graphviz-renderer
Render Graphviz DOT graphs to images with multiple layout algorithms
npx skills add a5c-ai/babysitter --skill graphviz-renderer --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Graphviz DOT Renderer Skill ## Overview Renders Graphviz DOT graph definitions to images supporting multiple layout algorithms for dependency visualization and large graph rendering. ## Capabilities - Render DOT graphs to PNG, SVG, PDF, PS formats - Multiple layout algorithms (dot, neato, fdp, sfdp, twopi, circo) - Large graph support with sfdp algorithm - Dependency visualization - Custom node and edge styling - Subgraph and cluster support ## Target Processes - microservices-decomposition - ddd-strategic-modeling - observability-implementation ## Input Schema ```json { "type": "object", "required": ["source"], "properties": { "source": { "type": "string", "description": "DOT graph definition" }, "outputFormat": { "type": "string", "enum": ["png", "svg", "pdf", "ps"], "default": "svg" }, "outputPath": { "type": "string", "description": "Output file path" }, "layout": { "type": "string", "enum": ["dot", "neato", "fdp", "sfdp", "twopi", "circo"], "default": "dot", "description": "Layout algorithm" }, "config": { "type": "object", "properties": { "dpi": { "type": "number", "default": 96 }, "rankdir": { "type": "string", "enum": ["TB", "BT", "LR", "RL"], "default": "TB" } } } } } ``
- Overview
- Capabilities
- Target Processes
- Input Schema
- Output Schema
- Usage Example
What does the graphviz-renderer skill do?
Render Graphviz DOT graphs to images with multiple layout algorithms
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill graphviz-renderer --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From a5c-ai/babysitter, a repository with 1,642 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.