Infographic generator
Skill Affitor/affiliate-skills/skills/content/infographic-generator
Generate branded infographic specifications from any content or data. Outputs structured layout, copy, data visualization, and color scheme — ready to render as HTML/CSS, Satori, Canva, or any design tool. Use this skill when the user wants an infographic, data visual, social media image, comparison chart, stat card, or says "create an infographic for [content]", "make a visual for my LinkedIn post", "design an image for [topic]", "stat graphic for [data]", "comparison infographic", "branded image", "social media graphic", "infographic for [blog post]", "data visualization", "visual content", "image for my post", "LinkedIn carousel image", "feature comparison chart", "pricing table image".From its SKILL.md
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SKILL.md
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Infographic Generator
Generate complete infographic specifications from any content, data, or topic. Outputs structured layout + all copy + data points + color scheme — ready to render as HTML/CSS, with Satori (server-side), in Canva, Figma, or any design tool.
LinkedIn posts with images get 2-3x more engagement. This skill turns your content into visual assets without design skills.
Inspired by content-pipeline's Satori rendering: AI writes the content → structured spec → rendered as a branded image.
Stage
This skill belongs to Stage S2: Content
When to Use
- After
viral-post-writercreates a LinkedIn post — add a visual - After
content-research-briefcollects stats — visualize them - When creating comparison content — feature/pricing comparison chart
- When sharing data or stats — stat highlight cards
- When creating a listicle — visual checklist or numbered list
- For any social media post that would benefit from a branded image
Input Schema
content: string # (required) Content to visualize — post text, data, or article
infographic_type: string # (optional, default: auto-detected)
# "stat_highlight" — 1-3 key numbers, large and bold
# "comparison" — side-by-side product/feature comparison
# "process_flow" — step-by-step workflow or how-to
# "checklist" — list of items with checkmarks
# "timeline" — chronological events
# "data_chart" — bar/pie chart representation
# "quote_card" — featured quote with attribution
# "feature_grid" — grid of features/benefits with icons
platform: string # (optional, default: "linkedin")
# "linkedin" — 1080×1350 (portrait, optimal engagement)
# "instagram" — 1080×1080 (square)
# "twitter" — 1200×675 (landscape)
# "facebook" — 1200×630
# "blog" — 1200×800 (featured image)
brand: object # (optional) Brand customization
name: string # Company/personal brand name
primary_color: string # Hex — "#0066FF"
secondary_color: string # Hex — "#1A1A2E"
accent_color: string # Hex — "#FF6B35"
font_style: string # "modern" | "classic" | "bold" | "minimal"
logo_text: string # Text-based logo — "Affitor" | "@yourhandle"
output_format: string # (optional, default: "spec")
# "spec" — structured JSON spec (for any renderer)
# "html" — renderable HTML/CSS (self-contained)
# "both" — spec + HTML
Workflow
Step 1: Analyze Content and Select Type
Read the input content and detect the best infographic type:
| Content Pattern | Auto-detected Type |
|---|---|
| Contains 1-3 prominent numbers/stats | stat_highlight |
| Contains "vs", comparison data | comparison |
| Contains numbered steps or a process | process_flow |
| Contains a list of items (3-10) | checklist or feature_grid |
| Contains dates or chronological events | timeline |
| Contains a notable quote | quote_card |
| Contains percentages or proportions | data_chart |
If infographic_type is provided, use that. Otherwise auto-detect.
Step 2: Extract Visual Data
From the content, extract exactly what needs to appear in the infographic:
For stat_highlight:
stats:
- number: "30%" # The big number
label: "commission" # What it measures
context: "recurring" # Additional context
- number: "60"
label: "cookie days"
context: "industry avg: 30"
For comparison:
items:
- name: "HeyGen"
features:
- label: "Commission"
value: "30% recurring"
highlight: true # winner for this row
- label: "Cookie"
value: "60 days"
highlight: true
- name: "Synthesia"
features:
- label: "Commission"
value: "25% one-time"
highlight: false
- label: "Cookie"
value: "30 days"
highlight: false
For process_flow:
steps:
- number: 1
title: "Research"
description: "Find winning programs"
icon: "🔍"
- number: 2
title: "Create"
description: "Write content that converts"
icon: "✍️"
For checklist:
items:
- text: "Recurring commission"
checked: true
- text: "60+ day cookie"
checked: true
- text: "Free trial available"
checked: true
- text: "Dedicated affiliate manager"
checked: false
Step 3: Design Layout
Based on type + platform, define the layout:
Platform dimensions:
| Platform | Width | Height | Aspect |
|---|---|---|---|
| 1080 | 1350 | 4:5 (portrait) | |
| 1080 | 1080 | 1:1 (square) | |
| 1200 | 675 | 16:9 (landscape) | |
| 1200 | 630 | ~2:1 | |
| Blog | 1200 | 800 | 3:2 |
Layout structure (all types):
┌─────────────────────────────┐
│ HEADER │ 10-15% height
│ Headline / Title │
├─────────────────────────────┤
│ │
│ BODY │ 70-80% height
│ Data / Content │
│ (type-specific layout) │
│ │
├─────────────────────────────┤
│ FOOTER │ 10% height
│ Brand / CTA / Source │
└─────────────────────────────┘
Step 4: Generate Color Scheme
If brand colors provided: Use them directly.
