generative-art-deployment
Deploy generative art projects for exhibition, web galleries, and print production. Covers rendering pipelines, resolution management, gallery hosting, and archival strategies for algorithmic artworks. Triggers on generative art deployment, art exhibition setup, or digital art publishing requests.
Best use case
generative-art-deployment is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Deploy generative art projects for exhibition, web galleries, and print production. Covers rendering pipelines, resolution management, gallery hosting, and archival strategies for algorithmic artworks. Triggers on generative art deployment, art exhibition setup, or digital art publishing requests.
Teams using generative-art-deployment should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/generative-art-deployment/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How generative-art-deployment Compares
| Feature / Agent | generative-art-deployment | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Deploy generative art projects for exhibition, web galleries, and print production. Covers rendering pipelines, resolution management, gallery hosting, and archival strategies for algorithmic artworks. Triggers on generative art deployment, art exhibition setup, or digital art publishing requests.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Generative Art Deployment
Move generative artworks from development to exhibition, print, and archive.
## Deployment Targets
| Target | Format | Resolution | Considerations |
|--------|--------|-----------|----------------|
| **Web gallery** | HTML/JS, WebGL | Screen (72-96 DPI) | Performance, loading time |
| **Physical print** | PNG/TIFF | 300 DPI minimum | Color profiles, bleed |
| **LED installation** | Video/WebGL | Panel-specific | Brightness, refresh rate |
| **NFT/on-chain** | PNG/SVG/HTML | Variable | File size, determinism |
| **Social media** | PNG/MP4 | Platform-specific | Compression, aspect ratio |
| **Archive** | Source + renders | Maximum | Reproducibility |
## Web Gallery Deployment
### Static Gallery Structure
```
gallery/
├── index.html # Gallery grid/navigation
├── works/
│ ├── piece-001/
│ │ ├── index.html # Full-screen viewer
│ │ ├── sketch.js # Live generative code
│ │ ├── thumbnail.png
│ │ └── metadata.json
│ └── piece-002/
│ └── ...
├── assets/
│ ├── style.css
│ └── gallery.js
└── catalog.json # Machine-readable catalog
```
### Work Metadata
```json
{
"title": "Recursive Bloom #47",
"artist": "Artist Name",
"date": "2026-03-20",
"medium": "Generative, p5.js",
"dimensions": "3840 × 2160",
"seed": 1742518400,
"parameters": {
"complexity": 0.7,
"palette": "autumn",
"iterations": 5000
},
"description": "An exploration of recursive growth patterns...",
"series": "Recursive Bloom",
"edition": "1/1",
"tags": ["recursion", "organic", "growth"]
}
```
### Performance Optimization
```javascript
// Render once, display static
function setup() {
const canvas = createCanvas(3840, 2160);
noLoop(); // Don't animate
}
function draw() {
randomSeed(SEED);
// ... generate artwork
saveCanvas('output', 'png');
}
// For interactive pieces: use requestAnimationFrame
// with quality degradation on low-end devices
function draw() {
if (frameRate() < 30) {
reduceComplexity();
}
}
```
## Print Production
### Resolution Pipeline
```python
def render_for_print(sketch_path: str, width_inches: float, height_inches: float, dpi: int = 300):
pixel_width = int(width_inches * dpi)
pixel_height = int(height_inches * dpi)
# Add bleed (0.125 inches on each side)
bleed = int(0.125 * dpi)
total_width = pixel_width + 2 * bleed
total_height = pixel_height + 2 * bleed
return {
"canvas_width": total_width,
"canvas_height": total_height,
"safe_area": {
"x": bleed, "y": bleed,
"width": pixel_width, "height": pixel_height,
},
"dpi": dpi,
"format": "TIFF", # Lossless for print
"color_profile": "sRGB", # Or Adobe RGB for wide gamut
}
```
### Color Management
```python
from PIL import Image, ImageCms
def convert_for_print(input_path: str, output_path: str):
img = Image.open(input_path)
srgb_profile = ImageCms.createProfile("sRGB")
# For fine art printing, embed the ICC profile
img.save(output_path, "TIFF", dpi=(300, 300), icc_profile=ImageCms.ImageCmsProfile(srgb_profile).tobytes())
```
### Print Sizes
| Size | Inches | Pixels (300 DPI) |
|------|--------|-------------------|
| A4 | 8.3 × 11.7 | 2490 × 3510 |
| A3 | 11.7 × 16.5 | 3510 × 4950 |
| A2 | 16.5 × 23.4 | 4950 × 7020 |
| 24×36 poster | 24 × 36 | 7200 × 10800 |
## Rendering Pipeline
### Batch Rendering
```python
import subprocess
from pathlib import Path
def batch_render(sketch: str, seeds: list[int], output_dir: str, width: int, height: int):
Path(output_dir).mkdir(parents=True, exist_ok=True)
for seed in seeds:
output = f"{output_dir}/render_{seed:08d}.png"
subprocess.run([
"node", sketch,
"--seed", str(seed),
"--width", str(width),
"--height", str(height),
"--output", output,
], check=True)
# Render a series
batch_render("sketch.js", seeds=range(1, 101), output_dir="renders/series-01", width=3840, height=2160)
```
### Deterministic Rendering
```javascript
// Ensure reproducibility: same seed = same output
function setup() {
const seed = parseInt(getURLParam('seed') || '42');
randomSeed(seed);
noiseSeed(seed);
// Record seed in metadata
document.title = `Piece #${seed}`;
}
```
## Exhibition Patterns
### Installation Checklist
- [ ] Hardware specs confirmed (GPU, resolution, orientation)
- [ ] Autostart on boot configured
- [ ] Crash recovery (watchdog/supervisor process)
- [ ] No UI chrome (cursor hidden, fullscreen)
- [ ] Network not required (all assets local)
- [ ] Power failure recovery tested
### Kiosk Mode Setup
```bash
# Linux kiosk mode
#!/usr/bin/env bash
xset -dpms # Disable power management
xset s off # Disable screen saver
unclutter -idle 0.5 & # Hide cursor
