---

name: baoyu-comic description: Knowledge comic creator supporting multiple styles (Logicomix/Ligne Claire, Ohmsha manga guide). Creates original educational comics with detailed panel layouts and sequential image generation. Use when user asks to create "知识漫画", "教育漫画", "biography comic", "tutorial comic", or "Logicomix-style comic".

Knowledge Comic Creator

Create original knowledge comics with multiple visual styles.

Usage

/baoyu-comic posts/turing-story/source.md
/baoyu-comic  # then paste content

Options

| Option | Values | |--------|--------| | --style | classic (default), dramatic, warm, sepia, vibrant, ohmsha, realistic, wuxia, shoujo, or custom description | | --layout | standard (default), cinematic, dense, splash, mixed, webtoon | | --aspect | 3:4 (default, portrait), 4:3 (landscape), 16:9 (widescreen) | | --lang | auto (default), zh, en, ja, etc. |

Style × Layout × Aspect can be freely combined. Custom styles can be described in natural language.

Aspect ratio is consistent across all pages in a comic.

Auto Selection

| Content Signals | Style | Layout | |-----------------|-------|--------| | Tutorial, how-to, beginner | ohmsha | webtoon | | Computing, AI, programming | ohmsha | dense | | Pre-1950, classical, ancient | sepia | cinematic | | Personal story, mentor | warm | standard | | Conflict, breakthrough | dramatic | splash | | Wine, food, business, lifestyle, professional | realistic | cinematic | | Martial arts, wuxia, xianxia, Chinese historical | wuxia | splash | | Romance, love, school life, friendship, emotional | shoujo | standard | | Biography, balanced | classic | mixed |

Script Directory

Important: All scripts are located in the scripts/ subdirectory of this skill.

Agent Execution Instructions:

1. Determine this SKILL.md file's directory path as SKILL_DIR
2. Script path = ${SKILL_DIR}/scripts/<script-name>.ts
3. Replace all ${SKILL_DIR} in this document with the actual path

Script Reference: | Script | Purpose | |--------|---------| | scripts/merge-to-pdf.ts | Merge comic pages into PDF |

File Structure

Each session creates an independent directory named by content slug:

comic/{topic-slug}/
├── source-{slug}.{ext}            # Source files (text, images, etc.)
├── analysis.md                    # Deep analysis results (YAML+MD)
├── storyboard-chronological.md    # Variant A (preserved)
├── storyboard-thematic.md         # Variant B (preserved)
├── storyboard-character.md        # Variant C (preserved)
├── characters-chronological/      # Variant A chars (preserved)
│   ├── characters.md
│   └── characters.png
├── characters-thematic/           # Variant B chars (preserved)
│   ├── characters.md
│   └── characters.png
├── characters-character/          # Variant C chars (preserved)
│   ├── characters.md
│   └── characters.png
├── storyboard.md                  # Final selected
├── characters/                    # Final selected
│   ├── characters.md
│   └── characters.png
├── prompts/
│   ├── 00-cover-[slug].md
│   └── NN-page-[slug].md
├── 00-cover-[slug].png
├── NN-page-[slug].png
└── {topic-slug}.pdf

Slug Generation:

1. Extract main topic from content (2-4 words, kebab-case)
2. Example: "Alan Turing Biography" → alan-turing-bio

Conflict Resolution: If comic/{topic-slug}/ already exists:

• Append timestamp: {topic-slug}-YYYYMMDD-HHMMSS
• Example: turing-story exists → turing-story-20260118-143052

Source Files: Copy all sources with naming source-{slug}.{ext}:

• source-biography.md, source-portrait.jpg, source-timeline.png, etc.
• Multiple sources supported: text, images, files from conversation

Workflow

Step 1: Analyze Content → analysis.md

Read source content, save it if needed, and perform deep analysis.

Actions:

1. Save source content (if not already a file):
   • If user provides a file path: use as-is
   • If user pastes content: save to source.md in target directory
2. Read source content
3. Deep analysis following references/analysis-framework.md:
   • Target audience identification
   • Value proposition for readers
   • Core themes and narrative potential
   • Key figures and their story arcs
4. Detect source language
5. Determine recommended page count:
   • Short story: 5-8 pages
   • Medium complexity: 9-15 pages
   • Full biography: 16-25 pages
6. Analyze content signals for style/layout recommendations
7. Save to analysis.md

analysis.md Format:

---
title: "Alan Turing: Father of Computing"
topic: Biography
time_span: 1912-1954
source_language: en
user_language: zh
aspect_ratio: "3:4"
recommended_page_count: 12
---

## Target Audience

- **Primary**: Tech enthusiasts curious about computing history
- **Secondary**: Students learning about scientific breakthroughs
- **Tertiary**: General readers interested in biographical stories

