Baoyu Article Illustrator: Type × Style × Palette Consistent Illustrations

The Baoyu Article Illustrator skill turns a piece of text into a set of illustrations that share a consistent type, style, and palette. You give it an article (as a file path or pasted content), optionally tell it what kind of images you want, and it analyzes the content, decides where pictures would help, generates prompts, and produces the images. This is useful when you need to add visuals to a blog post, documentation, a report, or any long-form content and you want the illustrations to look like they belong together.

What it does

The skill works through a seven-step workflow that mirrors how a human illustrator would approach the job. It reads the article, identifies the key concepts and where a visual would add value, confirms the settings with you, writes an outline, builds a prompt for each illustration, generates the images, and inserts them into the article. The three dimensions of control (type, style, palette) let you tune the output without rewriting prompts by hand.

• Type controls the information structure: infographic, scene, flowchart, comparison, framework, timeline.
• Style controls the rendering approach: notion, warm, minimal, blueprint, watercolor, elegant, and others.
• Palette controls the color scheme and is optional. If you skip it, the style's default colors are used.

You can combine them freely, or use a preset like edu-visual that sets all three at once.

Before you start

This is an optional skill. You install it on demand. The skill path is optional-skills/creative/baoyu-article-illustrator. It runs on Linux, macOS, and Windows. The author is 宝玉 (JimLiu) and the license is MIT.

You need an agent that has access to the image_generate tool, which is the backend for producing images. The underlying model is user-configured (the default is FAL FLUX 2 Klein 9B). The agent cannot select the backend; it uses whatever the user set up.

You also need the clarify tool for the confirmation step, vision_analyze if you want to use reference images, write_file and read_file for text operations, and terminal for downloading images and copying reference files.

The three dimensions

Dimension	Controls	Examples
Type	Information structure	infographic, scene, flowchart, comparison, framework, timeline
Style	Rendering approach	notion, warm, minimal, blueprint, watercolor, elegant
Palette	Color scheme (optional)	macaron, warm, neon, overrides style's default colors

Combine freely: type=infographic, style=vector-illustration, palette=macaron.

Or use presets: edu-visual → type + style + palette in one shot. See style-presets.md.

Types

Type	Best For
infographic	Data, metrics, technical
scene	Narratives, emotional
flowchart	Processes, workflows
comparison	Side-by-side, options
framework	Models, architecture
timeline	History, evolution

Styles

See references/styles.md for Core Styles, the full gallery, and Type × Style compatibility.

Output structure

{output-dir}/
├── source-{slug}.{ext}    # Only for pasted content
├── outline.md
├── prompts/
│   └── NN-{type}-{slug}.md
└── NN-{type}-{slug}.png

Default output directory:

Input	Output Directory	Markdown Insert Path
Article file path	{article-dir}/imgs/	imgs/NN-{type}-{slug}.png
Pasted content	illustrations/{topic-slug}/ (cwd)	illustrations/{topic-slug}/NN-{type}-{slug}.png

If the user asks for a different layout (e.g., images alongside the article, or a illustrations/ subdirectory), honor that.

Slug: 2-4 words, kebab-case. Conflict: append -YYYYMMDD-HHMMSS.

Core principles

• Visualize concepts, not metaphors, if the article uses a metaphor (e.g., "电锯切西瓜"), illustrate the underlying concept, not the literal image.
• Labels use article data, actual numbers, terms, and quotes from the article, not generic placeholders.
• Prompt files are reproducibility records, every illustration must have a saved prompt file under prompts/ before any image is generated.
• Strip secrets, scan source content for API keys, tokens, or credentials before writing anything to disk.

Workflow

- [ ] Step 1: Detect reference images (if provided)
- [ ] Step 2: Analyze content
- [ ] Step 3: Confirm settings (clarify tool, one question at a time)
- [ ] Step 4: Generate outline
- [ ] Step 5: Generate prompts
- [ ] Step 6: Generate images (image_generate)
- [ ] Step 7: Finalize

Step 1: Detect Reference Images

If the user supplies reference images (paths pasted inline, attachments, or a URL):

1. For each reference, call vision_analyze with the path/URL and a question asking for style, palette, composition, and subject. Record the returned description in {output-dir}/references/NN-ref-{slug}.md via write_file.
2. Do not try to copy the binary via write_file / read_file, those are text-only. If you want a local copy for the record, use terminal (cp "$src" "{output-dir}/references/NN-ref-{slug}.{ext}"). The skill itself never needs to read the binary; it works off the vision description.
3. Since image_generate doesn't take image inputs, the vision description is what gets embedded in prompts during Step 5.

Full procedures: references/workflow.md.

