ray.so Code Snippet Image Generator

Generate beautiful code snippet images using ray.so and save them locally.

Requirements

• The user MUST provide the code snippet, either directly or by pointing to a file/selection in context
• MUST ask the user for ALL styling parameters before generating, presenting ALL available options
• MUST use agent-browser for screenshot capture (check availability first)

Workflow

Step 1: Verify agent-browser Availability

Before proceeding, verify that agent-browser is available:

which agent-browser

If agent-browser is not found in the PATH, inform the user that this skill requires agent-browser and cannot proceed without it.

Step 2: Fetch Available Options

Fetch the current themes and languages from ray.so's GitHub repository using curl:

# Fetch and parse available themes
curl -s "https://raw.githubusercontent.com/raycast/ray-so/main/app/(navigation)/(code)/store/themes.ts" | grep -oE 'id:\s*"[^"]+"' | sed 's/id:\s*"//;s/"//' | sort -u

# Fetch and parse available languages
curl -s "https://raw.githubusercontent.com/raycast/ray-so/main/app/(navigation)/(code)/util/languages.ts" | grep -oE '^[[:space:]]*"?[a-zA-Z0-9+#-]+"?\s*:\s*\{' | sed 's/[[:space:]]*"//g;s/".*//;s/:.*//' | sort -u

Step 3: Ask User for ALL Parameters

MUST use AskUserQuestion to ask for EVERY parameter, presenting ALL available options. Ask for parameters in this order:

3.1 Theme Selection

Present ALL available themes. In the question, list every theme fetched from step 2. Example:

Question: "Which theme would you like?"
Description: "Available themes: [list ALL themes from curl output]"
Options (pick 4 popular ones for quick select):
- breeze (default, purple gradient)
- midnight (cyan-blue)
- vercel (minimalist dark)
- sunset (warm orange)
Note: User can select "Other" to type any theme from the full list

3.2 Language Selection

Infer the language when possible. Skip this question if:

• The user explicitly specified a language
• The code comes from a file with a clear extension (e.g., .py → python, .js → javascript, .ts → typescript, .rs → rust, .go → go, etc.)
• The syntax is unmistakably identifiable (e.g., def/import → python, func/package → go, fn/let mut → rust)

Only ask this question if the language cannot be confidently inferred:

Question: "Which language for syntax highlighting?"
Description: "Available languages: [list ALL languages from curl output]"
Options:
- auto (auto-detect)
- javascript
- python
- typescript
Note: User can select "Other" to type any language from the full list

3.3 Dark/Light Mode

Question: "Dark or light mode?"
Options:
- Dark mode (default)
- Light mode

3.4 Background

Question: "Show the gradient background?"
Options:
- Yes, show background (default)
- No, transparent/minimal background

3.5 Padding

Question: "How much padding around the code?"
Options:
- 16 (compact)
- 32 (small)
- 64 (medium, default)
- 128 (large)

3.6 Line Numbers

Question: "Show line numbers?"
Options:
- No (default)
- Yes

3.7 Title

Question: "Add a title above the code? (e.g., filename)"
Options:
- No title (default)
- Yes, add title
If yes, ask for the title text.

Note: Do NOT ask about output path/filename. Save to the current working directory with a sensible filename (e.g., rayso-snippet.png, or based on the title if provided like fibonacci.png). Only use a different path if the user explicitly specifies one in their original request.

Step 4: Build the ray.so URL

CRITICAL: ALL parameters must be in the URL hash (after #), NOT in the query string.

Build the URL using shell commands:

# 1. Base64 encode the code
CODE_BASE64=$(echo -n 'YOUR_CODE_HERE' | base64)

# 2. URL encode the base64 string
CODE_ENCODED=$(python3 -c "import urllib.parse; print(urllib.parse.quote('$CODE_BASE64'))")

# 3. Build the URL with ALL parameters in the hash
# Format: https://ray.so/#param1=value1&param2=value2&code=ENCODED_CODE
# Do NOT include width parameter - let ray.so auto-size to fit content
URL="https://ray.so/#theme=THEME&padding=PADDING&background=BACKGROUND&darkMode=DARKMODE&language=LANGUAGE&code=${CODE_ENCODED}"

# Add optional parameters if needed:
# If lineNumbers: add "&lineNumbers=true" before &code=
# If title: add "&title=URL_ENCODED_TITLE" before &code=

URL Hash Parameters:

Parameter	Values	Default
theme	Any theme from list	breeze
padding	16, 32, 64, 128	64
background	true, false	true
darkMode	true, false	true
language	Any language from list, or "auto"	auto
lineNumbers	true, false	false
title	URL-encoded string	(none)
width	Number (pixels)	auto
code	Base64-encoded, then URL-encoded	(required)

Note on width: Do NOT include the width parameter unless you specifically need a fixed width. Without it, ray.so auto-sizes the frame to fit the code content, avoiding unnecessary empty space.

