name	canvas-design
description	Create beautiful visual art as .png and .pdf files using design philosophy. Use when the user asks to create a poster, visual artwork, design piece, or static visual output via code generation. Requires the execute tool and Python with Pillow/reportlab.
license	Apache-2.0
metadata	{"author":"anthropic","version":"1.0","adapted-for":"loomic"}

Canvas Design Skill

These are instructions for creating visual artwork through code execution. Output .md (philosophy) + .pdf or .png (artwork) files.

Prerequisites

This skill requires:

The execute tool (sandbox code execution)
Python 3 with Pillow and reportlab installed
Font files available at the path in $FONT_DIR environment variable

Workflow

Complete in two steps:

Design Philosophy Creation (.md concept)
Express by creating it on a canvas via Python code execution (.pdf or .png)

Step 1: Design Philosophy

Create a VISUAL PHILOSOPHY (not layouts or templates) that will be interpreted through:

Form, space, color, composition
Images, graphics, shapes, patterns
Minimal text as visual accent

Name the movement (1-2 words): e.g., "Brutalist Joy" / "Chromatic Silence"

Articulate the philosophy (4-6 paragraphs) covering:

Space and form
Color and material
Scale and rhythm
Composition and balance
Visual hierarchy

Critical guidelines:

Avoid redundancy — each design aspect mentioned once
Emphasize craftsmanship REPEATEDLY: "meticulously crafted", "master-level execution"
Leave creative space for interpretation

Step 2: Canvas Creation

Use the execute tool to run Python code that generates the artwork.

IMPORTANT: Path Rules

虚拟路径 vs 真实路径：ls 和 read_file 工具使用虚拟路径（如 /skills/...），但 execute 工具运行在真实 shell 中。Python 代码里必须使用真实路径。

规则：

字体路径：永远用 os.environ["FONT_DIR"]，不要硬编码
输出文件：永远用相对路径保存（如 output.png），文件会在沙箱工作目录中
不要把 ls /skills/... 看到的虚拟路径用在 Python 代码里

Font Usage

字体通过 $FONT_DIR 环境变量访问（已在沙箱中预设）：

import os
font_dir = os.environ["FONT_DIR"]  # 必须用环境变量，不要硬编码路径
fonts = [f for f in os.listdir(font_dir) if f.endswith(".ttf")]

Load fonts with Pillow:

from PIL import ImageFont
font = ImageFont.truetype(os.path.join(font_dir, "WorkSans-Bold.ttf"), size=48)

Or with reportlab for PDF:

from reportlab.pdfbase import pdfmetrics
from reportlab.pdfbase.ttfonts import TTFont
pdfmetrics.registerFont(TTFont("WorkSans", os.path.join(font_dir, "WorkSans-Bold.ttf")))

Available Font Families

Serif: CrimsonPro (Regular/Bold/Italic), Gloock, IBMPlexSerif (Regular/Bold/Italic/BoldItalic), InstrumentSerif, Italiana, LibreBaskerville, Lora, YoungSerif

Sans-serif: ArsenalSC, BricolageGrotesque (Regular/Bold), InstrumentSans, Jura, Outfit, PoiretOne, SmoochSans, WorkSans

Monospace: DMMono, GeistMono (Regular/Bold), IBMPlexMono (Regular/Bold), JetBrainsMono, RedHatMono, Tektur

Display: BigShoulders (Regular/Bold), Boldonse, EricaOne, NationalPark, Silkscreen, PixelifySans

Handwritten: NothingYouCouldDo

Design Principles

Create museum/magazine-quality work — dense patterns, systematic shapes, clinical typography
Use a limited color palette (3-5 colors) that feels intentional and cohesive
Text is always minimal and visual-first — never paragraphs, only essential words
Use DIFFERENT fonts for different roles (display, body, labels, accents)
Nothing may overlap; all elements must stay within canvas boundaries with proper margins
Never cartoony or amateur — even for playful subjects, maintain sophistication

Code Execution Pattern

Write a complete Python script, then execute it.

所有路径必须用相对路径（多用户并发时绝对路径会冲突）：

1. write_file path="generate.py"       ← 相对路径，不要用 /tmp/xxx
2. execute: python3 generate.py
3. Output: img.save("output.png")      ← 相对路径
4. execute: pwd                        ← 获取当前工作目录的完整路径
5. persist_sandbox_file filePath="{pwd输出}/output.png"

禁止使用绝对路径写文件：

❌ write_file path="/tmp/script.py" — 多用户会覆盖
✅ write_file path="script.py" — 每个用户独立沙箱

Critical: In Python scripts:

Use os.environ["FONT_DIR"] for font paths（唯一允许的绝对路径）
Save output with RELATIVE paths (e.g., img.save("poster.png"))
Do NOT use /skills/... paths — those are virtual backend paths, not real filesystem

Refinement Pass

After generating the initial artwork, take a second pass:

Re-examine the code for alignment, spacing, color calibration
Refine rather than add — make existing composition more cohesive
The user expects "museum quality" — every detail matters

Step 3: Persist Output

After generating the artwork file, use the persist_sandbox_file tool to upload it to persistent storage. This gives the user a downloadable URL.

Important Notes

Always write complete Python scripts — do not use placeholders
Canvas size recommendation: 2400×3200 px for posters, 1920×1080 for landscapes
Save output as PNG (for raster) or PDF (for print-quality)
The subtle reference from the user's request should be woven into the art like a jazz musician quoting another song — perceptible to insiders but invisible to others

name	canvas-design
description	Create beautiful visual art as .png and .pdf files using design philosophy. Use when the user asks to create a poster, visual artwork, design piece, or static visual output via code generation. Requires the execute tool and Python with Pillow/reportlab.
license	Apache-2.0
metadata	{"author":"anthropic","version":"1.0","adapted-for":"loomic"}

Canvas Design Skill

These are instructions for creating visual artwork through code execution. Output .md (philosophy) + .pdf or .png (artwork) files.