## Value Proposition

What readers will gain:
1. Understanding of how modern computing was born
2. Emotional connection to a brilliant but tragic figure
3. Appreciation for the human cost of innovation

## Core Themes

| Theme | Narrative Potential | Visual Opportunity |
|-------|--------------------|--------------------|
| Genius vs. Society | High conflict, dramatic arcs | Contrast scenes |
| Code-breaking | Mystery, tension | Technical diagrams as art |
| Personal tragedy | Emotional depth | Intimate, somber panels |

## Key Figures & Story Arcs

### Alan Turing (Protagonist)
- **Arc**: Misunderstood genius → War hero → Tragic end
- **Visual identity**: Disheveled academic, intense eyes
- **Key moments**: Enigma breakthrough, arrest, final days

### Christopher Morcom (Catalyst)
- **Role**: Early friend whose death shaped Turing
- **Visual identity**: Youthful, bright
- **Key moments**: School friendship, sudden death

## Content Signals

- "biography" → classic + mixed
- "computing history" → ohmsha + dense
- "personal tragedy" → dramatic + splash

## Recommended Approaches

1. **Chronological** - follow life timeline (recommended for biography)
2. **Thematic** - organize by contributions (good for educational focus)
3. **Character-focused** - relationships drive narrative (good for emotional impact)

Step 2: Generate 3 Storyboard Variants

Create three distinct variants, each combining a narrative approach with a recommended style.

| Variant | Narrative Approach | Recommended Style | Layout | |---------|-------------------|-------------------|--------| | A | Chronological | sepia | cinematic | | B | Thematic | ohmsha | dense | | C | Character-focused | warm | standard |

For each variant:

1. Generate storyboard (storyboard-{approach}.md):

   • YAML front matter with narrative_approach, recommended_style, recommended_layout, aspect_ratio
   • Cover design
   • Each page: layout, panel breakdown, visual prompts
   • Written in user's preferred language
   • Reference: references/storyboard-template.md

2. Generate matching characters (characters-{approach}/):

   • characters.md - visual specs matching the recommended style (in user's preferred language)
   • characters.png - character reference sheet
   • Reference: references/character-template.md

All variants are preserved after selection for reference.

Step 3: User Confirms All Options

IMPORTANT: Present ALL options in a single confirmation step using AskUserQuestion. Do NOT interrupt workflow with multiple separate confirmations.

Determine which questions to ask:

| Question | When to Ask | |----------|-------------| | Storyboard variant | Always (required) | | Visual style | Always (required) | | Language | Only if source_language ≠ user_language | | Aspect ratio | Only if user might prefer non-default (e.g., landscape content) |

Language handling:

• If source language = user language: Just inform user (e.g., "Comic will be in Chinese")
• If different: Ask which language to use

All storyboards and prompts are generated in the user's selected/preferred language.

Aspect ratio handling:

• Default: 3:4 (portrait) - standard comic format
• Offer 4:3 (landscape) if content suits it (e.g., panoramic scenes, technical diagrams)
• Offer 16:9 (widescreen) for cinematic content

AskUserQuestion format (example with all questions):

Question 1 (Storyboard): Which storyboard variant?
- A: Chronological + sepia (Recommended)
- B: Thematic + ohmsha
- C: Character-focused + warm
- Custom

Question 2 (Style): Which visual style?
- sepia (Recommended from variant)
- classic / dramatic / warm / sepia / vibrant / ohmsha / realistic / wuxia
- Custom description

Question 3 (Language) - only if mismatch:
- Chinese (source material language)
- English (your preference)

Question 4 (Aspect) - only if relevant:
- 3:4 Portrait (Recommended)
- 4:3 Landscape
- 16:9 Widescreen

After confirmation:

1. Copy selected storyboard → storyboard.md
2. Copy selected characters → characters/
3. Update YAML front matter with confirmed style, language, aspect_ratio
4. If style differs from variant's recommended: regenerate characters/characters.png
5. User may edit files directly for fine-tuning

Step 4: Generate Images

With confirmed storyboard + style + aspect ratio:

For each page (cover + pages):

1. Save prompt to prompts/NN-{cover|page}-[slug].md (in user's preferred language)
2. Generate image using confirmed style and aspect ratio
3. Report progress after each generation

Image Generation Skill Selection:

• Check available image generation skills
• If multiple skills available, ask user preference

Character Reference Handling:

• If skill supports reference image: pass characters/characters.png
• If skill does NOT support reference image: include characters/characters.md content in prompt

Session Management: If image generation skill supports --sessionId:

1. Generate unique session ID: comic-{topic-slug}-{timestamp}
2. Use s