Crun Educational Comic

Crun Educational Comic & Storyboard Generator

Use this skill to transform complex educational concepts, science principles, history, coding logic, business models, or user-provided scripts into high-quality, multi-panel educational comic strips with consistent visual styles, characters, title banners, and clean dialogue captions.

• Educational Topic Deconstruction: Deconstruct complex knowledge into a structured 3–6 panel comic narrative (Hook ➔ Breakdown ➔ Metaphor/Analogy ➔ Summary/Takeaway).
• Visual & Character Consistency: Enforce consistent recurring educational characters (e.g. Professor Owl & Curious Leo, Dr. Byte & Apprentice Sam, or custom user characters) and distinct art styles (2D Flat Educational Vector, Infographic Manga, Hand-drawn Blackboard Sketch, Retro Comic Book).
• Pure Visual AI Prompting: Construct visual prompts for Crun T2I/I2I image models in clear English (bypassing AI text rendering errors inside image models).
• Interactive Button Confirmation: Present complete comic breakdown (panel prompts, style, model, and credit estimate) via ask_question tool before task creation.
• Automated Canvas Stitching & Captioning: Post-process panel images using skills/scenarios/crun-educational-comic/scripts/stitch_comic_panels.py to produce a composite comic grid (2x2, 1x4, 2x3, etc.) featuring title banners, panel numbers, and crisp dialogue callouts in the user's language.

Workflow Overview

Follow this end-to-end execution flow for all educational comic requests:

User Request (Topic / Concept / Script)
   │
   ├── 1. Educational Narrative Breakdown (3–6 Panels)
   │       ├─ Establish Character Cast & Visual Style Preset
   │       └─ Structure Panels: Hook -> Concept -> Metaphor -> Summary
   │
   ├── 2. Upload Reference Media (If user provides reference character/photo)
   │       └─ python <root>/runtime/crun_cli.py upload <local-file>
   │
   ├── 3. Route Model & Construct Visual Prompts
   │       ├─ Modality: "image", Operation: "text-to-image" or "image-edit"
   │       ├─ Models: openai/gpt-image-2, bytedance/seedream-5-pro, google/nano-banana-pro
   │       └─ English visual prompts maintaining character & lighting anchors
   │
   ├── 4. Estimate Affordability & Present Button Confirmation
   │       ├─ Estimate: python <root>/runtime/crun_cli.py task estimate --model <model> --input-file <input.json>
   │       └─ Ask Question: Single-select buttons (Style, Panels, Captions, Model & Credits)
   │
   ├── 5. Batch Create Tasks & Poll Status (crun-task-runner)
   │       ├─ python <root>/runtime/crun_cli.py task create --model <model> --input-file <input.json>
   │       └─ python <root>/runtime/crun_cli.py task wait --task-id <task_id>
   │
   └── 6. Multi-Panel Stitching & Caption Overlay
           └─ python <skill-root>/skills/scenarios/crun-educational-comic/scripts/stitch_comic_panels.py <panel_paths...> \
                --title "<Comic_Title>" --captions "<Caption_1>" "<Caption_2>" ... --grid <2x2|1x4|2x3> -o <output.png>

Detailed Execution Steps

1. Educational Narrative & Character Breakdown

Deconstruct the user's topic or request across three core structural dimensions:

A) Narrative Structure (3 to 6 Panels)

• Panel 1: The Hook / Curiosity Gap: Introduce a relatable question, common misconception, or intriguing scenario.
• Panel 2: The Core Concept / Mechanism: Reveal the underlying principle, scientific law, or system architecture.
• Panel 3: The Metaphor / Visual Analogy: Use a vivid real-world analogy (e.g. comparing computer memory to a desk, or gravity to a curved trampoline).
• Panel 4: Summary / Actionable Takeaway: Conclude with a clear, memorable takeaway or punchline.

B) Educational Character Presets

Maintain consistent characters across panel prompts by incorporating key visual anchors:

• Professor & Student: "Dr. Owl, a wise anthropomorphic owl in a white lab coat with round glasses, and Leo, an eager young rabbit boy with a blue hoodie."
• Tech Mentor & Apprentice: "Dr. Byte, a friendly futuristic robot with glowing cyan eyes, and Sam, a curious young programmer girl with orange hair."
• Custom / Historical Characters: Define exact hair color, clothing, face structure, and accessories, repeating these descriptions in every panel prompt.

C) Visual Style Presets

Explicitly specify one of the following style presets in panel prompts:

1. 2D Flat Educational Vector: Clean line art, vibrant flat colors, modern infographic style, high readability.
2. Infographic Manga: Japanese anime/manga comic style, expressive facial reactions, speed lines, high energy.
3. Hand-drawn Blackboard Sketch: Colored chalk illustration on dark blackboard background, educational classroom feel.
4. Retro Vintage Comic: Pop-art halftone dot texture, classic western comic book aesthetic.

2. Upload Reference Media (If Provided)

When the user provides a custom character drawing, diagram, or photo:

python <skill-root>/runtime/crun_cli.py upload <local-file>

Use the returned file_url in Crun image-edit model payloads (operation: "image-edit").

3. Route Model and Build Visual Prompts

Read skills/crun-model-router/SKILL.md to select image models:

• Preferred Models: openai/gpt-image-2, bytedance/seedream-5-pro, google/nano-banana-pro, qwen-image-2.0-pro.
• Resolution & Aspect Ratio:
  • Individual panels default to 1:1 square (1024x1024 or 1K).
• Prompt Formula:

  [Style Preset] + [Character Visual Anchors] + [Panel Scene Action & Expression] + [Background & Props] + [Lighting & Composition] + "clean lighting, high quality educational comic panel, no text"
  

• Text Rendering Rule: Do NOT request AI image models to render dialogue text or CJK words inside the image prompt. Keep prompts purely visual. Text dialogues will be cleanly rendered via post-processing script.