Prerequisites

This skill requires:

The execute tool (sandbox code execution)
Python 3 with Pillow and reportlab installed
Font files available at the path in $FONT_DIR environment variable

Workflow

Complete in two steps:

Design Philosophy Creation (.md concept)
Express by creating it on a canvas via Python code execution (.pdf or .png)

Step 1: Design Philosophy

Create a VISUAL PHILOSOPHY (not layouts or templates) that will be interpreted through:

Form, space, color, composition
Images, graphics, shapes, patterns
Minimal text as visual accent

Name the movement (1-2 words): e.g., "Brutalist Joy" / "Chromatic Silence"

Articulate the philosophy (4-6 paragraphs) covering:

Space and form
Color and material
Scale and rhythm
Composition and balance
Visual hierarchy

Critical guidelines:

Avoid redundancy — each design aspect mentioned once
Emphasize craftsmanship REPEATEDLY: "meticulously crafted", "master-level execution"
Leave creative space for interpretation

Step 2: Canvas Creation

Use the execute tool to run Python code that generates the artwork.

IMPORTANT: Path Rules

规则：

字体路径：永远用 os.environ["FONT_DIR"]，不要硬编码
输出文件：永远用相对路径保存（如 output.png），文件会在沙箱工作目录中
不要把 ls /skills/... 看到的虚拟路径用在 Python 代码里

Font Usage

字体通过 $FONT_DIR 环境变量访问（已在沙箱中预设）：

import os
font_dir = os.environ["FONT_DIR"]  # 必须用环境变量，不要硬编码路径
fonts = [f for f in os.listdir(font_dir) if f.endswith(".ttf")]

Load fonts with Pillow:

from PIL import ImageFont
font = ImageFont.truetype(os.path.join(font_dir, "WorkSans-Bold.ttf"), size=48)

Or with reportlab for PDF:

from reportlab.pdfbase import pdfmetrics
from reportlab.pdfbase.ttfonts import TTFont
pdfmetrics.registerFont(TTFont("WorkSans", os.path.join(font_dir, "WorkSans-Bold.ttf")))

Available Font Families

Serif: CrimsonPro (Regular/Bold/Italic), Gloock, IBMPlexSerif (Regular/Bold/Italic/BoldItalic), InstrumentSerif, Italiana, LibreBaskerville, Lora, YoungSerif

Sans-serif: ArsenalSC, BricolageGrotesque (Regular/Bold), InstrumentSans, Jura, Outfit, PoiretOne, SmoochSans, WorkSans

Monospace: DMMono, GeistMono (Regular/Bold), IBMPlexMono (Regular/Bold), JetBrainsMono, RedHatMono, Tektur

Display: BigShoulders (Regular/Bold), Boldonse, EricaOne, NationalPark, Silkscreen, PixelifySans

Handwritten: NothingYouCouldDo

Design Principles

Create museum/magazine-quality work — dense patterns, systematic shapes, clinical typography
Use a limited color palette (3-5 colors) that feels intentional and cohesive
Text is always minimal and visual-first — never paragraphs, only essential words
Use DIFFERENT fonts for different roles (display, body, labels, accents)
Nothing may overlap; all elements must stay within canvas boundaries with proper margins
Never cartoony or amateur — even for playful subjects, maintain sophistication

Code Execution Pattern

Write a complete Python script, then execute it.

所有路径必须用相对路径（多用户并发时绝对路径会冲突）：

1. write_file path="generate.py"       ← 相对路径，不要用 /tmp/xxx
2. execute: python3 generate.py
3. Output: img.save("output.png")      ← 相对路径
4. execute: pwd                        ← 获取当前工作目录的完整路径
5. persist_sandbox_file filePath="{pwd输出}/output.png"

禁止使用绝对路径写文件：

❌ write_file path="/tmp/script.py" — 多用户会覆盖
✅ write_file path="script.py" — 每个用户独立沙箱

Critical: In Python scripts:

Use os.environ["FONT_DIR"] for font paths（唯一允许的绝对路径）
Save output with RELATIVE paths (e.g., img.save("poster.png"))
Do NOT use /skills/... paths — those are virtual backend paths, not real filesystem

Refinement Pass

After generating the initial artwork, take a second pass:

Re-examine the code for alignment, spacing, color calibration
Refine rather than add — make existing composition more cohesive
The user expects "museum quality" — every detail matters

Step 3: Persist Output

After generating the artwork file, use the persist_sandbox_file tool to upload it to persistent storage. This gives the user a downloadable URL.

Important Notes

Always write complete Python scripts — do not use placeholders
Canvas size recommendation: 2400×3200 px for posters, 1920×1080 for landscapes
Save output as PNG (for raster) or PDF (for print-quality)
The subtle reference from the user's request should be woven into the art like a jazz musician quoting another song — perceptible to insiders but invisible to others

canvas-design

Canvas Design Skill

Prerequisites

Workflow

Step 1: Design Philosophy

Step 2: Canvas Creation

IMPORTANT: Path Rules

Font Usage

Available Font Families

Design Principles

Code Execution Pattern

Refinement Pass

Step 3: Persist Output

Important Notes

Más de este repositorio

Más de este repositorio

Canvas Design Skill

Prerequisites

Workflow

Step 1: Design Philosophy

Step 2: Canvas Creation

IMPORTANT: Path Rules

Font Usage

Available Font Families

Design Principles

Code Execution Pattern

Refinement Pass

Step 3: Persist Output

Important Notes