excalidraw-diagram

name	excalidraw-diagram
description	[Utilities] Use when the user wants to visualize workflows, architectures, or concepts as Excalidraw diagram JSON files.

Codex compatibility note:

• Invoke repository skills with $skill-name in Codex; this mirrored copy rewrites legacy Claude /skill-name references.
• Task tracker mandate: BEFORE executing any workflow or skill step, create/update task tracking for all steps and keep it synchronized as progress changes.
• User-question prompts mean to ask the user directly in Codex.
• Ignore Claude-specific mode-switch instructions when they appear.
• Strict execution contract: when a user explicitly invokes a skill, execute that skill protocol as written.
• Subagent authorization: when a skill is user-invoked or AI-detected and its protocol requires subagents, that skill activation authorizes use of the required spawn_agent subagent(s) for that task.
• Do not skip, reorder, or merge protocol steps unless the user explicitly approves the deviation first.
• For workflow skills, execute each listed child-skill step explicitly and report step-by-step evidence.
• If a required step/tool cannot run in this environment, stop and ask the user before adapting.

Codex Project-Reference Loading (No Hooks)

Codex uses static project-reference loading instead of runtime-injected project docs. When coding, planning, debugging, testing, or reviewing, open project docs explicitly using this routing.

Always read:

• docs/project-config.json (project-specific paths, commands, modules, and workflow/test settings)
• docs/project-reference/docs-index-reference.md (routes to the full docs/project-reference/* catalog)
• docs/project-reference/lessons.md (always-on guardrails and anti-patterns)

Missing/stale context route: If docs/project-config.json, the docs index, lessons.md, CLAUDE.md, AGENTS.md, or any task-required reference doc is missing or stale, auto-run $project-init or the narrow setup route ($project-config, $docs-init, $scan-all, $scan --target=<key>, $claude-md-init) before ordinary project-specific work. If Codex mirrors or AGENTS.md are missing/stale, ask the user to run $sync-codex; do not auto-run it.

Situation-based docs:

• Backend/CQRS/API/domain/entity changes: backend-patterns-reference.md, domain-entities-reference.md, project-structure-reference.md
• Frontend/UI/styling/design-system: frontend-patterns-reference.md, scss-styling-guide.md, design-system/README.md
• Spec authoring, docs/specs/ pathing, or TC format: feature-spec-reference.md, spec-system-reference.md, spec-principles.md
• Behavior/public-contract changes or spec-test-code sync: workflow-spec-test-code-cycle-reference.md plus the spec docs above
• Derived spec indexes/ERDs/reimplementation guides: spec-system-reference.md and source Feature Specs under docs/specs/
• Integration test implementation/review: integration-test-reference.md
• E2E test implementation/review: e2e-test-reference.md
• Code review/audit work: code-review-rules.md plus domain docs above based on changed files

Do not read all docs blindly. Start from docs-index-reference.md, then open only relevant files for the task.

[BLOCKING] Execute skill steps in declared order. NEVER skip, reorder, or merge steps without explicit user approval. [BLOCKING] Before each step or sub-skill call, update task tracking: set in_progress when step starts, set completed when step ends. [BLOCKING] Every completed/skipped step MUST include brief evidence or explicit skip reason. [BLOCKING] If Task tools are unavailable, create and maintain an equivalent step-by-step plan tracker with the same status transitions.

Quick Summary

Goal: Produce .excalidraw JSON diagrams that visualize workflows, architectures, or concepts and visually ARGUE a concept — where the structure itself carries the meaning and (for technical diagrams) concrete evidence artifacts teach — validated through the render-view-fix loop until the rendered image matches the conceptual design.

Summary:

• Diagrams must ARGUE not DISPLAY: pass the Isomorphism Test (structure alone communicates the concept) — map each major concept to a DIFFERENT visual pattern (fan-out, convergence, timeline, tree, cycle) and default text to free-floating (<30% inside containers).
• Assess depth FIRST: simple/conceptual (abstract shapes) vs comprehensive/technical — technical diagrams MUST research real specs (actual event names, JSON formats, API/method names) and embed evidence artifacts across the three zoom levels (summary flow + section boundaries + concrete detail).
• Build comprehensive JSON section-by-section, never in one pass (hard ~32k-token output limit) — use descriptive string IDs, namespace seeds per section (100xxx, 200xxx), and update cross-section boundElements as you go; pull all colors from references/color-palette.md and never invent new ones.
• The Render & Validate loop is MANDATORY, not a final check: render to PNG via render_excalidraw.py, Read the image, audit against your planned vision plus visual defects (clipping, overlaps, arrows crossing shapes), fix, and re-render — typically 2-4 iterations until it matches the conceptual design.

