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 mhrsdev/AI-Agent-Skills-Library --skill material-3git clone --depth 1 https://github.com/mhrsdev/AI-Agent-Skills-LibraryWrote 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/mhrsdev/ai-agent-skills-library/material-3)<a href="https://agentmods.dev/skills/mhrsdev/ai-agent-skills-library/material-3"><img src="https://agentmods.dev/badge/skills/mhrsdev/ai-agent-skills-library/material-3.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 | $0.00102 | $0.08079 |
| Opus 5 | $0.00051 | $0.04040 |
| Sonnet 5 | $0.00020 | $0.01616 |
| Haiku 4.5 | $0.00010 | $0.00808 |
Grade A, and why
material-3 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 8d 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
100% identical to material-3 — 0 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 — 668 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Material Design 3
This skill guides implementation of Google's Material Design 3 (MD3) — a personal, adaptive, expressive design system. MD3 uses dynamic color, tonal surfaces, rounded shapes, and spring-based motion to create UIs that feel alive and personal.
Philosophy
MD3 is built on three principles:
- Personal: Dynamic color adapts UI to the user's wallpaper or content. Theming is individual, not one-size-fits-all.
- Adaptive: Layouts transform across 5 window size classes. Components resize, reposition, and change form factor responsively.
- Expressive: Shape morphing, spring physics, and emphasized typography create moments of delight without sacrificing usability.
Current Updates: Google I/O 2026
Material's Google I/O 2026 update reinforces a Compose-first Android path and expands expressive/adaptive guidance:
- Material Android is Compose-first: For new Android work, prefer Jetpack Compose Material3 for the latest components, expressive APIs, adaptive scaffolds, and Styles API integration. Android Views may remain necessary in existing apps, but they should not be treated as the default path for new Material 3 implementations.
- Expressive layout system: Use an expressive layout scaffold to adapt screens across mobile, desktop, foldables, watches, XR, and other spatial form factors. Start from adaptive scaffolds/window size classes instead of fixed phone-first layouts.
- 8dp spacing system: Apply spacing tokens for margins, padding, and gaps so layouts and components can adapt programmatically to device type and density.
- New/updated expressive components: Lists, menus, search, and search app bars have refreshed expressive guidance, with Jetpack Compose as the primary implementation target.
- Watches and XR: Watches emphasize physics-based motion, arc text, and edge-hugging containers. XR emphasizes spatial panels and depth-based elevation.
Key differences from MD2:
- Tonal surfaces replace elevation shadows as the primary depth cue
- Dynamic color generates full schemes from a single seed color
- Fully rounded corners by default (not slightly rounded)
- Spring-based motion physics replace fixed easing curves for components
- 3 levels of user-controlled contrast (standard/medium/high)
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 8d ago First seen · 668 lines · 102 tokens per session scan A 5bbd6a903bb3
material-3 is a skill published in the GitHub repository mhrsdev/AI-Agent-Skills-Library (6 stars, last pushed 2mo ago), licensed MIT. It adds 102 tokens to every session and 8,079 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to material-3, differing in 0 lines, and is treated as a copy.
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shaft-accessibility-testing
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shaft-locator-design
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HTMLHero.skill
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HTMLPPT.skill
A workflow for turning a designed HTML page or existing web project into a multi-page presentation that runs in a browser. It keeps later slides consistent with the first page's visual style.