chromium-browser \
--kiosk \
--disable-infobars \
--disable-session-crashed-bubble \
--noerrdialogs \
file:///home/gallery/piece/index.html
```
## Archival Strategy
### Archive Package
```
archive/
├── README.md # How to run this piece
├── source/ # Original source code
│ ├── sketch.js
│ └── package.json
├── renders/ # High-res rendered outputs
│ ├── render_001.tiff
│ └── render_001.png
├── metadata.json # Full metadata including parameters
├── dependencies/ # Vendored dependencies
│ └── p5.min.js
└── documentation/
├── process.md # Artist statement, process notes
└── screenshots/ # Exhibition documentation
```
### Reproducibility Contract
```json
{
"runtime": "node 20.x + p5.js 1.9.x",
"seed": 42,
"canvas": "3840x2160",
"parameters": {},
"checksum": "sha256:abc123...",
"rendered": "2026-03-20T10:00:00Z"
}
```
## Anti-Patterns
- **No seed recorded** — Every generative piece must have a reproducible seed
- **Web-only renders** — Always produce high-res static renders for print/archive
- **Missing metadata** — Parameters, dimensions, and creation date are essential
- **No offline fallback** — Exhibition pieces must work without network
- **Lossy-only archives** — Keep lossless TIFF/PNG alongside compressed versions
- **Undocumented dependencies** — Vendor or lockfile all runtime dependenciesRelated Skills
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taxonomy-modeling-design
Phase 2 of the pentaphase structural-overhaul protocol. Classifies entities, standardizes attributes, establishes relationships, and designs the access framework. Use when the user invokes phase 2 of an overhaul, asks to "design the taxonomy" or "model the structure", or has completed a landscape audit and is ready to redesign. Consumes phase-1-landscape-report.md; produces phase-2-taxonomy-model.md.
systemic-ingestion-normalization
Phase 4 of the pentaphase structural-overhaul protocol. Purges redundancies, enriches and aligns legacy entities to the new schema, executes phased ingestion into the new environment, and audits integrity. Use when the user invokes phase 4 of an overhaul, asks to "migrate the data" or "ingest into the new system", or has a configured environment ready to accept legacy entities. Consumes phase-3-environment-spec.md; produces phase-4-ingestion-report.md.
system-environment-configuration
Phase 3 of the pentaphase structural-overhaul protocol. Translates the taxonomy model into objective technical criteria, evaluates candidate mechanisms or frameworks, instantiates the chosen architecture, and programs validation rules. Use when the user invokes phase 3 of an overhaul, asks to "select a system" or "configure the environment", or has a taxonomy model and is ready to choose technology. Consumes phase-2-taxonomy-model.md; produces phase-3-environment-spec.md.
pentaphase-orchestrator
Threads the full five-phase structural-overhaul protocol — landscape discovery, taxonomy design, environment configuration, systemic ingestion, governance evolution — for any substrate the user names. Use when the user requests a structural overhaul, system redesign, or end-to-end restructuring of a documentation system, asset registry, code monorepo, knowledge base, or operational workflow; or when they explicitly invoke the pentaphase methodology. Coordinates handoffs between phase-skills and seats validation gates between phases.
landscape-discovery-audit
Phase 1 of the pentaphase structural-overhaul protocol. Inventories assets, maps current flow, identifies friction, and defines value metrics for any substrate. Use when the user invokes phase 1 of an overhaul, requests a baseline audit, asks to "discover the landscape" of a system, or wants to understand current state before redesigning. Produces phase-1-landscape-report.md.
governance-evolution-protocol
Phase 5 of the pentaphase structural-overhaul protocol. Codifies operational protocols, onboards the ecosystem of participants, programs behavior monitoring, and establishes an iteration cadence so the substrate evolves rather than calcifies. Use when the user invokes phase 5 of an overhaul, asks to "establish governance" or "lock in the protocols", or has completed ingestion and is ready to declare the substrate operational. Consumes phase-4-ingestion-report.md; produces phase-5-governance-charter.md, which closes the protocol.
dimension-surfacing
Surfaces the parallel domain dimensions implicit in a dense or minimal prompt. Use when a user prompt is small on the surface but plainly implies multiple independent domains needing different expertise; when explicitly invoked by the coliseum-orchestrator skill as Phase 1; or when the user asks "what dimensions does this prompt encode" or "what axes does this break into." Produces a named dimension set where each dimension is independently executable and not a paraphrase of another.
coliseum-dispatch
Dispatches a composed set of assignment envelopes to domain-expert subagents in parallel, in a single message with multiple Agent tool calls. Enforces the no-pingpong gate via the pingpong-detector agent before any dispatch fires. Use when invoked by the coliseum-orchestrator as Phase 3; when envelopes are already composed and the next step is parallel execution; or when the user asks to "fan out" or "dispatch in parallel." Produces a dispatch log capturing what was sent, when, and where returns land.
assignment-composition
Wraps each surfaced dimension as a self-contained 9-section autonomous-work-assignment envelope — scope, context, success criteria, allowed tools, return format, handoff — all the recipient subagent needs to execute without coming back. Use when invoked by coliseum-orchestrator as Phase 2; when dimensions are named and the next step is to make each independently dispatchable; or when the user asks "compose this as an assignment." The no-pingpong gate validates each envelope before dispatch.