mermaid-to-image▌
zc277584121/marketing-skills · updated Apr 8, 2026
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Convert ```mermaid code blocks in Markdown (or other text) files into PNG images, and replace the code blocks with image references. Useful for platforms that don't render Mermaid natively (GitHub Pages/Jekyll, Dev.to, etc.).
Skill: Mermaid to Image
Convert ```mermaid code blocks in Markdown (or other text) files into PNG images, and replace the code blocks with image references. Useful for platforms that don't render Mermaid natively (GitHub Pages/Jekyll, Dev.to, etc.).
When to Use
- The user asks to convert Mermaid diagrams in a file to images
- The user wants to render specific Mermaid code blocks as PNG
- A publishing workflow requires static images instead of Mermaid code blocks
Workflow
Step 1: Identify target files
The user may specify:
- A single file:
convert mermaid blocks in docs/architecture.md - Multiple files:
convert mermaid in all files under docs/ - A specific code block:
convert the second mermaid block in README.md
Scan the target file(s) for ```mermaid code blocks. Report how many blocks were found and in which files before proceeding.
Step 2: Determine the image output directory
Check the project structure to find where images are typically stored:
# Look for common image directories
ls -d images/ img/ assets/ assets/images/ static/images/ docs/images/ 2>/dev/null
If a clear image directory exists (e.g., images/, assets/images/), use it. Create a subdirectory by topic if appropriate (e.g., images/<topic>/).
If no image directory is obvious or multiple candidates exist, ask the user:
Where should I save the rendered Mermaid images?
1. images/ (create new)
2. assets/images/
3. docs/figures/
4. Custom — enter a path
Tip: add "remember" to save this choice to CLAUDE.local.md.
If the user says "remember", save the choice to the project's CLAUDE.local.md:
## Mermaid Image Output
- **Image directory**: `<chosen-path>`
On subsequent runs, check CLAUDE.local.md for a ## Mermaid Image Output section and use it directly.
Step 3: Render each diagram to PNG
Use the mermaid.ink API to render diagrams. Run this Python snippet for each block:
import base64, urllib.request
def render_mermaid(code: str, output_path: str):
"""Render a Mermaid diagram to PNG via mermaid.ink API."""
encoded = base64.urlsafe_b64encode(code.encode()).decode()
url = f"https://mermaid.ink/img/{encoded}?bgColor=white"
req = urllib.request.Request(url, headers={"User-Agent": "Mozilla/5.0"})
resp = urllib.request.urlopen(req, timeout=30)
with open(output_path, "wb") as f:
f.write(resp.read())
Important: The User-Agent header is required — mermaid.ink returns 403 without it.
Naming convention
Use descriptive filenames based on the diagram content, not generic names:
- GOOD:
architecture-overview.png,data-flow.png,heartbeat-sequence.png - BAD:
mermaid-1.png,diagram.png,image1.png
Step 4: Replace code blocks with image references
Replace each ```mermaid ... ``` block with a Markdown image reference using a relative path from the file to the image:

If the project uses absolute URLs (e.g., GitHub Pages), use those instead:

Choose the link style that matches the project's existing image references. If unsure, use relative paths.
Step 5: Report results
After processing, summarize:
- How many diagrams were converted
- Where the images were saved
- Which files were modified
Edge Cases
- Large diagrams: mermaid.ink may time out on very complex diagrams. If a render fails, report the error and suggest the user simplify the diagram or try an alternative renderer.
- Multiple blocks in one file: process all blocks in order, give each a unique descriptive filename.
- Already-rendered blocks: if a mermaid block already has a corresponding image (commented out or adjacent), skip it or ask the user.
- Non-Markdown files: the same approach works for any text file containing mermaid code blocks (e.g.,
.rst,.txt).
How to use mermaid-to-image on Cursor
AI-first code editor with Composer
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 mermaid-to-image
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches mermaid-to-image from GitHub repository zc277584121/marketing-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate mermaid-to-image. Access the skill through slash commands (e.g., /mermaid-to-image) 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.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.7★★★★★54 reviews- ★★★★★Zara Khanna· Dec 16, 2024
Keeps context tight: mermaid-to-image is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Ganesh Mohane· Dec 12, 2024
We added mermaid-to-image from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Kaira Thompson· Dec 12, 2024
Registry listing for mermaid-to-image matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Zara Agarwal· Nov 7, 2024
mermaid-to-image is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Valentina Verma· Nov 7, 2024
mermaid-to-image fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Sakshi Patil· Nov 3, 2024
Useful defaults in mermaid-to-image — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Valentina Smith· Nov 3, 2024
mermaid-to-image reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Olivia Verma· Oct 26, 2024
Solid pick for teams standardizing on skills: mermaid-to-image is focused, and the summary matches what you get after install.
- ★★★★★Aditi Haddad· Oct 26, 2024
mermaid-to-image has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Chaitanya Patil· Oct 22, 2024
Registry listing for mermaid-to-image matched our evaluation — installs cleanly and behaves as described in the markdown.
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