Step 2: Analyze

Analysis	Output
Content type	Technical / Tutorial / Methodology / Narrative
Purpose	information / visualization / imagination
Core arguments	2-5 main points
Positions	Where illustrations add value

Read source (file path → read_file, or pasted text) and write the analysis to {output-dir}/analysis.md using write_file.

Full procedures: references/workflow.md.

Step 3: Confirm Settings

Use the clarify tool. Since clarify handles one question at a time, ask the most important question first. Skip any question whose answer is already present in the user's request.

Order	Question	Options
Q1	Preset or Type	[Recommended preset], [alt preset], or manual: infographic, scene, flowchart, comparison, framework, timeline, mixed
Q2	Density	minimal (1-2), balanced (3-5), per-section (Recommended), rich (6+)
Q3	Style (skip if preset chosen in Q1)	[Recommended], minimal-flat, sci-fi, hand-drawn, editorial, scene, poster
Q4	Palette (optional)	Default (style colors), macaron, warm, neon
Q5	Language (only if article language is ambiguous)	article language / user language

Don't ask more than 2-3 clarify questions in a row. If the user already specified these in their request, skip entirely.

Full procedures: references/workflow.md.

Step 4: Generate Outline → outline.md

Save {output-dir}/outline.md using write_file with frontmatter (type, density, style, palette, image_count) and one entry per illustration:

## Illustration 1
**Position**: [section/paragraph]
**Purpose**: [why]
**Visual Content**: [what to show]
**Filename**: 01-infographic-concept-name.png

Full template: references/workflow.md.

Step 5: Generate Prompts

BLOCKING: Every illustration must have a saved prompt file before any image is generated, the prompt file is the reproducibility record.

For each illustration:

1. Create a prompt file per references/prompt-construction.md.
2. Save to {output-dir}/prompts/NN-{type}-{slug}.md using write_file with YAML frontmatter.
3. Prompts MUST use type-specific templates with structured sections (ZONES / LABELS / COLORS / STYLE / ASPECT).
4. LABELS MUST include article-specific data: actual numbers, terms, metrics, quotes.
5. Process references (direct/style/palette) per prompt frontmatter, for direct usage, embed a textual description of the reference in the prompt (since image_generate doesn't take reference-image inputs).

Step 6: Generate Images

For each prompt file:

1. Call image_generate(prompt=..., aspect_ratio=...). image_generate returns a JSON result containing an image URL; it does NOT write to disk and does NOT accept an output path.
2. Map the prompt's ASPECT to image_generate's enum: 16:9 → landscape, 9:16 → portrait, 1:1 → square. Custom ratios → nearest named aspect.
3. Download the returned URL to {output-dir}/NN-{type}-{slug}.png via terminal (e.g. curl -sSL -o "{output-dir}/NN-{type}-{slug}.png" "{url}").
4. On generation failure, auto-retry once.

Note: the underlying image-generation backend is user-configured (default: FAL FLUX 2 Klein 9B) and is NOT agent-selectable via image_generate. Do not write model names into prompts expecting them to route.

Step 7: Finalize

Insert ![description](https://github.com/NousResearch/hermes-agent/blob/main/optional-skills/creative/baoyu-article-illustrator/{relative-path}/NN-{type}-{slug}.png) after the corresponding paragraph. Alt text: concise description in the article's language.

Report:

Article Illustration Complete!
Article: [path] | Type: [type] | Density: [level] | Style: [style] | Palette: [palette or default]
Images: X/N generated

Modification

Action	Steps
Edit	Update prompt → Regenerate → Update reference
Add	Position → Prompt → Generate → Update outline → Insert
Delete	Delete files → Remove reference → Update outline

References

File	Content
references/workflow.md	Detailed procedures
references/usage.md	Invocation examples
references/styles.md	Style gallery + Palette gallery
references/style-presets.md	Preset shortcuts (type + style + palette)
references/prompt-construction.md	Prompt templates

Pitfalls

1. Data integrity is paramount, never summarize, paraphrase, or alter source statistics. "73% increase" stays "73% increase".
2. Strip secrets, scan source content for API keys, tokens, or credentials before including in any output file.
3. Don't illustrate metaphors literally, visualize the underlying concept.
4. Prompt files are mandatory, no image generation without a saved prompt file. The file is what lets you regenerate or switch backends later.
5. image_generate aspect ratios, the tool supports landscape, portrait, and square. Custom ratios map to the nearest option.
6. image_generate returns a URL, not a local file, always download via terminal (curl) before inserting local image paths into the article.
7. No backend selection from the agent, image_generate uses whatever model the user configured (default: FAL FLUX 2 Klein 9B). Don't write "use to generate this" into prompts expecting it to route.