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 agentmods add skills/unicomai/wanwu/algorithmic-artnpx skills add UnicomAI/wanwu --skill algorithmic-artgit clone --depth 1 https://github.com/UnicomAI/wanwuWrote 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/unicomai/wanwu/algorithmic-art)<a href="https://agentmods.dev/skills/unicomai/wanwu/algorithmic-art"><img src="https://agentmods.dev/badge/skills/unicomai/wanwu/algorithmic-art.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 | $0.00062 | $0.04151 |
| Opus 5 | $0.00031 | $0.02076 |
| Sonnet 5 | $0.00012 | $0.00830 |
| Haiku 4.5 | $0.00006 | $0.00415 |
Grade A, and why
algorithmic-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 4d 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 algorithmic-art — 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 — 405 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Algorithmic philosophies are computational aesthetic movements that are then expressed through code. Output .md files (philosophy), .html files (interactive viewer), and .js files (generative algorithms).
This happens in two steps:
- Algorithmic Philosophy Creation (.md file)
- Express by creating p5.js generative art (.html + .js files)
First, undertake this task:
ALGORITHMIC PHILOSOPHY CREATION
To begin, create an ALGORITHMIC PHILOSOPHY (not static images or templates) that will be interpreted through:
- Computational processes, emergent behavior, mathematical beauty
- Seeded randomness, noise fields, organic systems
- Particles, flows, fields, forces
- Parametric variation and controlled chaos
THE CRITICAL UNDERSTANDING
- What is received: Some subtle input or instructions by the user to take into account, but use as a foundation; it should not constrain creative freedom.
- What is created: An algorithmic philosophy/generative aesthetic movement.
- What happens next: The same version receives the philosophy and EXPRESSES IT IN CODE - creating p5.js sketches that are 90% algorithmic generation, 10% essential parameters.
Consider this approach:
- Write a manifesto for a generative art movement
- The next phase involves writing the algorithm that brings it to life
The philosophy must emphasize: Algorithmic expression. Emergent behavior. Computational beauty. Seeded variation.
HOW TO GENERATE AN ALGORITHMIC PHILOSOPHY
Name the movement (1-2 words): "Organic Turbulence" / "Quantum Harmonics" / "Emergent Stillness"
Articulate the philosophy (4-6 paragraphs - concise but complete):
To capture the ALGORITHMIC essence, express how this philosophy manifests through:
- Computational processes and mathematical relationships?
- Noise functions and randomness patterns?
- Particle behaviors and field dynamics?
- Temporal evolution and system states?
- Parametric variation and emergent complexity?
CRITICAL GUIDELINES:
- Avoid redundancy: Each algorithmic aspect should be mentioned once. Avoid repeating concepts about noise theory, particle dynamics, or mathematical principles unless adding new depth.
- Emphasize craftsmanship REPEATEDLY: The philosophy MUST stress multiple times that the final algorithm should appear as though it took countless hours to develop, was refined with care, and comes from someone at the absolute top of their field. This framing is essential - repeat phrases like "meticulously crafted algorithm," "the product of deep computational expertise," "painstaking optimization," "master-level implementation."
- Leave creative space: Be specific about the algorithmic direction, but concise enough that the next Claude has room to make interpretive implementation choices at an extremely high level of craftsmanship.
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.
- 4d ago First seen · 405 lines · 62 tokens per session scan A 3bc4092c0980
algorithmic-art is a skill published in the GitHub repository UnicomAI/wanwu (2,455 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 62 tokens to every session and 4,151 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to algorithmic-art, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
prompt-writing
Create, refine, and optimize high-quality YAML prompts for AI assistants. Use when working with prompt templates, system prompts, agent prompts, or any prompt engineering tasks. Provides structure guidelines, template patterns, and quality standards for YAML-based prompts.
skill-creator
Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.
skill-creator
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
github-deep-research
Conduct multi-round deep research on any GitHub Repo. Use when users request comprehensive analysis, timeline reconstruction, competitive analysis, or in-depth investigation of GitHub. Produces structured markdown reports with executive summaries, chronological timelines, metrics analysis, and Mermaid diagrams.…
claude-to-deerflow
Interact with DeerFlow AI agent platform via its HTTP API. Use this skill when the user wants to send messages or questions to DeerFlow for research/analysis, start a DeerFlow conversation thread, check DeerFlow status or health, list available models/skills/agents in DeerFlow, manage DeerFlow memory, upload files to…
video-generation
Use this skill when the user requests to generate, create, or imagine videos. Supports structured prompts and reference image for guided generation.