luanmorenommaciel/agentspec/excalidraw-diagram
Claude Code and agent skill by luanmorenommaciel/agentspec
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Create Excalidraw diagram JSON files that make visual arguments. Use when the user wants to visualize workflows, architectures, or concepts.
Install
npx vskill@latest install luanmorenommaciel/agentspec/excalidraw-diagramSKILL.md
Excalidraw Diagram Creator
Generate .excalidraw JSON files that argue visually, not just display information.
Setup: Ensure the required dependencies are available in the environment. See the First-Time Setup section below (under Render & Validate) for the exact install commands.
Customization
All colors and brand-specific styles live in one file: references/color-palette.md. Read it before generating any diagram and use it as the single source of truth for all color choices — shape fills, strokes, text colors, evidence artifact backgrounds, everything.
To make this skill produce diagrams in your own brand style, edit color-palette.md. Everything else in this file is universal design methodology and Excalidraw best practices.
Core Philosophy
Diagrams should ARGUE, not DISPLAY.
A diagram isn't formatted text. It's a visual argument that shows relationships, causality, and flow that words alone can't express. The shape should BE the meaning.
The Isomorphism Test: If you removed all text, would the structure alone communicate the concept? If not, redesign.
The Education Test: Could someone learn something concrete from this diagram, or does it just label boxes? A good diagram teaches—it shows actual formats, real event names, concrete examples.
Depth Assessment (Do This First)
Before designing, determine what level of detail this diagram needs:
Simple/Conceptual Diagrams
Use abstract shapes when:
- Explaining a mental model or philosophy
- The audience doesn't need technical specifics
- The concept IS the abstraction (e.g., "separation of concerns")
Comprehensive/Technical Diagrams
Use concrete examples when:
- Diagramming a real system, protocol, or architecture
- The diagram will be used to teach or explain (e.g., YouTube video)
- The audience needs to understand what things actually look like
- You're showing how multiple technologies integrate
For technical diagrams, you MUST include evidence artifacts (see below).
Research Mandate (For Technical Diagrams)
Before drawing anything technical, research the actual specifications.
If you're diagramming a protocol, API, or framework:
- Look up the actual JSON/data formats
- Find the real event names, method names, or API endpoints
- Understand how the pieces actually connect
- Use real terminology, not generic placeholders
Bad: "Protocol" → "Frontend" Good: "AG-UI streams events (RUN_STARTED, STATE_DELTA, A2UI_UPDATE)" → "CopilotKit renders via createA2UIMessageRenderer()"
Research makes diagrams accurate AND educational.
Evidence Artifacts
Evidence artifacts are concrete examples that prove your diagram is accurate and help viewers learn. Include them in technical diagrams.
Types of evidence artifacts (choose what's relevant to your diagram):
| Artifact Type | When to Use | How to Render | |
|---|---|---|---|
| Code snippets | APIs, integrations, implementation details | Dark rectangle + syntax-colored text (see color palette for evidence artifact colors) | |
| Data/JSON examples | Data formats, schemas, payloads | Dark rectangle + colored text (see color palette) | |
| Event/step sequences | Protocols, workflows, lifecycles | Timeline pattern (line + dots + labels) | |
| UI mockups | Showing actual output/results | Nested rectangles mimicking real UI | |
| Real input content | Showing what goes IN to a system | Rectangle with sample content visible | |
| API/method names | Real function calls, endpoints | Use actual names from docs, not placeholders |
Example: For a diagram about a streaming protocol, you might show:
- The actual event names from the spec (not just "Event 1", "Event 2")
- A code snippet showing how to connect
- What the streamed data actually looks like
Example: For a diagram about a data transformation pipeline:
- Show sample input data (actual format, not "Input")
- Show sample output data (actual format, not "Output")
- Show intermediate states if relevant
The key principle: show what things actually look like, not just what they're called.
Multi-Zoom Architecture
Comprehensive diagrams operate at multiple zoom levels simultaneously. Think of it like a map that shows both the country borders AND the street names.
Level 1: Summary Flow
A simplified overview showing the full pipeline or process at a glance. Often placed at the top or bottom of the diagram.
Example: Input → Processing → Output or Client → Server → Database
Level 2: Section Boundaries
Labeled regions that group related components. These create visual "rooms" that help viewers understand what belongs together.
Example: Grouping by responsibility (Backend / Frontend), by phase (Setup / Execution / Cleanup), or by team (User / System / External)
Level 3: Detail Inside Sections
Evidence artifacts, code snippets, and concrete examples within each section. This is where the educational value lives.
Example: Inside a "Backend" section, you might show the actual API response format, not just a box labeled "API Response"
For comprehensive diagrams, aim to include all three levels. The summary gives context, the sections organize, and the details teach.
Bad vs Good
| Bad (Displaying) | Good (Arguing) | |
|---|---|---|
| 5 equal boxes with labels | Each concept has a shape that mirrors its behavior | |
| Card grid layout | Visual structure matches conceptual structure | |
| Icons decorating text | Shapes that ARE the meaning | |
| Same container for everything | Distinct visual vocabulary per concept | |
| Everything in a box | Free-floating text with selective containers |
Simple vs Comprehensive (Know Which You Need)
| Simple Diagram | Comprehensive Diagram | |
|---|---|---|
| Generic labels: "Input" → "Process" → "Output" | Specific: shows what the input/output actually looks like | |
| Named boxes: "API", "Database", "Client" | Named boxes + examples of actual requests/responses | |
| "Events" or "Messages" label | Timeline with real event/message names from the spec | |
| "UI" or "Dashboard" rectangle | Mockup showing actual UI elements and content | |
| ~30 seconds to explain | ~2-3 minutes of teaching content | |
| Viewer learns the structure | Viewer learns the structure AND the details |
Simple diagrams are fine for abstract concepts, quick overviews, or when the audience already knows the details. Comprehensive diagrams are needed for technical architectures, tutorials, educational content, or when you want the diagram itself to teach.
