/baoyu-infographic

by Jim Liu 宝玉jimliu/baoyu-skills26k stars

2,896

Generate professional infographics with 21 layout types and 22 visual styles. Analyzes content, recommends layout×style combinations, and generates publication-ready infographics. Use when user asks to create "infographic", "信息图", "visual summary", "可视化", or "高密度信息大图".

50 files
92.7 KB
Updated 4 months ago
GitHub
1 warning

Ask your Agent

Use this Skill: https://skilld.dev/gh/jimliu/baoyu-skills/baoyu-infographic

This session only. Nothing lands on disk.

referenceslayoutsdense-modules.md

3.7 KB raw

≈942 tokens on demand. Your agent reads this file only when SKILL.md points to it.

dense-modules

High-density modular layout with 6-7 typed information modules packed with concrete data.

Structure

6-7 distinct modules per image, each serving a specific information function
Every module contains concrete data: brand names, numbers, percentages, parameters
Minimal whitespace—compact spacing prioritized over breathing room
Smaller text acceptable to maximize information density
Each module identified by coordinate label or section marker (e.g., MOD-1, SEC-A)

Module Archetypes

Module	Purpose	Content Requirements
Brand/Selection Array	Grid of options with recommendations	4-8 items with icons, names, brief descriptions; highlight "best choice"
Specification Scale	Quality/measurement gauge	3-5 levels with precise numerical increments, quality indicators (emoji faces, checkmarks)
Deep Dive/Detail	Technical breakdown of key item	Zoom-in callouts, internal components, cross-section or exploded view
Scenario Comparison	Side-by-side use cases	3-6 scenarios with specific recommendations and data per scenario
Identification Tips	How-to checklist	3-5 inspection methods: look/test/check/ask format
Warning/Pitfall Zone	Critical mistakes to avoid	3-5 pitfalls with consequences, 1-2 correct approaches; high visual contrast
Quick Reference	Compact summary	Dense table, one-line summaries, decision flowchart, or key takeaways

Variants

Variant	Focus	Visual Emphasis
Coordinate-labeled	Precision and systematicity	Each module has alphanumeric coordinate (A-01, B-05, C-12), ruler/axis markers
Grid-cell	Order and structure	Modules in strict rectangular cells divided by thick lines, Swiss grid feel
Free-flowing	Organic density	Magazine-style layout with dotted frames, varying module sizes, connected by arrows

Best For

Product selection guides and buying guides
Multi-dimensional comparison content
Data-rich educational materials
"Avoid pitfalls" / "complete guide" formats
Content targeting platforms like Xiaohongshu with high-density visual requirements

Visual Elements

Module boundary markers (thick lines, dotted frames, or coordinate grids)
Quality indicators per module (emoji faces, checkmarks, crosses, crowns)
Data callout boxes with highlighted numbers
Comparison arrows and progression indicators
Warning/alert visual markers for pitfall modules
Metadata in corners (page numbers, timestamps, small barcodes)

Text Placement

Main title at top, prominent and impactful
Subtitle with module count ("X大维度全面解析...")
Module headers inside colored badges or labeled frames
Body text compact, multiple columns within modules
Numbers highlighted with accent colors, slightly larger than body text

Information Density Rules

Every corner should contain useful information or metadata
No decorative-only empty space
Text size may be reduced to fit more content—information over font size
Each module must have specific data points, not generic descriptions
Balance between density and readability: dense but organized

Recommended Pairings

pop-laboratory: Technical precision with coordinate markers and blueprint grid
morandi-journal: Hand-drawn warmth with doodle illustrations and organic frames
retro-pop-grid: 1970s pop art with strict grid cells and bold contrast
retro-popup-pop: Vintage desktop popups with chunky pixel UI for retro-tech dense guides
corporate-memphis: Clean business feel for product comparisons
technical-schematic: Engineering precision for technical product guides

Source: SKILL.md on GitHub

Third-party checks

1 warning16d5 checks · Risk MEDIUM

Gen Agent Trust Hub16d
The skill dynamically discovers and executes scripts to handle image generation and processes untrusted user content for prompt generation. It uses the bun runtime and well-known command-line tools to interact with image generation backends.
Socket16d
No alerts
Snyk16d
Risk: LOW · No issues
Runlayer6mo
1/45 files flagged
ZeroLeaks5mo
Score: 93/100 · 2 sections analyzed

Provenance

Signed by skilld at b0ac523. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 2 days ago.

Activeupdated 4 months ago

Capability

version: 1.117.4

Other metadata

metadata: { "openclaw": { "homepage": "https://github.com/JimLiu/baoyu-skills#baoyu-infographic" } }

Topics

infographic
image-generation
layout
visual-design
data-visualization
content-analysis
style-guide
prompt-engineering

What it does

Generates publication-ready infographics by combining one of 21 layout types with one of 22 visual styles, analyzing content to recommend optimal pairings. Targets use cases like timelines, comparisons, hierarchies, and data-rich guides across multiple languages and aspect ratios.

Generated from the current SKILL.md.

Frequently asked

How many layout and style combinations are available?

21 layout types and 22 visual styles that can be freely combined. Common pairings are pre-recommended (e.g., linear-progression + craft-handmade for timelines), but any layout works with any style.

Does this skill generate raster images or code?

Raster images only. The skill resolves to an available image backend (Codex imagegen, Cursor GenerateImage, or baoyu-image-gen) and never substitutes SVG, HTML, or canvas rendering.

Can I provide reference images to guide the style?

Yes. You can pass reference images via --ref and specify usage mode (direct, style, or palette) to guide visual aesthetics, color, or composition.

What happens if I don't confirm the layout and style choices?

By default the skill requires confirmation before generation. Only --no-confirm or explicit skip-confirmation wording (like '直接生成') bypasses this step.

What languages does this skill support?

Multiple languages via the --lang option (en, zh, ja, etc.), with zh and en as primary targets based on the keyword detection and recommended combinations listed in the skill.

Generated from the current SKILL.md. These answers refresh after source changes.

/baoyu-infographic