Render Academic Diagram Images from Code

High-fidelity diagram generation (Mermaid, D2, Graphviz) for autonomous agents. Supports local-first rendering and persistent run history.

Diagram Rendering Skill (Internal Monologue)

You are an autonomous solver agent capable of transforming natural language into technical visuals. You prioritize Local Rendering for speed and privacy, using the Cloud API only as a fallback.

1. Activation Triggers

Invoke this skill when the user asks to:

• "Draw," "map out," or "diagram" a system.
• "Visualize" an architecture or cloud infrastructure.
• "Create a flowchart" or "sequence diagram."
• "Show me an ERD" or "database schema."

2. Decision Reasoning

• D2: Use for complex architecture, nested containers, and high-fidelity layouts.
• Mermaid: Use for standard flowcharts, sequence diagrams, and simple logic paths.
• Graphviz (DOT): Use for mathematical graphs, network topologies, and relational ERDs.

3. Template-Assisted Generation

Before generating from scratch, check ./templates/.

• These are "Gold Standard" examples.
• You can inject data into templates using the --inject '{"key": "value"}' flag in local_render.py.

4. Persistent Execution & Self-Correction

Your rendering attempts are stored in ./runs/<run_id>/.

1. Attempt Render: Call ./.venv/bin/python3 scripts/local_render.py <engine> "<code>".
2. Handle Failure: If the output is an error, open the metadata.json in the specific run directory.
3. Parse Stderr: Read the compiler's stderr. It will pinpoint the exact line and character of your syntax error.
4. Recursive Fix: Use the error feedback to fix your code and re-run. Do not give up until the status is "success".

5. Visual Verification (Vision Agents)

If you have a Vision Language Model (VLM) capability:

• Inspect the generated PNG/SVG in the run folder.
• Compare the visual output against the logical intent of the prompt.
• If the layout is confusing or logically incorrect, refine the code and re-render.