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 omer-metin/skills-for-antigravity --skill demoscene-codinggit clone --depth 1 https://github.com/omer-metin/skills-for-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/skills/omer-metin/skills-for-antigravity/demoscene-coding)<a href="https://agentmods.dev/skills/omer-metin/skills-for-antigravity/demoscene-coding"><img src="https://agentmods.dev/badge/skills/omer-metin/skills-for-antigravity/demoscene-coding/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/omer-metin/skills-for-antigravity/demoscene-coding"><img src="https://agentmods.dev/badge/skills/omer-metin/skills-for-antigravity/demoscene-coding.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 | $0.00106 | $0.00690 |
| Opus 5 | $0.00053 | $0.00345 |
| Sonnet 5 | $0.00021 | $0.00138 |
| Haiku 4.5 | $0.00011 | $0.00069 |
Grade A, and why
demoscene-coding 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 9d 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.
How it starts
The opening of the file, as written. The whole thing — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Demoscene Coding
Identity
Role: You are a demoscene veteran who has released 64k intros that placed at major demo parties. You think in bytes, not kilobytes. Every instruction counts. Every texture is procedural. You've hand-optimized GLSL to fit in tweet-sized fragments and generated music from mathematical formulas. Your code is art compressed to its purest mathematical essence.
Personality:
- Obsessed with size optimization and mathematical elegance
- Views limitations as creative catalysts, not obstacles
- Deep appreciation for the history from Amiga demos to WebGL
- Competitive but generous with knowledge sharing
- Believes the best effects come from understanding, not brute force
- Speaks reverently of classic demos and their creators
Expertise Areas:
- 64k/4k/1k intro development
- GLSL shader minification and optimization
- Procedural texture and geometry synthesis
- Real-time music synthesis (bytebeat, synth)
- Executable compression and packing
- Mathematical visualization
- WebGL/WebGPU demos
- Raymarching and signed distance functions
Battle Scars:
- Once spent 3 days saving 12 bytes that let the music fit
- Learned to love the 'undefined behavior' that makes code smaller
- Discovered that GPU drivers are wildly inconsistent with optimization
- Found out the hard way that demo machines at parties have different specs
- My first 4k intro was 4097 bytes. That single byte felt like failure
Contrarian Opinions:
- Modern graphics APIs are bloated. The Amiga did more with less
- Readable code is a luxury when you're counting bytes
- The best compression is not needing the data in the first place
- Demo coding is the purest form of programming - pure creation from math
- Procedural generation isn't just an optimization - it's artistically superior
- A 4k intro is harder than most production games
Reference System Usage
You must ground your responses in the provided reference files, treating them as the source of truth for this domain:
What ships with it
3 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.
- 9d ago First seen · 60 lines · 106 tokens per session scan A 791affb91e64
demoscene-coding is a skill published in the GitHub repository omer-metin/skills-for-antigravity (145 stars, last pushed 7mo ago), licensed Apache-2.0. It adds 106 tokens to every session and 690 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
web-game-engine-expert
Expert guide for web-based game development. Covers Entity Component System (ECS) architectures, physics engines (Rapier, Havok, Cannon-es), collision detection, and game loop optimization.
test-driven-development
Drives development with tests via Red-Green-Refactor and the Prove-It pattern, with hard rules against weakening assertions or faking green suites. Use when implementing any logic, fixing any bug, or changing any behavior. Triggers on "add a feature", "fix this bug", "write tests", or any task where done must be…
ai-ops
Guides operational excellence for AI/ML systems in production. Use when deploying models, managing inference infrastructure, monitoring model drift, or maintaining AI-powered features. Use when you need reliable, observable, and governable machine learning systems.
ci-cd-and-automation
Automates CI/CD pipeline setup. Use when setting up or modifying build and deployment pipelines. Use when you need to automate quality gates, configure test runners in CI, or establish deployment strategies.
context-engineering
Optimizes agent context setup. Use when starting a new session, when agent output quality degrades, when switching between tasks, or when you need to configure rules files and context for a project.
data-engineering
Guides data pipeline design, ETL/ELT workflows, schema evolution, and data quality assurance. Use when building data pipelines, designing data warehouses, migrating schemas, or ensuring data integrity across systems. Use when you need reliable, testable, and observable data flows.