name: visual-card description: Generate professional visual note cards (视觉笔记卡片/信息图) from knowledge base content as single-page HTML infographics with PNG export. Use when users want to create visual summaries, knowledge cards, infographics, or poster-style visualizations of topics from the knowledge base. argument-hint: [topic-or-chapter-path] allowed-tools: Bash(*), Read, Glob, Grep, Write, Edit

Tapestry Visual Card

Generate professional visual note cards from knowledge base content: $ARGUMENTS

When to use this skill

Use this skill when:

• A user wants to create a visual note card or infographic from KB content
• The user asks to "visualize", "create a card", "make an infographic", or "generate a visual summary"
• The user mentions 视觉笔记, 知识卡片, 信息图, or one-pager summary
• You need to present knowledge base content in a shareable, poster-style format
• The user wants to export KB content for social media or presentations

Purpose

This skill transforms structured knowledge base content into beautiful, information-dense visual cards following an editorial magazine aesthetic. It produces:

1. A self-contained HTML file with embedded styles and export functionality
2. A high-quality PNG image (via Playwright rendering)

The cards follow a fixed layout structure optimized for readability and social sharing.

Workflow

Architecture: Agent-Driven Content Synthesis

This skill uses an Agent-driven approach where the Agent reads the template specification and autonomously decides how to map source content to template blocks.

Key principle: The template structure is fixed (7 display blocks), but content mapping is intelligent and adaptive.

Step 1: Identify the Source Content

Resolve the target from the argument:

• If a specific chapter path is provided (e.g., "ai-and-development-tools/ai-agent-architecture.md"), use that chapter
• If a topic is provided (e.g., "ai-and-development-tools"), offer to generate cards for its chapters
• If no argument, list available topics and ask the user to choose

# List available topics
ls -d data/books/*/

Step 2: Run the Context Preparation Script

python visual-card/_scripts/generate_card.py --chapter "<topic>/<chapter.md>"

This prints a JSON object with three keys:

• source.content: the raw chapter markdown
• source.frontmatter: parsed metadata (title, author, date, etc.)
• template_specification: full natural language spec of the 7 template blocks
• instructions: task description

Step 3: Read the Template Specification

Read visual-card/_templates/card_template_spec.md to understand:

• What each of the 7 blocks is for
• Quality criteria for content selection
• Expected JSON output format with examples

Step 4: Synthesize the Content Mapping (Agent Task)

Analyze the source content against the template spec and produce a synthesis JSON. Write it to a temp file:

# Write synthesis to temp file
cat > /tmp/card_synthesis.json << 'EOF'
{
  "metadata": {
    "chapter": "<chapter-name>",
    "title_en": "<English title, 3-8 words>",
    "title_cn": "<Chinese title>",
    "topic_label": "<TOPIC LABEL IN CAPS>",
    "source_label": "<SOURCE IN CAPS>",
    "thesis": "<one sentence with <strong>keyword</strong> embedded>"
  },
  "framework": {
    "formula": "<A × B × C format>",
    "formula_subtext": "<what the formula means>",
    "label": "<FRAMEWORK NAME>",
    "components": [
      {"letter": "A", "name": "Component Name", "description": "One-line description"}
    ]
  },
  "insights": [
    {"number": "01", "title": "Insight Title", "description": "2-3 sentence insight description"}
  ],
  "dark_panel": {
    "icon": "⚡",
    "section_title": "<Section Title>",
    "block1": {"title": "<Block 1 Title>", "items": ["item 1", "item 2", "item 3"]},
    "block2": {"title": "<Block 2 Title>", "items": ["item 1", "item 2", "item 3", "item 4"]},
    "conclusion": {"label": "核心洞察", "text": "<conclusion text>", "highlight": "<highlighted phrase>"}
  },
  "closing_thought": {
    "text": "<memorable closing insight>",
    "attribution": "<author or source>"
  }
}
EOF

Content decisions to make:

• Framework: Identify 2-6 core concepts that structure the topic (acronym or logical grouping)
• Insights: Select 3-5 most surprising, actionable, or paradigm-shifting points
• Dark panel: Narrative flow — overview → key mechanics → conclusion
• Thesis: A single provocative claim that captures the article's core argument
• Closing thought: A memorable synthesis that gives the reader something to carry away

Step 5: Generate HTML/PNG from Synthesis

python visual-card/_scripts/generate_card_from_synthesis.py \
  --synthesis /tmp/card_synthesis.json \
  --chapter "<topic>/<chapter.md>"

This renders the synthesis into the HTML template and exports PNG via Playwright.