Container vs. Free-Floating Text
Not every piece of text needs a shape around it. Default to free-floating text. Add containers only when they serve a purpose.
| Use a Container When... | Use Free-Floating Text When... | |
|---|---|---|
| It's the focal point of a section | It's a label or description | |
| It needs visual grouping with other elements | It's supporting detail or metadata | |
| Arrows need to connect to it | It describes something nearby | |
| The shape itself carries meaning (decision diamond, etc.) | Typography alone creates sufficient hierarchy | |
| It represents a distinct "thing" in the system | It's a section title, subtitle, or annotation |
Typography as hierarchy: Use font size, weight, and color to create visual hierarchy without boxes. A 28px title doesn't need a rectangle around it.
The container test: For each boxed element, ask "Would this work as free-floating text?" If yes, remove the container.
Design Process (Do This BEFORE Generating JSON)
Step 0: Assess Depth Required
Before anything else, determine if this needs to be:
- Simple/Conceptual: Abstract shapes, labels, relationships (mental models, philosophies)
- Comprehensive/Technical: Concrete examples, code snippets, real data (systems, architectures, tutorials)
If comprehensive: Do research first. Look up actual specs, formats, event names, APIs.
Step 1: Understand Deeply
Read the content. For each concept, ask:
- What does this concept DO? (not what IS it)
- What relationships exist between concepts?
- What's the core transformation or flow?
- What would someone need to SEE to understand this? (not just read about)
Step 2: Map Concepts to Patterns
For each concept, find the visual pattern that mirrors its behavior:
| If the concept... | Use this pattern | |
|---|---|---|
| Spawns multiple outputs | Fan-out (radial arrows from center) | |
| Combines inputs into one | Convergence (funnel, arrows merging) | |
| Has hierarchy/nesting | Tree (lines + free-floating text) | |
| Is a sequence of steps | Timeline (line + dots + free-floating labels) | |
| Loops or improves continuously | Spiral/Cycle (arrow returning to start) | |
| Is an abstract state or context | Cloud (overlapping ellipses) | |
| Transforms input to output | Assembly line (before → process → after) | |
| Compares two things | Side-by-side (parallel with contrast) | |
| Separates into phases | Gap/Break (visual separation between sections) |
Step 3: Ensure Variety
For multi-concept diagrams: each major concept must use a different visual pattern. No uniform cards or grids.
Step 4: Sketch the Flow
Before JSON, mentally trace how the eye moves through the diagram. There should be a clear visual story.
Step 5: Generate JSON
Only now create the Excalidraw elements. See below for how to handle large diagrams.
Step 6: Render & Validate (MANDATORY)
After generating the JSON, you MUST run the render-view-fix loop until the diagram looks right. This is not optional — see the Render & Validate section below for the full process.
Large / Comprehensive Diagram Strategy
For comprehensive or technical diagrams, you MUST build the JSON one section at a time. Do NOT attempt to generate the entire file in a single pass. This is a hard constraint — Claude Code has a ~32,000 token output limit per response, and a comprehensive diagram easily exceeds that in one shot. Even if it didn't, generating everything at once leads to worse quality. Section-by-section is better in every way.
The Section-by-Section Workflow
Phase 1: Build each section
- Create the base file with the JSON wrapper (
type,version,appState,files) and the first section of elements. - Add one section per edit. Each section gets its own dedicated pass — take your time with it. Think carefully about the layout, spacing, and how this section connects to what's already there.
- Use descriptive string IDs (e.g.,
"trigger_rect","arrow_fan_left") so cross-section references are readable. - Namespace seeds by section (e.g., section 1 uses 100xxx, section 2 uses 200xxx) to avoid collisions.
- Update cross-section bindings as you go. When a new section's element needs to bind to an element from a previous section (e.g., an arrow connecting sections), edit the earlier element's
boundElementsarray at the same time.
Phase 2: Review the whole
After all sections are in place, read through the complete JSON and check:
- Are cross-section arrows bound correctly on both ends?
- Is the overall spacing balanced, or are some sections cramped while others have too much whitespace?
- Do IDs and bindings all reference elements that actually exist?
Fix any alignment or binding issues before rendering.
Phase 3: Render & validate
Now run the render-view-fix loop from the Render & Validate section. This is where you'll catch visual issues that aren't obvious from JSON — overlaps, clipping, imbalanced composition.
Section Boundaries
Plan your sections around natural visual groupings from the diagram plan. A typical large diagram might split into:
This is the start of the file. Read the full SKILL.md on GitHub.
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Source
- SKILL.md in the repositoryCheckingplugin/skills/excalidraw-diagram/SKILL.md
Popularity
- Repository stars
- 176
- Repository forks
- 87
- 7-day trend score
- 26
Stars and forks are for the whole GitHub repository, not this skill alone. Refreshed 16 May 2026, 5 months ago.
Works with
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