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 nimadorostkar/Claude-Skills-collection --skill generative-artgit clone --depth 1 https://github.com/nimadorostkar/Claude-Skills-collectionWrote 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/nimadorostkar/claude-skills-collection/generative-art)<a href="https://agentmods.dev/skills/nimadorostkar/claude-skills-collection/generative-art"><img src="https://agentmods.dev/badge/skills/nimadorostkar/claude-skills-collection/generative-art/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/nimadorostkar/claude-skills-collection/generative-art"><img src="https://agentmods.dev/badge/skills/nimadorostkar/claude-skills-collection/generative-art.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00036 | $0.01388 |
| Opus 5 | $0.00018 | $0.00694 |
| Sonnet 5 | $0.00007 | $0.00278 |
| Haiku 4.5 | $0.00004 | $0.00139 |
Grade A, and why
generative-art 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 12d 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 — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Generative Art
Purpose
Create visual work through code, where the interesting output comes from constrained randomness rather than unconstrained noise. Pure randomness produces mush; the craft is in the constraints.
When to Use
- Creating algorithmic or procedural visual work.
- Building a generative system that produces a family of related outputs.
- Data-driven visual work.
- Exploring composition computationally.
Capabilities
- Controlled randomness: distributions, noise fields, seeded reproducibility.
- Composition: grids, subdivision, packing, flow fields.
- Colour systems for generative palettes.
- Output at print resolution.
Inputs
- The visual idea or the underlying system.
- Constraints: palette, format, medium.
- Whether outputs must be reproducible (they should be).
Outputs
- A system producing a family of related, non-identical works.
- Seeded, reproducible output.
- Assets at the resolution the medium requires.
Workflow
- Seed the randomness — Always. An unseeded generative system produces something beautiful once and can never produce it again. This is the first thing to get right and the most commonly skipped.
- Constrain the space — Randomness within a structure. A grid whose cells vary, a palette whose members are sampled, an angle that jitters within a range. Unconstrained randomness is visual noise.
- Use noise, not uniform random, for anything spatial — Perlin or simplex noise produces coherent variation.
Math.random()per pixel produces static. - Build the palette as a system — Sample from a small set, or from a ramp. Per-element random colour destroys any coherence the composition had.
- Generate many, select few — The output of a generative system is a distribution. Produce a hundred, choose the ones that work, and adjust the constraints toward them.
- Render at the output resolution — Not scaled up afterwards. A generative composition designed at 800px and upscaled to print looks exactly like that.
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.
- 12d ago First seen · 127 lines · 36 tokens per session scan A f8bc9fcafac3
generative-art is a skill published in the GitHub repository nimadorostkar/Claude-Skills-collection (26 stars, last pushed 24d ago), licensed MIT. It adds 36 tokens to every session and 1,388 once invoked, about $0.0002 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 2026-08-30.
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