Antigravity Skill Vault is a collection of reusable Agent Skills for Google Antigravity, covering software development, operations, security, and business work. It is for people who want Antigravity agents to follow specialized expertise, personas, and structured workflows. The catalogue skills are entries from this collection.
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 skills add rmyndharis/antigravity-skills --skill article-illustrationsgit clone --depth 1 https://github.com/rmyndharis/antigravity-skillsWrote 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/skills/rmyndharis/antigravity-skills/article-illustrations)<a href="https://agentmods.dev/skills/rmyndharis/antigravity-skills/article-illustrations"><img src="https://agentmods.dev/badge/skills/rmyndharis/antigravity-skills/article-illustrations/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/rmyndharis/antigravity-skills/article-illustrations"><img src="https://agentmods.dev/badge/skills/rmyndharis/antigravity-skills/article-illustrations.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00045 | $0.02233 |
| Opus 5.5 | $0.00018 | $0.00893 |
| Sonnet 5.5 | $0.00009 | $0.00447 |
| Haiku 4.5 | $0.00005 | $0.00223 |
Grade A, and why
article-illustrations scanned grade A with 0 findings 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 1mo 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
86% identical to article-illustrations — 117 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 215 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Article Illustrations — Grav Hand-Drawn Style
Overview
Generate 16:9 landscape hand-drawn illustrations for articles, blog posts, and technical content. Each illustration captures one cognitive anchor point from an article and turns it into a clean, absurd, memorable whiteboard-sketch explanation.
The skill uses a recurring character IP called Grav: a small, round, always-floating figure with dot eyes and a thin antenna. Grav participates in the core action of every illustration — never just decoration.
Repository: vssinghh/article-illustrations
Use this skill when
- Writing articles, blog posts, or documentation that need inline illustrations
- Turning abstract concepts into concrete visual metaphors
- Establishing a consistent visual language across multiple articles
- Producing hand-drawn explanation sketches, not PPT infographics
Do not use this skill when
- You need polished, corporate-style PPT infographics or formal flowcharts
- You do not want a recurring character IP (Grav) featured in the illustrations
- You require animated, 3D, or realistic interface mockups instead of whiteboard sketches
Instructions
Step 1: Digest the Article
Read the article and identify cognitive anchor points — core judgments, turning points, input/output loops, before/after contrasts, and common pitfalls. Don't distribute illustrations evenly; prioritize moments that benefit from visual explanation.
Step 2: Plan a Shot List
For each illustration, define:
- Placement: After which section
- Theme: What this image is about
- Core Meaning: The one idea it conveys
- Structure Type: One of 8 composition patterns (Workflow, System Closeup, Before/After, Role States, Conceptual Metaphor, Layered Method, Map Route, Mini Comic)
- Grav's Action: What Grav is doing in the scene
- Annotation Labels: 3–5 short English labels
Step 3: Generate Images
Fill in the Prompt Template below with the shot-list details and generate one image per illustration. Use whatever image-generation capability is available in your environment — a built-in generate_image tool, an MCP image server, or simply by emitting the filled-in prompt for the user to paste into their own generator. Generate each image separately; never combine illustrations. Every image follows strict style rules:
- Pure white background, no textures
- Black hand-drawn line art with slight wobble
- Sparse red/orange/blue handwritten annotations
- Grav always floating (never touching surfaces)
- One core idea per image
- 40–60% canvas usage, 35%+ whitespace
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.
- 1mo ago First seen · 215 lines · 45 tokens per session scan A b4a68c84e7e6
article-illustrations is a skill published in the GitHub repository rmyndharis/antigravity-skills (1,850 stars, last pushed 11d ago), licensed MIT. It adds 45 tokens to every session and 2,233 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 86% identical to article-illustrations, differing in 117 lines, and is treated as a copy.
Other skills, from other repositories
ai-news-skill
Fetch the latest AI/ML news, trending open-source projects, social media discussions, and KOL (Key Opinion Leader) perspectives from 370+ curated sources. Use when the user asks about recent AI news, AI trends, what's happening in machine learning, open-source AI projects, AI Twitter/Reddit discussions, or wants a…
code-review
Systematic code review skill covering both requesting a review (pre-commit checklist) and receiving and responding to review feedback. Checks code quality, security, test coverage, architectural alignment, and documentation before any code is committed.
writing-plans
TÜRKÇE AÇIKLAMA ─────────────── Bu skill, onaylanmış bir scope veya fikirden somut, uygulanabilir bir implementasyon planı üretir. Hangi dosya değişecek, hangi sırayla, kim yapacak, ne kadar sürecek, hangi riskler var — hepsini netleştirir. Planın çıktısı doğrudan executing-plans veya dispatching-parallel-agents…
brainstorming
TÜRKÇE AÇIKLAMA ─────────────── Bu skill, bir fikir veya problemi Sokratik yöntemle rafine eder. Agent sana cevap vermez — sorular sorar. Bu sorular aracılığıyla fikrin netleşir, varsayımlar sorgulanır, kapsam belirlenir ve gerçek ihtiyaç ortaya çıkar. "Ne yapalım?" sorusunu "Tam olarak ne yapmamız gerekiyor ve…
project-context-primer
Run this skill at the very start of any new conversation or agent session before writing a single line of code. It loads the project's architectural decisions, conventions, known gotchas, and current task status so the agent operates with full context — not as a blank slate.
test-driven-execution
Before writing any implementation code, define the acceptance criteria and test cases that the code must satisfy. Agents then write code to pass these tests — not to match a vague description. Eliminates "it works on my machine" and "I think this is what you wanted" outcomes.