Workflow:

1. Detect — classify request scope and target artifacts.
2. Execute — apply required steps with evidence-backed actions.
3. Verify — confirm constraints, output quality, and completion evidence.

Key Rules:

• MUST ATTENTION keep claims evidence-based (file:line) with confidence >80% to act.
• MUST ATTENTION keep task tracking updated as each step starts/completes.
• NEVER skip mandatory workflow or skill gates.

Customization

All colors and brand-specific styles live in one file: references/color-palette.md. Read it before generating any diagram; use it as single source of truth for all color choices — shape fills, strokes, text colors, evidence artifact backgrounds, everything.

To produce diagrams in your own brand style, edit color-palette.md. Everything else in this file is universal design methodology and Excalidraw best practices.

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Core Philosophy

Diagrams should ARGUE, not DISPLAY.

A diagram isn't formatted text. It's a visual argument showing 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.

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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 ATTENTION include evidence artifacts (see below).

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Research Mandate (For Technical Diagrams)

Before drawing anything technical, research the actual specifications.

If you're diagramming a protocol, API, or framework:

1. Look up the actual JSON/data formats
2. Find the real event names, method names, or API endpoints
3. Understand how the pieces actually connect
4. 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.

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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.

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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.

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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.

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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 ATTENTION 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.

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Large / Comprehensive Diagram Strategy

For comprehensive or technical diagrams, you MUST ATTENTION 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

1. Create the base file with the JSON wrapper (type, version, appState, files) and the first section of elements.
2. 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.
3. Use descriptive string IDs (e.g., "trigger_rect", "arrow_fan_left") so cross-section references are readable.
4. Namespace seeds by section (e.g., section 1 uses 100xxx, section 2 uses 200xxx) to avoid collisions.
5. 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 boundElements array 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:

• Section 1: Entry point / trigger
• Section 2: First decision or routing
• Section 3: Main content (hero section — may be the largest single section)
• Section 4-N: Remaining phases, outputs, etc.

Each section should be independently understandable: its elements, internal arrows, and any cross-references to adjacent sections.

What NOT to Do

• Don't generate the entire diagram in one response. You will hit the output token limit and produce truncated, broken JSON. Even if the diagram is small enough to fit, splitting into sections produces better results.
• Don't use a coding agent to generate the JSON. The agent won't have sufficient context about the skill's rules, and the coordination overhead negates any benefit.
• Don't write a Python generator script. The templating and coordinate math seem helpful but introduce a layer of indirection that makes debugging harder. Hand-crafted JSON with descriptive IDs is more maintainable.

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Visual Pattern Library

Fan-Out (One-to-Many)

Central element with arrows radiating to multiple targets. Use for: sources, PRDs, root causes, central hubs.

        ○
       ↗
  □ → ○
       ↘
        ○

Convergence (Many-to-One)

Multiple inputs merging through arrows to single output. Use for: aggregation, funnels, synthesis.

  ○ ↘
  ○ → □
  ○ ↗

Tree (Hierarchy)

Parent-child branching with connecting lines and free-floating text (no boxes needed). Use for: file systems, org charts, taxonomies.

  label
  ├── label
  │   ├── label
  │   └── label
  └── label

Use line elements for the trunk and branches, free-floating text for labels.

Spiral/Cycle (Continuous Loop)

Elements in sequence with arrow returning to start. Use for: feedback loops, iterative processes, evolution.

  □ → □
  ↑     ↓
  □ ← □

Cloud (Abstract State)

Overlapping ellipses with varied sizes. Use for: context, memory, conversations, mental states.

Assembly Line (Transformation)

Input → Process Box → Output with clear before/after. Use for: transformations, processing, conversion.

  ○○○ → [PROCESS] → □□□
  chaos              order

Side-by-Side (Comparison)

Two parallel structures with visual contrast. Use for: before/after, options, trade-offs.

Gap/Break (Separation)

Visual whitespace or barrier between sections. Use for: phase changes, context resets, boundaries.

Lines as Structure

Use lines (type: line, not arrows) as primary structural elements instead of boxes:

• Timelines: Vertical or horizontal line with small dots (10-20px ellipses) at intervals, free-floating labels beside each dot
• Tree structures: Vertical trunk line + horizontal branch lines, with free-floating text labels (no boxes needed)
• Dividers: Thin dashed lines to separate sections
• Flow spines: A central line that elements relate to, rather than connecting boxes

Timeline:           Tree:
  ●─── Label 1        │
  │                   ├── item
  ●─── Label 2        │   ├── sub
  │                   │   └── sub
  ●─── Label 3        └── item

Lines + free-floating text often creates a cleaner result than boxes + contained text.