Example URL construction:

# For code: for i in range(23):\n    print(i)
# Theme: midnight, Padding: 64, Dark mode: true, Background: true, Language: python, Title: test.py

CODE='for i in range(23):
    print(i)'
CODE_BASE64=$(echo -n "$CODE" | base64)
CODE_ENCODED=$(python3 -c "import urllib.parse; print(urllib.parse.quote('$CODE_BASE64'))")
TITLE_ENCODED=$(python3 -c "import urllib.parse; print(urllib.parse.quote('test.py'))")
URL="https://ray.so/#theme=midnight&padding=64&background=true&darkMode=true&language=python&title=${TITLE_ENCODED}&code=${CODE_ENCODED}"
echo "$URL"

Step 5: Capture High-Quality Image with agent-browser

MUST use agent-browser (verified in Step 1). This approach uses the html-to-image library (same as ray.so's internal export) with high pixelRatio for crisp, sharp text rendering.

IMPORTANT: Always use a unique session name with --session to avoid stale session issues.

# Generate unique session name
SESSION="rayso-$(date +%s)"

# 1. Set viewport
agent-browser --session $SESSION set viewport 1400 900

# 2. Open the URL
agent-browser --session $SESSION open "$URL"

# 3. Wait for the page to fully render
agent-browser --session $SESSION wait --load networkidle
agent-browser --session $SESSION wait 3000

# 4. Load html-to-image library (same library ray.so uses internally)
agent-browser --session $SESSION eval 'new Promise((r,e)=>{const s=document.createElement("script");s.src="https://cdn.jsdelivr.net/npm/[email protected]/dist/html-to-image.js";s.onload=r;s.onerror=e;document.head.appendChild(s)})'

# 5. Capture at 4x resolution using html-to-image (produces crisp text)
agent-browser --session $SESSION eval 'htmlToImage.toPng(document.querySelector("#frame > div"),{pixelRatio:4,skipAutoScale:true})' > /tmp/rayso-dataurl-$SESSION.txt

# 6. Close the browser
agent-browser --session $SESSION close

# 7. Convert data URL to PNG file
DATAURL=$(cat /tmp/rayso-dataurl-$SESSION.txt | tr -d '"' | tr -d '\n')
echo "$DATAURL" | sed 's/data:image\/png;base64,//' | base64 -d > /path/to/output.png

# 8. Clean up temp file
rm /tmp/rayso-dataurl-$SESSION.txt

Critical notes:

• Uses html-to-image library which is what ray.so uses for its own export feature
• pixelRatio: 4 produces high-DPI images with crisp, sharp text (4x native resolution)
• The data URL is captured directly from the library, not from a screenshot
• No ImageMagick required - pure browser-based rendering at high resolution
• Output is correctly sized with no extra whitespace

Step 6: Confirm Output and STOP

Report the saved file location to the user. The task is complete - do not perform any additional checks, explorations, or verifications after the screenshot is saved.

Complete Example

User: "Create a code snippet image of this Python function"

def fibonacci(n):
    if n <= 1:
        return n
    return fibonacci(n-1) + fibonacci(n-2)

1. Check which agent-browser - confirmed available

2. Fetch themes and languages:

curl -s "https://raw.githubusercontent.com/raycast/ray-so/main/app/(navigation)/(code)/store/themes.ts" | grep -oE 'id:\s*"[^"]+"' | sed 's/id:\s*"//;s/"//' | sort -u

3. Ask user for parameters via AskUserQuestion:

   • Theme: user selects "midnight"
   • Language: inferred as python from def syntax - not asked
   • Dark mode: user selects "Dark mode"
   • Background: user selects "Yes"
   • Padding: user selects "64"
   • Line numbers: user selects "No"
   • Title: user selects "No title"

4. Build URL (all params in hash, no width for auto-sizing):

CODE='def fibonacci(n):
    if n <= 1:
        return n
    return fibonacci(n-1) + fibonacci(n-2)'
CODE_BASE64=$(echo -n "$CODE" | base64)
CODE_ENCODED=$(python3 -c "import urllib.parse; print(urllib.parse.quote('$CODE_BASE64')
how to use ray-so-code-snippet
CursorClaude CodeClineCodexWindsurfGooseGitHub CopilotVS CodeGemini CLIKiloOpencodeKimi CLIRoo ClineAiderContinueZedBolt.newLovablev0Replit AgentSweepSmol DeveloperDevinCavemanAmpAntigravity
How to use ray-so-code-snippet on Cursor
AI-first code editor with Composer
1
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
›Cursor installed and configured on your development machine
›Node.js version 16.0+ with npm package manager (verify with node --version)
›Active project directory or workspace where you want to add ray-so-code-snippet
2
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
$npx skills add https://github.com/intellectronica/agent-skills --skill ray-so-code-snippetcopy
The skills CLI fetches ray-so-code-snippet from GitHub repository intellectronica/agent-skills and configures it for Cursor.
3
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ── always included ────
│   • Amp
│   • Antigravity
│   • Cline
│   • Codex
│ ●Cursor(selected)
│   • Cursor
│   • Windsurf
4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/ray-so-code-snippet
Reload or restart Cursor to activate ray-so-code-snippet. Access the skill through slash commands (e.g., /ray-so-code-snippet) or your agent's skill management interface.
⚠
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
Additional Resources
›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?