4. Estimate Affordability & Button Confirmation Gate

Read skills/crun-account-credits/SKILL.md:

python <skill-root>/runtime/crun_cli.py task estimate --model <model> --input-file <input.json>

Verify affordable: true. Before task creation, call ask_question tool to present an interactive choice modal with confirmation buttons.

Mandatory Confirmation Specification:

• Title: e.g., "Educational Comic: Newton's Third Law"
• Total Panels: e.g., 4 Panels (2x2 Grid)
• Visual Style: e.g., 2D Flat Educational Vector
• Characters: e.g., Dr. Owl & Apprentice Leo
• Panel Dialogue Breakdown:
  • Panel 1: "Dr. Owl: Every action has an equal and opposite reaction!"
  • Panel 2: "Leo: Like pushing against a wall?"
  • Panel 3: "Dr. Owl: Exactly! And rocket propulsion works the same way."
  • Panel 4: "Leo: Wow, physics makes sense now!"
• Model & Total Credits: e.g., bytedance/seedream-5-pro (4 panels x 2.5 = 10.0 Credits total)

Interactive Buttons (ask_question):

• Option 1: "Confirm & Generate Comic" (or "确认生成教育漫画")
• Option 2: "Modify Script/Style" (or "修改脚本或画风")
• Option 3: "Cancel" (or "取消")

5. Task Creation & Panel Batching

Read skills/crun-task-runner/SKILL.md. Create a task for each panel and poll status until complete:

python <skill-root>/runtime/crun_cli.py task create --model <model> --input-file <panel_1.json>
python <skill-root>/runtime/crun_cli.py task wait --task-id <task_id_1>

Collect all downloaded panel image file paths: [panel_1.png, panel_2.png, panel_3.png, panel_4.png].

6. Multi-Panel Stitching & Post-Processing

Invoke stitch_comic_panels.py to assemble the downloaded panel images into a unified comic layout with title header and text captions:

python <skill-root>/skills/scenarios/crun-educational-comic/scripts/stitch_comic_panels.py \
  <panel_1.png> <panel_2.png> <panel_3.png> <panel_4.png> \
  --title "<Comic_Title>" \
  --captions "<Caption_1>" "<Caption_2>" "<Caption_3>" "<Caption_4>" \
  --grid 2x2 \
  --output <output_comic_path.png>

Script Parameters:

• images: Space-separated list of downloaded panel image paths.
• --title: Title header banner text.
• --captions: Captions/dialogues in exact panel order (supports CJK & multilingual automatic line wrapping).
• --grid: Layout grid (2x2, 1x4 vertical, 2x3, or auto).
• --output / -o: Output path for stitched comic image (.png).

Delivery and Preview

Always deliver the final result with execution details and an inline local markdown preview:

✅ Educational Comic Generated Successfully!
- Topic Title: <title>
- Total Panels: <panel_count> (<grid_layout>)
- Visual Style: <style_preset>
- Model Used: <model>
- Credits Used: <total_credits>
- File Path: <local_output_path>

![Educational Comic Preview](/absolute/path/to/comic_output.png)

Canonical User Scenarios & Exemplar Breakdowns

Scenario 1: 4-Panel Physics Comic ("Newton's Third Law")

• Input Intent: "Draw a 4-panel comic explaining Newton's Third Law for middle school students."
• Visual Style Preset: 2D Flat Educational Vector
• Characters: Dr. Owl (lab coat, glasses) & Student Leo (rabbit boy, blue hoodie).
• Panel Structure:
  1. Panel 1: Dr. Owl introducing the concept ("Action = Reaction").
  2. Panel 2: Leo jumping off a skateboard, pushing the skateboard backward.
  3. Panel 3: Rocket ship jetting exhaust gas downward while flying upward.
  4. Panel 4: Leo & Dr. Owl high-fiving ("Physics is everywhere!").
• Stitching Command:

  python skills/scenarios/crun-educational-comic/scripts/stitch_comic_panels.py \
    panel_1.png panel_2.png panel_3.png panel_4.png \
    --title "Science Comic: Newton's Third Law" \
    --captions "Dr. Owl: For every action, there is an equal and opposite reaction!" \
               "Leo: When I jump forward, my feet push the skateboard backward!" \
               "Dr. Owl: Exactly! Space rockets fly by pushing hot gas downward." \
               "Leo: Cool! Action and reaction are everywhere in life!" \
    --grid 2x2 \
    -o output_newton_comic.png
  

Scenario 2: 3-Panel Coding Comic ("How Git Branching Works")

• Input Intent: "Make an educational comic explaining Git branches vs main line."
• Visual Style Preset: Infographic Manga
• Characters: Dr. Byte (friendly robot) & Sam (girl programmer).
• Panel Structure:
  1. Panel 1: Sam worried about breaking production code on main.
  2. Panel 2: Dr. Byte demonstrating creating a parallel feature branch like a parallel universe track.
  3. Panel 3: Safely merging the tested feature branch back into main.
• Stitching Command:

  python skills/scenarios/crun-educational-comic/scripts/stitch_comic_panels.py \
    panel_1.png panel_2.png panel_3.png \
    --title "Tech Comic: Understanding Git Branching" \
    --captions "Sam: I'm scared to test new code directly on the production main branch!" \
               "Dr. Byte: Create a Git Branch! It's like a safe parallel universe to experiment." \
               "Sam: Once it works, I merge it back cleanly. No risk at all!" \
    --grid 3x1 \
    -o output_git_comic.png