If no brand colors: Generate a professional palette:
# Default professional palette options
palettes:
dark_modern: # Dark background, light text
bg: "#1A1A2E"
text: "#FFFFFF"
accent: "#0066FF"
secondary: "#16213E"
light_clean: # Light background, dark text
bg: "#FFFFFF"
text: "#1A1A2E"
accent: "#0066FF"
secondary: "#F0F4F8"
warm_bold: # Warm tones
bg: "#FFF8F0"
text: "#2D2D2D"
accent: "#FF6B35"
secondary: "#FFE8D6"
dark_gradient: # Gradient dark
bg: "linear-gradient(135deg, #1A1A2E, #16213E)"
text: "#FFFFFF"
accent: "#00D4AA"
secondary: "#2A2A4A"
Auto-select based on platform:
- LinkedIn →
dark_modernorlight_clean(professional) - Twitter →
dark_gradientorwarm_bold(attention-grabbing) - Instagram → Any (most visual flexibility)
Step 5: Generate All Copy
Write every piece of text that appears in the infographic:
copy:
headline: string # Main title — bold, short (max 8 words)
subheadline: string # Optional supporting line
body_items: string[] # Data labels, descriptions, etc.
cta: string # Call-to-action text — "Link in bio" | "See comments for link"
footer: string # Brand name or @handle
source: string # "Data: openaffiliate.dev" or source attribution
Copy rules:
- Headlines: 3-8 words, bold claim or specific number
- All text must be readable at mobile scale (not too small)
- No more than 50 total words on the infographic (less = better)
- Data > adjectives (show numbers, not "amazing" or "incredible")
Step 6: Output
Spec output (default):
Complete structured spec that any renderer can consume:
infographic_spec:
type: string
platform: string
dimensions:
width: number
height: number
colors:
background: string
text: string
accent: string
secondary: string
layout:
header: object
body: object
footer: object
data: object # Type-specific data (stats, comparison items, steps, etc.)
copy:
headline: string
subheadline: string
body_items: string[]
cta: string
footer: string
source: string
HTML output (if output_format is "html" or "both"):
Generate a self-contained HTML file with inline CSS that renders the infographic at exact dimensions. This can be:
- Opened in a browser and screenshotted
- Rendered server-side with Satori or Puppeteer
- Used as a starting point for design iteration
<!-- Self-contained, no external dependencies -->
<div style="width: 1080px; height: 1350px; ...">
<!-- Header -->
<!-- Body (type-specific) -->
<!-- Footer -->
</div>
Step 7: Self-Validation
Before presenting output, verify:
- Total word count on infographic ≤ 50 words
- All text readable at 50% zoom (minimum effective font size)
- Color contrast meets accessibility (WCAG AA: 4.5:1 ratio)
- Data points are accurate and attributed
- Layout doesn't feel cramped — whitespace is intentional
- Platform dimensions are correct
If any check fails, fix before delivering.
Output Format
## Infographic: [Headline]
### Spec
- **Type:** [stat_highlight]
- **Platform:** [LinkedIn] — 1080×1350
- **Colors:** [dark_modern] — bg: #1A1A2E, accent: #0066FF
### Preview (text representation)
┌─────────────────────────────────┐
│ │
│ HeyGen vs Synthesia │
│ The Real Comparison │
│ │
│ ┌──────────┐ ┌──────────┐ │
│ │ HeyGen │ │Synthesia │ │
│ │ │ │ │ │
│ │ 30% rec. │ │ 25% once │ │
│ │ 60 days │ │ 30 days │ │
│ │ ★ 127 │ │ ★ 84 │ │
│ └──────────┘ └──────────┘ │
│ │
│ 🏆 Winner: HeyGen │
│ │
│ ─────────────────────────── │
│ @yourhandle · openaffiliate.dev │
└─────────────────────────────────┘
### Data
[Structured spec as YAML or JSON]
### HTML (if requested)
[Self-contained HTML/CSS code block]
### Next Steps
- Post to [platform] with your viral post from `viral-post-writer`
- Create variations for other platforms: `--platform instagram`
- Generate more infographics from different data in your `content-research-brief`
Error Handling
- Content has no extractable data: Generate a
quote_cardorchecklisttype instead. Note: "No numerical data found. Created a [type] infographic instead." - Too much data for one infographic: Select top 3-5 most impactful data points. Note: "Content has [X] data points. Selected the [Y] most impactful for visual clarity. Consider creating multiple infographics."