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Shape Meaning

Choose shape based on what it represents—or use no shape at all:

Concept Type	Shape	Why
Labels, descriptions, details	none (free-floating text)	Typography creates hierarchy
Section titles, annotations	none (free-floating text)	Font size/weight is enough
Markers on a timeline	small ellipse (10-20px)	Visual anchor, not container
Start, trigger, input	ellipse	Soft, origin-like
End, output, result	ellipse	Completion, destination
Decision, condition	diamond	Classic decision symbol
Process, action, step	rectangle	Contained action
Abstract state, context	overlapping ellipse	Fuzzy, cloud-like
Hierarchy node	lines + text (no boxes)	Structure through lines

Rule: Default to no container. Add shapes only when they carry meaning. Aim for <30% of text elements to be inside containers.

---

Color as Meaning

Colors encode information, not decoration. Every color choice should come from references/color-palette.md — the semantic shape colors, text hierarchy colors, and evidence artifact colors are all defined there.

Key principles:

• Each semantic purpose (start, end, decision, AI, error, etc.) has a specific fill/stroke pair
• Free-floating text uses color for hierarchy (titles, subtitles, details — each at a different level)
• Evidence artifacts (code snippets, JSON examples) use their own dark background + colored text scheme
• Always pair a darker stroke with a lighter fill for contrast

Do not invent new colors. If a concept doesn't fit an existing semantic category, use Primary/Neutral or Secondary.

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Modern Aesthetics

For clean, professional diagrams:

Roughness

• roughness: 0 — Clean, crisp edges. Use for modern/technical diagrams.
• roughness: 1 — Hand-drawn, organic feel. Use for brainstorming/informal diagrams.

Default to 0 for most professional use cases.

Stroke Width

• strokeWidth: 1 — Thin, elegant. Good for lines, dividers, subtle connections.
• strokeWidth: 2 — Standard. Good for shapes and primary arrows.
• strokeWidth: 3 — Bold. Use sparingly for emphasis (main flow line, key connections).

Opacity

Always use opacity: 100 for all elements. Use color, size, and stroke width to create hierarchy instead of transparency.

Small Markers Instead of Shapes

Instead of full shapes, use small dots (10-20px ellipses) as:

• Timeline markers
• Bullet points
• Connection nodes
• Visual anchors for free-floating text

---

Layout Principles

Hierarchy Through Scale

• Hero: 300×150 - visual anchor, most important
• Primary: 180×90
• Secondary: 120×60
• Small: 60×40

Whitespace = Importance

The most important element has the most empty space around it (200px+).

Flow Direction

Guide the eye: typically left→right or top→bottom for sequences, radial for hub-and-spoke.

Connections Required

Position alone doesn't show relationships. If A relates to B, there must be an arrow.

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Arrow Routing (Preventing Overlap)

Straight arrows are the default, but they cause overlaps in dense diagrams. When arrows cross through other elements, use curved or elbowed routing to clear obstacles. This is especially critical in ERDs, architecture diagrams, and any layout with many connections.

Strategy Selection

1. Straight — Only for direct neighbors with a clear, unobstructed path between them. Use 2 points: [[0,0], [dx, dy]].

2. Curved (primary overlap fix) — Use when a straight arrow would cross through other elements. Add "roundness": {"type": 2} and a 3-point arc: [[0,0], [midX, -arcHeight], [endX, endY]]. The midpoint's Y offset creates a smooth parabola that clears obstacles. Use 15-30px offset for short arrows, 30-50px for long ones. Negative Y = arc above, positive Y = arc below.

3. Elbowed — Use when curved isn't enough (same-row entities with many obstacles between). Set "elbowed": true with a 4-point right-angle path: [[0,0], [0, -offset], [targetX, -offset], [targetX, 0]]. Add "fixedSegments" to pin the horizontal segment.

Binding Modes

Use the modern binding format with mode and fixedPoint (not the legacy focus/gap format):

• "orbit" — Arrow attaches to shape's outer edge. Best for most connections. fixedPoint: [xRatio, yRatio] where [0,0.5] = left, [1,0.5] = right, [0.5,0] = top, [0.5,1] = bottom.
• "inside" — Arrow starts/ends from inside the shape. Use for vertical drops within a column.

See references/element-templates.md for full JSON templates of each arrow type.

When to Audit Arrows

During the render-view-fix loop, specifically check:

• Do any arrows cross through shapes they shouldn't?
• Are parallel arrows distinguishable (not overlapping each other)?
• For fan-out patterns (one entity with 5+ outgoing arrows), consider reducing to essential relationships or using varied arc heights to separate paths.

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Text Rules

CRITICAL: The JSON text property contains ONLY readable words.