Step 6: Present the Results

Report back with:

• The generated PNG path (primary deliverable)
• The HTML path (for browser-based editing/export)
• A brief summary of the content mapping decisions made

Card Design System

The visual cards follow a fixed editorial layout:

┌──────────────────────────────────────────┐
│ TOPIC LABEL              SOURCE LABEL    │  ← Top Bar
├────────────────────┬─────────────────────┤
│ English Title      │ Thesis statement    │  ← Title Area
│ 中文标题            │ with key insight    │
├─────┬─────┬─────┬──┴──────────────────────┤
│  M  │  P  │  D  │  G  │                  │  ← Framework Row (2-6 cards)
├─────┴─────┴─────┴─────┴──────────────────┤
│ ⚡ Dark Panel      │ ★ Light Panel        │  ← Two-Column Content
│ (narrative/story)  │ (numbered insights)  │
├──────────────────────────────────────────┤
│ Formula = M × P × D × G    Closing note  │  ← Highlight Bar
├──────────────────────────────────────────┤
│ FRAMEWORK LABEL              BRAND NAME   │  ← Footer
└──────────────────────────────────────────┘

Color Palette

Default theme (customizable):

• Primary: Deep teal #1a7a6d
• Accent: Orange #e8713a
• Background: Warm gray #f0ebe4
• Dark panel: #1a1a1a

Typography

• English display: Playfair Display (serif)
• Chinese body: Noto Sans SC
• Monospace/labels: JetBrains Mono

Content Strategy

When generating cards from KB content:

1. Extract or synthesize a framework - Identify the core structural model (2-6 components)
2. Create a memorable acronym - Make the framework easy to remember
3. Write a provocative thesis - Strong, opinionated claim in the title area
4. Dark panel = narrative - Problems, transitions, paradigm shifts
5. Light panel = insights - Actionable numbered takeaways (3-4 items)
6. Bottom formula - Distill into one equation-style summary

Output Format

By default, generate both HTML and PNG:

1. HTML file: Self-contained with embedded CSS, Google Fonts, and html2canvas for browser export
2. PNG file: High-quality render at 1.5× scale (1800px wide), optimized for social media

Output location: data/cards/YYYY-MM-DD-{topic-slug}/

Language Handling

CRITICAL: The card content language should match the source KB content language:

• If the KB chapter is in Chinese, generate Chinese card content
• If the KB chapter is in English, generate English card content
• Always include bilingual titles (English + Chinese) regardless of content language
• Technical terms and framework acronyms remain in their original language

Dependencies

The skill requires:

• Python 3.8+
• Playwright (for PNG rendering): pip install playwright && playwright install chromium

Dependencies are checked and installed automatically on first run.

Resources

• visual-card/_scripts/generate_card.py: Main orchestrator for Agent-driven workflow
• visual-card/_scripts/prepare_card_context.py: Prepares context (source + template spec) for Agent
• visual-card/_scripts/generate_card_from_synthesis.py: Generates HTML/PNG from Agent synthesis
• visual-card/_scripts/html2png.py: Playwright-based HTML to PNG renderer
• visual-card/_scripts/generate_card_legacy.py: Legacy hardcoded extraction (fallback)
• visual-card/_templates/card_template.html: Canonical HTML/CSS template
• visual-card/_templates/card_template_spec.md: Natural language specification for Agent (describes 7 blocks, quality criteria, examples)
• _assets/: Additional assets (if needed)

Example Usage

# Generate a card for a specific chapter
python visual-card/_scripts/generate_card.py \
  --chapter "data/books/markets-and-trading/market-structure-and-signals"

# Generate with custom color scheme
python visual-card/_scripts/generate_card.py \
  --chapter "data/books/ai-and-research/model-training-and-optimization" \
  --primary-color "#2d5a8c" \
  --accent-color "#d4af37"

# Generate HTML only (skip PNG)
python visual-card/_scripts/generate_card.py \
  --chapter "data/books/community-qa-and-discussion/platform-discussions" \
  --html-only

Integration with Other Skills

This skill works best after:

• Synthesis: Content is already organized in the knowledge base
• Display: User has browsed the KB and identified interesting chapters

It can be chained with:

• Display: Generate cards for all chapters in a topic
• Feed: Create visual cards from feed items

Notes

• Cards are 1200px wide by default (poster format)
• PNG export uses device-scale-factor for high-DPI rendering
• The floating action button (FAB) in HTML allows browser-based export at multiple resolutions
• All cards include a copyright attribution to the original visual-note-card-skills project