1/chat/completions
Authorization: Bearer $ANTIGRAVITY_API_KEY

Anthropic-native (Claude Code, etc.)

POST http://localhost:8045/v1/messages
Authorization: Bearer $ANTIGRAVITY_API_KEY

Gemini-native

POST http://localhost:8045/v1/models/{model}:generateContent
Authorization: Bearer $ANTIGRAVITY_API_KEY

Code Examples

Python — OpenAI SDK (Gemini via Antigravity)

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["ANTIGRAVITY_API_KEY"],
    base_url="http://localhost:8045/v1",
)

response = client.chat.completions.create(
    model="gemini-2.5-pro",
    messages=[
        {"role": "user", "content": "Explain quantum entanglement in simple terms."}
    ],
    stream=True,
)

for chunk in response:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)

Python — Anthropic SDK (Claude via Antigravity)

import os
import anthropic

client = anthropic.Anthropic(
    api_key=os.environ["ANTIGRAVITY_API_KEY"],
    base_url="http://localhost:8045",
)

message = client.messages.create(
    model="claude-sonnet-4-5",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Write a haiku about distributed systems."}
    ],
)
print(message.content[0].text)

Python — Streaming with Anthropic

import os
import anthropic

client = anthropic.Anthropic(
    api_key=os.environ["ANTIGRAVITY_API_KEY"],
    base_url="http://localhost:8045",
)

with client.messages.stream(
    model="claude-opus-4-5",
    max_tokens=2048,
    system="You are a helpful coding assistant.",
    messages=[{"role": "user", "content": "Implement a binary search in Rust."}],
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

TypeScript / Node — OpenAI SDK

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.ANTIGRAVITY_API_KEY!,
  baseURL: "http://localhost:8045/v1",
});

async function chat(prompt: string): Promise<string> {
  const response = await client.chat.completions.create({
    model: "gemini-2.5-flash",
    messages: [{ role: "user", content: prompt }],
  });
  return response.choices[0].message.content ?? "";
}

// Image generation via Imagen 3
async function generateImage(prompt
how to use antigravity-manager

How to use antigravity-manager on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add antigravity-manager
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/aradotso/trending-skills --skill antigravity-manager

The skills CLI fetches antigravity-manager from GitHub repository aradotso/trending-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/antigravity-manager

Reload or restart Cursor to activate antigravity-manager. Access the skill through slash commands (e.g., /antigravity-manager) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.825 reviews
  • Pratham Ware· Dec 28, 2024

    Solid pick for teams standardizing on skills: antigravity-manager is focused, and the summary matches what you get after install.

  • Harper Lopez· Dec 12, 2024

    antigravity-manager has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Arjun Harris· Dec 12, 2024

    Useful defaults in antigravity-manager — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Yash Thakker· Sep 25, 2024

    We added antigravity-manager from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Amelia Wang· Sep 9, 2024

    antigravity-manager fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Liam Reddy· Aug 28, 2024

    We added antigravity-manager from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Dhruvi Jain· Aug 16, 2024

    antigravity-manager fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Olivia Ghosh· Jul 19, 2024

    antigravity-manager reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Oshnikdeep· Jul 7, 2024

    Registry listing for antigravity-manager matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Ganesh Mohane· Jun 26, 2024

    antigravity-manager reduced setup friction for our internal harness; good balance of opinion and flexibility.

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