{
    "id": "myElement1",
    "text": "Start",
    "originalText": "Start"
}

Settings: fontSize: 16, fontFamily: 3, textAlign: "center", verticalAlign: "middle"

---

JSON Structure

{
  "type": "excalidraw",
  "version": 2,
  "source": "https://excalidraw.com",
  "elements": [...],
  "appState": {
    "viewBackgroundColor": "#ffffff",
    "gridSize": 20
  },
  "files": {}
}

Element Templates

See references/element-templates.md for copy-paste JSON templates for each element type (text, line, dot, rectangle, arrow). Pull colors from references/color-palette.md based on each element's semantic purpose.

---

Render & Validate (MANDATORY)

You cannot judge a diagram from JSON alone. After generating or editing the Excalidraw JSON, you MUST ATTENTION render it to PNG, view the image, and fix what you see — in a loop until it's right. This is a core part of the workflow, not a final check.

How to Render

cd .claude/skills/excalidraw-diagram/references && uv run python render_excalidraw.py <path-to-file.excalidraw>

This outputs a PNG next to the .excalidraw file. Then use the Read tool on the PNG to actually view it.

The Loop

After generating the initial JSON, run this cycle:

1. Render & View — Run the render script, then Read the PNG.

2. Audit against your original vision — Before looking for bugs, compare the rendered result to what you designed in Steps 1-4. Ask:

• Does the visual structure match the conceptual structure you planned?
• Does each section use the pattern you intended (fan-out, convergence, timeline, etc.)?
• Does the eye flow through the diagram in the order you designed?
• Is the visual hierarchy correct — hero elements dominant, supporting elements smaller?
• For technical diagrams: are the evidence artifacts (code snippets, data examples) readable and properly placed?

3. Check for visual defects:

• Text clipped by or overflowing its container
• Text or shapes overlapping other elements
• Arrows crossing through elements instead of routing around them
• Arrows landing on the wrong element or pointing into empty space
• Labels floating ambiguously (not clearly anchored to what they describe)
• Uneven spacing between elements that should be evenly spaced
• Sections with too much whitespace next to sections that are too cramped
• Text too small to read at the rendered size
• Overall composition feels lopsided or unbalanced

4. Fix — Edit the JSON to address everything you found. Common fixes:

• Widen containers when text is clipped
• Adjust x/y coordinates to fix spacing and alignment
• Convert overlapping straight arrows to curved (roundness: {"type": 2} + 3-point arc) or elbowed (elbowed: true + 4-point path) — see Arrow Routing section
• Reposition labels closer to the element they describe
• Resize elements to rebalance visual weight across sections

5. Re-render & re-view — Run the render script again and Read the new PNG.

6. Repeat — Keep cycling until the diagram passes both the vision check (Step 2) and the defect check (Step 3). Typically takes 2-4 iterations. Don't stop after one pass just because no critical bugs — if the composition could be better, improve it.

When to Stop

The loop is done when:

• The rendered diagram matches the conceptual design from your planning steps
• No text is clipped, overlapping, or unreadable
• Arrows route cleanly and connect to the right elements
• Spacing is consistent and the composition is balanced
• You'd be comfortable showing it to someone without caveats

First-Time Setup

If the render script hasn't been set up yet:

cd .claude/skills/excalidraw-diagram/references
uv sync
uv run playwright install chromium

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Quality Checklist

Depth & Evidence (Check First for Technical Diagrams)

1. Research done: Did you look up actual specs, formats, event names?
2. Evidence artifacts: Are there code snippets, JSON examples, or real data?
3. Multi-zoom: Does it have summary flow + section boundaries + detail?
4. Concrete over abstract: Real content shown, not just labeled boxes?
5. Educational value: Could someone learn something concrete from this?

Conceptual

6. Isomorphism: Does each visual structure mirror its concept's behavior?
7. Argument: Does the diagram SHOW something text alone couldn't?
8. Variety: Does each major concept use a different visual pattern?
9. No uniform containers: Avoided card grids and equal boxes?

Container Discipline

10. Minimal containers: Could any boxed element work as free-floating text instead?
11. Lines as structure: Are tree/timeline patterns using lines + text rather than boxes?
12. Typography hierarchy: Are font size and color creating visual hierarchy (reducing need for boxes)?

