infographic
Generate high-density infographics with structured layout and style choices and bundled generation tooling. Use when the user asks to create an infographic, a visual summary, or a dense single-page information graphic.
Best use case
infographic is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Generate high-density infographics with structured layout and style choices and bundled generation tooling. Use when the user asks to create an infographic, a visual summary, or a dense single-page information graphic.
Teams using infographic should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/infographic/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How infographic Compares
| Feature / Agent | infographic | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Generate high-density infographics with structured layout and style choices and bundled generation tooling. Use when the user asks to create an infographic, a visual summary, or a dense single-page information graphic.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
Best AI Skills for ChatGPT
Find the best AI skills to adapt into ChatGPT workflows for research, writing, summarization, planning, and repeatable assistant tasks.
AI Agents for Marketing
Discover AI agents for marketing workflows, from SEO and content production to campaign research, outreach, and analytics.
AI Agents for Startups
Explore AI agent skills for startup validation, product research, growth experiments, documentation, and fast execution with small teams.
SKILL.md Source
# Infographic Generation (`infographic`)
## Reference Images (Important)
If you use reference images (image-to-image / series reference / consistency refs):
- Reference images must be public URLs.
- **HTTPS is strongly recommended.**
- `http://` may work but is insecure and can be blocked by some networks.
- Local file paths and `data:` URLs are not supported by the WeryAI gateway.
This skill turns complex source material into a single high-density infographic.
Maintain the layout and style mapping when the bundled generation runtime updates, and re-check recent runtime behavior if a newer version changes prompt handling.
Script:
- `scripts/scaffold.ts`
- `scripts/build-prompt.ts`
- `scripts/build-batch.ts`
## Safety & Scope
- **Network**: This skill calls the WeryAI gateway over HTTPS (`https://api.weryai.com`).
- **Auth**: Uses `IMAGE_GEN_API_KEY`. The key is never printed. It may be persisted **only** when you explicitly run `npm run setup -- --persist-api-key`.
- **Reference images**: Must be public URLs (`https://` recommended). `http://` may work but is insecure. Local file paths and `data:` URLs are rejected.
- **No arbitrary shell**: The generation runtime does not execute arbitrary shell commands.
- **Files written**: Output images and optional local config under `.image-skills/infographic/` (project) and/or `~/.image-skills/infographic/` (home).
## Use Cases
- high-density information graphics
- visual summaries
- data relationship graphics
- process posters
- content that needs to be explained in one image
Not a good fit for:
- a single article cover image
- multi-card RedNote content
- a sequential comic narrative
## Core Dimensions
1. `layout`: the information structure
2. `style`: the visual language
See:
- [references/analysis-framework.md](references/analysis-framework.md)
- [references/structured-content-template.md](references/structured-content-template.md)
- [references/layouts-and-styles.md](references/layouts-and-styles.md)
- [references/prompt-template.md](references/prompt-template.md)
## Commands
| Script | Purpose |
| --- | --- |
| `scripts/scaffold.ts` | Initialize `analysis.md`, `structured-content.md`, and prompts |
| `scripts/build-batch.ts` | Generate `batch.json` from prompt variants |
| `scripts/build-prompt.ts` | Build final prompt from `structured-content.md` |
| `npm run generate` | Generate the infographic |
| `./scripts/vendor/compression-runtime/scripts/main.ts` | Compress output for delivery |
## Workflow
### Step 1: Initialize Working Files
Create the working directory and starter files:
```bash
${BUN_X} {baseDir}/scripts/scaffold.ts \
--output-dir infographic/topic-slug \
--topic "Topic name" \
--lang en
```
This creates:
- `analysis.md`
- `structured-content.md`
- `prompts/infographic.md`
### Step 2: Understand the Source
Extract:
- the main topic
- the target audience
- the density of information
- whether the content is driven by data, lists, hierarchy, process, or comparison
- the user's language, especially for titles, labels, and annotations
Save the result to `analysis.md`, using:
- [references/analysis-framework.md](references/analysis-framework.md)
### Step 3: Organize Structured Content
Before selecting the final layout and style, rewrite the material into `structured-content.md`:
- [references/structured-content-template.md](references/structured-content-template.md)
This step should make titles, sections, labels, and data points explicit.