- No brand colors: Use default palette. Note: "No brand colors specified. Using [palette name]. Add brand colors for consistent branding."
- HTML output too complex: Simplify layout. Infographics should be simple — complexity kills visual impact.
Examples
Example 1: User: "Make an infographic comparing HeyGen vs Synthesia for LinkedIn" → type: comparison, platform: linkedin (1080×1350) → Extract: commission, cookie, rating, price for each → Output: side-by-side comparison card with winner highlighted → Dark modern palette, bold numbers
Example 2: User: "Create a stat card from my research brief showing HeyGen's key numbers" → type: stat_highlight, platform: linkedin → Extract: "$60M raised", "40K businesses", "30% commission" → Output: 3 large numbers with labels and context
Example 3: User: "Visualize the affiliate funnel steps as an infographic" → type: process_flow, platform: blog (1200×800) → Steps: Research → Content → Landing → Deploy → Track → Optimize → Output: horizontal flow with icons and brief descriptions
Feedback & Issue Reporting
When this skill produces unexpected, incomplete, or incorrect output, generate a
skill_feedback block (see shared/references/feedback-protocol.md for full schema).
Skill-specific failure modes:
- No extractable data from content: Content is purely narrative, no stats/numbers. Report as
data_quality. - HTML output doesn't render correctly: CSS issues, wrong dimensions, text overflow. Report as
wrong_outputwith the HTML. - Too many words on infographic: >50 words makes it unreadable. Report as
wrong_output.
Auto-detect triggers:
infographic_spec.datahas <2 data points- Total word count in all copy fields > 60
- Dimensions don't match declared platform
Report issues: GitHub Issues | Discussions
References
shared/references/social-data-providers.md— data sources for infographic contentshared/references/platform-rules.md— platform-specific image requirementsshared/references/flywheel-connections.md— master flywheel connection mapshared/references/feedback-protocol.md— issue detection and reporting standard
Flywheel Connections
Feeds Into
social-media-scheduler(S5) — infographic ready to schedule with postlanding-page-creator(S4) — infographic as hero image or section visualemail-drip-sequence(S5) — infographic as email visual contentbio-link-deployer(S5) — infographic in link hub
Fed By
content-research-brief(S2) — key stats and data for visualizationviral-post-writer(S2) — post content to create accompanying visualaffiliate-blog-builder(S3) — blog data for featured image infographictrending-content-scout(S1) — engagement data for benchmark visualstraffic-analyzer(S1) — traffic data for comparison infographicscomparison-post-writer(S3) — comparison data for visual formatcommission-calculator(S1) — commission data for stat highlights
Feedback Loop
- S6 posts with infographics vs without →
performance-reportshows engagement lift → prioritize infographic creation for high-value content
Quality Gate
Before delivering output, verify:
- Would I stop scrolling for this image?
- Can I understand the main point in under 3 seconds?
- Is the data accurate and attributed?
- Does it look professional, not like clip art?
- Is it readable on a phone screen?
Any NO → redesign before delivering.
chain_metadata:
skill_slug: "infographic-generator"
stage: "content"
timestamp: string
suggested_next:
- "social-media-scheduler"
- "viral-post-writer"
- "landing-page-creator"
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.
Gives 0 of the 12 instructions most social media skills give in ~3.7k tokens
Counted across 400 of the 422 authors here whose files we hold, read 2026-09-06
- Build content around three to five pillarsin 40 of 400, across 16 files
- Read product marketing context before asking questionsin 37 of 400, across 15 files
- Gather goals, audience, brand voice, and resourcesin 35 of 400, across 13 files
- Maintain one to two weeks of scheduled contentin 27 of 400, across 8 files
- Batch content creation in weekly sessionsin 23 of 400, across 7 files
- Review top and bottom posts weeklyin 22 of 400, across 7 files
- Run the quality gate before deliveringin 20 of 400, across 11 files
- Add subtitles to all social videoin 20 of 400, across 7 files
- Write standalone captions that work without contextin 20 of 400, across 7 files
- Prefer specificity over adjectivesin 19 of 400, across 10 files
- Respond to all comments on your postsin 18 of 400, across 6 files
- Carry one actual claim per postin 18 of 400, across 9 files
Said here and by no other author read
- Detect the best infographic type from content
- Use the provided infographic type when given
- Extract the exact data points to visualize
- Design the layout from type and platform
- Use provided brand colors directly
- Generate a default palette when brand colors are missing
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.