Structural

13. Connections: Every relationship has an arrow or line
14. Flow: Clear visual path for the eye to follow
15. Hierarchy: Important elements are larger/more isolated
16. Arrow routing: No arrows crossing through shapes — use curved/elbowed routing where needed

Technical

17. Text clean: text contains only readable words
18. Font: fontFamily: 3
19. Roughness: roughness: 0 for clean/modern (unless hand-drawn style requested)
20. Opacity: opacity: 100 for all elements (no transparency)
21. Container ratio: <30% of text elements should be inside containers

Visual Validation (Render Required)

22. Rendered to PNG: Diagram has been rendered and visually inspected
23. No text overflow: All text fits within its container
24. No overlapping elements: Shapes and text don't overlap unintentionally
25. Even spacing: Similar elements have consistent spacing
26. Arrows land correctly: Arrows connect to intended elements without crossing others
27. Readable at export size: Text is legible in the rendered PNG
28. Balanced composition: No large empty voids or overcrowded regions

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Excalidraw Diagram Creator

Be skeptical. Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence percentages (Idea should be more than 80%).

Generate .excalidraw JSON files that argue visually, not just display information.

Output directory: Save generated .excalidraw files to docs/diagrams/. Create the directory if it doesn't exist. Use kebab-case filenames that describe the diagram's subject (e.g., docs/diagrams/cqrs-command-flow.excalidraw, docs/diagrams/cross-service-messaging.excalidraw). If the user specifies a different path, use that instead.

Setup: If the user asks you to set up this skill (renderer, dependencies, etc.), see README.md for instructions.

AI Mistake Prevention — Failure modes to avoid on every task:

Re-read files after context changes. Context compaction, resume, or long-running work can make memory stale; verify current files before acting. Verify generated content against source evidence. AI hallucinates APIs, names, claims, and document facts. Check the relevant source before documenting or referencing. Check downstream references before deleting or renaming. Removing an artifact can stale docs, generated mirrors, configs, and callers; map references first. Trace the full impact chain after edits. Changing a definition can miss derived outputs and consumers. Follow the affected chain before declaring done. Verify ALL affected outputs, not just the first. One green check is not all green checks; validate every output surface the change can affect. Assume existing values are intentional — ask WHY before changing. Before changing a constant, limit, flag, wording, or pattern, read nearby context and history. Surface ambiguity before acting — don't pick silently. Multiple valid interpretations require an explicit question or stated assumption with risk. Keep shared guidance role-relevant. Universal guidance must help every receiving skill or agent; code-specific obligations belong only in code-specific protocols.

Critical Thinking Mindset — Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence >80% to act. Anti-hallucination: Never present guess as fact — cite sources for every claim, admit uncertainty freely, self-check output for errors, cross-reference independently, stay skeptical of own confidence — certainty without evidence root of all hallucination.

MUST ATTENTION apply critical + sequential thinking — every claim needs appropriate traced evidence (file:line for repo/code claims; source URL or artifact section for research, product, content, and docs claims); confidence >80% to act, <60% DO NOT recommend. Anti-hallucination: never present guess as fact, admit uncertainty freely, cross-reference independently, stay skeptical of own confidence.

MUST ATTENTION apply AI mistake prevention — verify generated content against evidence, trace downstream references before deleting or renaming, verify all affected outputs, re-read files after context loss, and surface ambiguity before acting.

Prompt-Enhance Closing Anchors

IMPORTANT MUST ATTENTION follow declared step order for this skill; NEVER skip, reorder, or merge steps without explicit user approval IMPORTANT MUST ATTENTION for every step/sub-skill call: set in_progress before execution, set completed after execution IMPORTANT MUST ATTENTION every skipped step MUST include explicit reason; every completed step MUST include concise evidence IMPORTANT MUST ATTENTION if Task tools unavailable, maintain an equivalent step-by-step plan tracker with synchronized statuses

Closing Reminders

IMPORTANT MUST ATTENTION Goal: Produce .excalidraw JSON diagrams that visually ARGUE a concept — where the structure itself carries the meaning and (for technical diagrams) concrete evidence artifacts teach — validated through the render-view-fix loop until the rendered image matches the conceptual design.

Protocols in force (concise digest of the SYNC/shared blocks this skill carries) — MUST ATTENTION honor each canonical body:

• AI Mistake Prevention: verify generated content against evidence, trace downstream references, verify all affected outputs, re-read after context loss, surface ambiguity.
• Critical Thinking: Apply critical + sequential thinking; every claim needs traced proof, confidence >80% to act.