### Step 4: Choose `layout` and `style`
Default priorities:
- `layout`: `bento-grid`
- `style`: `infographic`
Recommended rules:
- step-by-step tutorial -> `linear-progression`
- comparison -> `binary-comparison`
- hierarchy -> `hierarchical-layers`
- broad knowledge overview -> `bento-grid`
- dense modular content -> `dense-modules`
If the user explicitly asks for a specific visual direction, follow that preference.
### Step 5: Build `batch.json` for Variants
If you want to explore multiple infographic directions, place multiple prompt files in `prompts/`, for example:
- `01-technical.md`
- `02-minimal.md`
- `03-bold.md`
Then generate a batch file:
```bash
${BUN_X} {baseDir}/scripts/build-batch.ts \
--prompts infographic/topic-slug/prompts \
--output infographic/topic-slug/batch.json \
--images-dir infographic/topic-slug \
--model "$M" \
--jobs 3
```
The script reads `Style direction:` and `Aspect ratio:` from each prompt file when possible, then maps them into generation task fields.
### Step 6: Build the Final Prompt
Convert `structured-content.md` into the final prompt:
```bash
${BUN_X} {baseDir}/scripts/build-prompt.ts \
--structured-content infographic/topic-slug/structured-content.md \
--output infographic/topic-slug/prompts/infographic.md \
--audience "product teams" \
--style technical-schematic \
--aspect 4:3 \
--lang en
```
This keeps the prompt synchronized with the structured content instead of rewriting it by hand.
### Step 7: Map to the Bundled Runtime
The bundled image runtime currently exposes one structured style argument, `--style`, so:
- map `style` to `--style`
- write `layout`, labels, hierarchy, and information relationships into the prompt body
- prefer `3:4`, `4:3`, or `16:9` depending on content shape
Recommended mapping:
| infographic style | runtime `--style` |
| --- | --- |
| `craft-handmade` | `watercolor` |
| `chalkboard` | `chalk` |
| `corporate-memphis` | `flat-illustration` |
| `technical-schematic` | `infographic` |
| `bold-graphic` | `poster` |
| `storybook-watercolor` | `watercolor` |
| `retro-pop-grid` | `poster` |
| `minimal` | `editorial` |
Use [references/prompt-template.md](references/prompt-template.md) and make sure to:
- define the main title, subtitle, sections, and labels explicitly
- include numbers, years, and terminology directly when possible
- emphasize hierarchy, whitespace, and readability instead of abstract style-only wording
- state the target language explicitly if the image needs on-canvas text
### Step 8: Run Generation
On first use in a new project, run `npm run ensure-ready -- --project <your-project> --workflow infographic` from this skill directory before generation. This reads the doctor report and auto-runs `bootstrap` if local script dependencies are still missing. If the report shows a missing `IMAGE_GEN_API_KEY` and the user approves, run `npm run setup -- --project <your-project> --workflow infographic --persist-api-key` when the key is already in env, or persist it to `.image-skills/infographic/.env` on the user's behalf, then continue without leaving this workflow.
When this skill is first connected, tell the user that the default generation model is **Nano Banana 2** (`GEMINI_3_1_FLASH_IMAGE`). Also tell them it can be switched later whenever another model fits the task better.
```bash
${BUN_X} {baseDir}/npm run generate \
--promptfiles prompts/infographic.md \
--style infographic \
--image infographic/topic-slug/infographic.png \
--ar 3:4 \
-m "$M"
```
Batch example:
```bash
${BUN_X} {baseDir}/npm run generate \
--batchfile infographic/topic-slug/batch.json \
--jobs 3
```
If the user has not chosen a model yet, follow this skill's model-selection rules first.
## Output Convention
Suggested output directory:
```text
infographic/<topic-slug>/
```
Suggested minimum files:
- `analysis.md`
- `structured-content.md`
- `prompts/infographic.md`
- `batch.json`
- `infographic.png`
## Re-run Behavior
- `scaffold.ts` on an existing directory overwrites `analysis.md`, `structured-content.md`, and the starter prompt files.
- `build-prompt.ts` overwrites the target prompt file each time it runs.
- `build-batch.ts` overwrites `batch.json`.
- Re-running the base generator with the same `--image` path overwrites the existing infographic output.
- If multiple prompt variants are listed in `batch.json`, re-running the batch regenerates all listed variants unless you remove the ones you want to keep.
## Definition of Done
- `analysis.md`, `structured-content.md`, and `prompts/infographic.md` exist in the output directory.
- The generated infographic matches the chosen `layout / style / aspect`.
- The image is shown directly to the user with a summary of the parameters used.
- A compressed webp version is produced for delivery.
## Iteration
When the user wants changes after seeing the generated infographic:
- **Layout issues** ("too crowded", "unclear hierarchy") → switch `layout` in `structured-content.md` and rebuild the prompt. Try a different layout pattern.
- **Style mismatch** ("too busy", "not professional enough") → change `style` / `--style`, re-generate.
- **Content issues** ("missing X", "remove this section") → revise `structured-content.md`, rebuild prompt with `build-prompt.ts`, re-generate.
- **Text readability** ("text too small", "can't read it") → reduce information density in the prompt, or switch to a less text-heavy layout.
- **Want to explore alternatives** → create multiple prompt variants in `prompts/`, batch-generate to compare layouts and styles.
Infographics are single-image outputs, so each iteration only re-generates one image.
## Delivery
When the infographic is ready:
1. **Show the image directly** — do not just print a file path.
2. Briefly state: layout type, style, aspect ratio.
3. Ask if the user wants changes (layout, content, style) or is satisfied.
4. **Auto-compress**: once confirmed, run the bundled compression runtime to produce a webp version.
```bash
${BUN_X} {baseDir}/./scripts/vendor/compression-runtime/scripts/main.ts infographic/topic-slug/infographic.png -f webp -q 85
```
If multiple variants were generated via batch, show all of them and let the user pick the best one.
Internal checklist (for agent): `layout / style / aspect`, information division, model, text language, compression done.Related Skills
---
name: article-factory-wechat
humanizer
Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.
find-skills
Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.
tavily-search
Use Tavily API for real-time web search and content extraction. Use when: user needs real-time web search results, research, or current information from the web. Requires Tavily API key.
baidu-search
Search the web using Baidu AI Search Engine (BDSE). Use for live information, documentation, or research topics.
agent-autonomy-kit
Stop waiting for prompts. Keep working.
Meeting Prep
Never walk into a meeting unprepared again. Your agent researches all attendees before calendar events—pulling LinkedIn profiles, recent company news, mutual connections, and conversation starters. Generates a briefing doc with talking points, icebreakers, and context so you show up informed and confident. Triggered automatically before meetings or on-demand. Configure research depth, advance timing, and output format. Walking into meetings blind is amateur hour—missed connections, generic small talk, zero leverage. Use when setting up meeting intelligence, researching specific attendees, generating pre-meeting briefs, or automating your prep workflow.
self-improvement
Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Claude ('No, that's wrong...', 'Actually...'), (3) User requests a capability that doesn't exist, (4) An external API or tool fails, (5) Claude realizes its knowledge is outdated or incorrect, (6) A better approach is discovered for a recurring task. Also review learnings before major tasks.
botlearn-healthcheck
botlearn-healthcheck — BotLearn autonomous health inspector for OpenClaw instances across 5 domains (hardware, config, security, skills, autonomy); triggers on system check, health report, diagnostics, or scheduled heartbeat inspection.
linkedin-cli
A bird-like LinkedIn CLI for searching profiles, checking messages, and summarizing your feed using session cookies.
notebooklm
Google NotebookLM 非官方 Python API 的 OpenClaw Skill。支持内容生成(播客、视频、幻灯片、测验、思维导图等)、文档管理和研究自动化。当用户需要使用 NotebookLM 生成音频概述、视频、学习材料或管理知识库时触发。
小红书长图文发布 Skill
## 概述