IMPORTANT MUST ATTENTION diagrams must ARGUE not DISPLAY — pass the Isomorphism Test (structure alone communicates the concept); NEVER ship a card grid or equal-box layout — map each concept to a DIFFERENT visual pattern (fan-out, convergence, timeline, tree, cycle) — why: uniform containers display labels, they do not argue meaning IMPORTANT MUST ATTENTION the Render & Validate loop is MANDATORY — NEVER ship JSON without rendering to PNG via render_excalidraw.py, Reading the image, and fixing visual defects (clipping, overlaps, arrows crossing shapes) across 2-4 iterations — why: you cannot judge a diagram from JSON alone IMPORTANT MUST ATTENTION cite file:line evidence for every claim with confidence >80% to act (<60% DO NOT recommend) — why: speculation produces wrong diagrams that pass silent review IMPORTANT MUST ATTENTION break work into small todo tasks using task tracking BEFORE starting, keep exactly one in_progress, mark completed immediately, add a final review todo — why: context loss without a task list silently drops findings IMPORTANT MUST ATTENTION assess depth FIRST (simple/conceptual vs comprehensive/technical) — technical diagrams MUST research real specs (actual event names, JSON formats, API/method names), search 3+ existing patterns before inventing, and embed evidence artifacts at all three zoom levels — why: closest example ≠ matching preconditions, and generic placeholders teach nothing IMPORTANT MUST ATTENTION build comprehensive JSON section-by-section, NEVER in one pass — pass it through the ~32k-token output limit, use descriptive string IDs, namespace seeds per section (100xxx, 200xxx), update cross-section boundElements as you go — why: a single pass produces truncated, broken JSON IMPORTANT MUST ATTENTION pull ALL colors from references/color-palette.md and NEVER invent new ones — use Primary/Neutral or Secondary when no semantic category fits — why: color encodes meaning, invented colors break the encoding IMPORTANT MUST ATTENTION default text to free-floating (<30% inside containers) — for each boxed element ask "Would this work as free-floating text?" and remove the container if yes — why: typography creates hierarchy without boxes

Anti-Rationalization:

Evasion	Rebuttal
"JSON looks correct, skip rendering"	You cannot judge a diagram from JSON — render to PNG, Read it, fix what you see.
"One render pass, no critical bugs, done"	Vision check ≠ defect check. Keep cycling until composition is balanced (2-4 passes).
"Generate the whole diagram in one response"	You will hit the ~32k output limit and produce broken JSON. Build section-by-section.
"Generic labels are enough for this"	Technical diagrams must show actual specs/formats/names — placeholders teach nothing.
"This color looks better"	Pull only from references/color-palette.md; invented colors break the meaning code.
"Box everything for consistency"	Default to free-floating text (<30% boxed); typography is the hierarchy, not borders.

IMPORTANT MUST ATTENTION diagrams must ARGUE not DISPLAY — Render & Validate loop is MANDATORY (render → Read PNG → fix → repeat) IMPORTANT MUST ATTENTION assess depth FIRST, research real specs for technical diagrams, build comprehensive JSON section-by-section IMPORTANT MUST ATTENTION cite file:line evidence (confidence >80% to act); break work into small task tracking todos before starting and add a final review todo

[IMPORTANT] Analyze how big the task is and break it into many small todo tasks systematically before starting — this is very important.

Hookless Prompt Protocol Mirror (Auto-Synced)

Source: .claude/.ck.json + .claude/skills/shared/sync-inline-versions.md (:full blocks) + .claude/scripts/lib/hookless-prompt-protocol.cjs

[WORKFLOW-EXECUTION-PROTOCOL] [BLOCKING] Workflow Execution Protocol — MANDATORY IMPORTANT MUST CRITICAL. Do not skip for any reason.

Generic portability boundary: Reusable skills and protocol text stay project-neutral; project-specific conventions are discovered from docs/project-config.json and docs/project-reference/. Apply shared AI-SDD from shared/sdd-artifact-contract.md. Read docs/project-config.json and docs/project-reference/docs-index-reference.md, then open the project reference docs named there. For spec, test-case, behavior-change, public-contract, or docs/specs/ work, route through the local spec docs named by the docs index: feature-spec-reference.md, spec-system-reference.md, spec-principles.md, and workflow-spec-test-code-cycle-reference.md when specs/tests/code must stay synchronized. If either file or a required reference doc is missing or stale, auto-run $project-init (or the narrow lower-level route such as $project-config, $docs-init, $scan-all, or $scan --target=<key>) before ordinary project-specific work. Any supported AI tool may execute when this shared context and local docs are available.

1. DETECT: If the prompt starts with an explicit slash skill/workflow command, execute it directly. Otherwise match the prompt against the workflow catalog and skill list.
2. ANALYZE: Choose the best option: execute directly, invoke a skill, activate a standard workflow, or compose a custom step combination.
3. AUTO-SELECT: Pick the best option yourself. Do not ask the user to choose between direct execution, skill, standard workflow, or custom workflow.
4. ACTIVATE: For a selected workflow, call $start-workflow <workflowId>; for a selected skill, invoke that skill; for a custom workflow, sequence custom steps directly; for direct execution, proceed with the task.
5. CREATE TASKS: task tracking for ALL workflow/skill/custom steps before execution when the selected path has multiple steps.
6. EXECUTE: Advance per the Workflow Step Advancement & Parallel Phases rule in your context instructions — model-driven; a sub-agent completion advances a step identically to an inline call; a parallel-phase group is an all-return barrier (advance only after ALL members return, never serialize it)

Shared AI-SDD Protocol Markers

Source: .claude/skills/shared/sync-inline-versions.md

SYNC:ai-sdd-artifact-contract

AI-SDD Artifact Contract — Shared spec-driven development rules stay portable and source-owned.

1. Keep reusable AI-SDD principles in .claude; put repository-specific paths, commands, owners, products, and formats in project config/reference docs.
2. Preserve cycle: spec -> plan -> tasks -> implement -> verify -> update spec/docs.
3. Trace every requirement or invariant through decision, task, TC/test, source evidence, and docs/spec update.
4. Treat code-to-spec extraction as reference-only until accepted by the canonical spec owner.
5. Any supported AI tool may plan, implement, review, or verify with synced context; using multiple tools is optional.
6. Update .claude source first, then sync generated mirrors; do not manually edit .agents, .codex, or AGENTS.md. — why: mirrors are generated artifacts; hand-edits are overwritten on the next sync
7. If docs/project-config.json, root instruction files, or a required project-reference doc is missing or stale, auto-run $project-init or the narrow lower-level route before ordinary project-specific work.

Active reference: shared/sdd-artifact-contract.md in the active skills root.

---

SYNC:ai-sdd-artifact-contract:reminder

• MANDATORY Apply shared/sdd-artifact-contract.md; keep reusable AI-SDD in .claude and local rules in project docs.
• MANDATORY Code-to-spec extraction is reference-only until canonical acceptance; any supported AI tool may execute with synced context.
• MANDATORY Update .claude source before syncing generated mirrors; do not manually edit .agents, .codex, or AGENTS.md.
• MANDATORY Missing or stale project config, root instruction files, or required reference docs route project-specific work through $project-init or the narrow setup route automatically. [TASK-PLANNING] [MANDATORY] BEFORE executing any workflow or skill step, create/update task tracking for all planned steps, then keep it synchronized as each step starts/completes.

[LESSON-LEARNED-REMINDER] [BLOCKING] Task Planning & Continuous Improvement — MANDATORY. Do not skip.

Break work into small tasks (task tracking) before starting. Add final task: "Analyze AI mistakes & lessons learned".

Extract lessons — ROOT CAUSE ONLY, not symptom fixes:

1. Name the FAILURE MODE (reasoning/assumption failure), not symptom — "assumed API existed without reading source" not "used wrong enum value".
2. Generality test: does this failure mode apply to ≥3 contexts/codebases? If not, abstract one level up.
3. Write as a universal rule — strip project-specific names/paths/classes. Useful on any codebase.
4. Consolidate: multiple mistakes sharing one failure mode → ONE lesson.
5. Recurrence gate: "Would this recur in future session WITHOUT this reminder?" — No → skip $learn.
6. Auto-fix gate: "Could $code-review/$code-simplifier/$security-review/$lint catch this?" — Yes → improve review skill instead.
7. BOTH gates pass → ask user to run $learn. [CRITICAL-THINKING-MINDSET] Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence >80% to act. Anti-hallucination principle: Never present guess as fact — cite sources for every claim, admit uncertainty freely, self-check output for errors, cross-reference independently, stay skeptical of own confidence — certainty without evidence root of all hallucination. AI Attention principle (Primacy-Recency): Put the 3 most critical rules at both top and bottom of long prompts/protocols so instruction adherence survives long context windows. Goal-driven execution: Define success criteria first, loop until verified, and stop only when observable checks pass. Tests verify intent: Tests must protect business rules/invariants and fail when the protected intent breaks, not only mirror current behavior.

Common AI Mistake Prevention (System Lessons)

• Re-read files after context compaction. Edit requires prior Read in same context; compaction wipes read state. Re-read before editing.
• Grep for old terms after bulk replacements. AI over-trusts find/replace completeness. Grep full repo after bulk edits for missed refs in docs/configs/catalogs.
• Check downstream references before deleting. Deletions cascade doc/code staleness. Map referencing files before removal.
• After memory loss, check existing state before creating new. Compaction wipes prior-work memory. Query current state to resume — never blindly duplicate.
• Verify AI-generated content against actual code. AI hallucinates APIs, class names, method signatures. Grep to confirm existence before documenting/referencing.
• Trace full dependency chain after edits. Changing a definition misses downstream consumers. Trace the full chain.
• When renaming, grep ALL consumer file types. Some file types silently ignore missing refs (no compile error). Search code, templates, configs, generated files.
• Trace ALL code paths when verifying correctness. Code existing ≠ code executing. Trace early exits, error branches, conditional skips — not just happy path.
• Update docs that embed canonical data when source changes. Docs inlining derived data (workflows, schemas, configs) go stale silently. Update all embedding docs alongside source.
• Verify sub-agent results after context recovery. Background agents may finish while parent compacted — grep-verify output, don't trust assumed completion.
• Cross-check full target list against sub-agent assignments. Parallel sub-agents by category miss boundary items. Reconcile union of assignments against target list before proceeding.
• Sub-agents inherit knowledge only from their agent .md definition — use custom agent types, not built-in Explore. Tool adoption = permission + knowledge + enforcement (numbered workflow step).
• Persist sub-agent findings incrementally, not as a final batch. Long sub-agents hit cutoffs before final write — findings lost. Instruct append-per-section to report file.
• When debugging, ask "whose responsibility?" before fixing. Trace caller (wrong data) vs callee (wrong handling). Fix at responsible layer — never patch symptom site.
• Grep ALL removed names after extraction/refactoring. Primary file "done" ≠ secondary files clean. Grep entire scope for every removed symbol before declaring complete.
• Assume existing values are intentional — ask WHY before changing. Pattern-matching as "wrong" skips context. Before changing any constant/limit/flag: read comments, git blame, surrounding code.
• Verify ALL affected outputs, not just the first. One build green ≠ all green. Multi-stack changes (backend/frontend/tests/docs) require verifying EVERY output.
• Evaluate fit before copying a nearby pattern. Closest example ≠ matching preconditions — verify the new context shares the same constraints, base classes, scope, lifetime.
• Holistic-first debugging — resist nearest-attention trap. Don't dive into first plausible cause. List EVERY precondition (config, env vars, paths, DB, endpoints, creds, versions, DI, data). Verify each against evidence (grep/query — not reasoning). Ask "what would falsify this?" — if nothing, it's not a hypothesis. Most expensive failure: going deeper in "obvious" layer while bug sits in layer never questioned.
• Surgical changes — apply the diff test (context-aware). Two modes: (1) Bug fix → every line traces to the bug; no restyling; orphan cleanup only for imports YOUR changes made unused. (2) Review/enhancement → implement improvements AND announce as "Enhancement beyond main request: [what]". Never silently scope-creep. Diff test: "Would this line exist if I wasn't asked to do X?" — if no, delete or announce.
• Surface ambiguity before coding — don't pick silently. Multiple valid interpretations → present each with effort: "[Request] could mean (1) [N h], (2) [N h]. Which matters?" List scope/format/volume/constraints assumptions first. If simpler path exists, say so. Never silently pick.
• [MANDATORY FIRST ACTION] ALWAYS activate a suitable skill or workflow BEFORE responding. Match task against workflow catalog + skill list; invoke via skill invocation or $start-workflow <workflowId>. NEVER answer or write code before checking. Skip = protocol violation.
• Why-Review adversarial mindset — apply when reviewing any plan, decision, or design. Default SKEPTIC not VALIDATOR: steel-man a rejected alternative, invert each stated reason ("what does it sacrifice?"), stress-test top 2-3 assumptions, run pre-mortem ("ships, fails in 3 months — what breaks?"), surface 1-2 alternatives author missed. Section presence ≠ quality; quality = causal reasoning + concrete mitigations + evidence, not "it's better" or "monitor closely".
• Front-load report-write in sub-agent prompts for large reviews. Many-file sub-agents hit budget before final write — findings lost. Design prompts so: (1) report-write is first explicit deliverable, (2) append per-file/section (not batched), (3) scope bounded so reads don't exhaust budget. Truncated mid-sentence with no report file → spawn narrower scope, don't retry same prompt.
• After context compaction, re-verify all prior phase outcomes before continuing. Summaries describe intent, not environment state (git index, filesystem, processes). On resume, FIRST audit: git status, re-read modified files, verify filesystem. Every "completed" claim is an untested hypothesis until evidence confirms.
• OOM/memory: check row count before row size. Triage: (1) Unbounded query — no DB filter for trigger? Push filter to DB; eliminates OOM. (2) Large rows? Projection reduces proportionally. Row reduction > projection in ROI.
• Keep domain concepts out of generic/shared/infrastructure layers. Reusable layer (shared library, framework, infra module) must reference NO consumer-specific domain concept — tenant/customer/product IDs, business entities, feature rules. Leak compiles + runs → passes review silently while coupling the "reusable" layer to one consumer. Keep shared type domain-free; push domain fields/logic down into the consumer via subclass/composition. — why: a layer coupled to one consumer's domain is no longer reusable.