Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add aAAaqwq/AGI-Super-Team --skill article-to-infographicgit clone --depth 1 https://github.com/aAAaqwq/AGI-Super-TeamWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/aaaaqwq/agi-super-team/article-to-infographic)<a href="https://agentmods.dev/skills/aaaaqwq/agi-super-team/article-to-infographic"><img src="https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/article-to-infographic/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/aaaaqwq/agi-super-team/article-to-infographic"><img src="https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/article-to-infographic.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00144 | $0.01818 |
| Opus 5 | $0.00072 | $0.00909 |
| Sonnet 5 | $0.00029 | $0.00364 |
| Haiku 4.5 | $0.00014 | $0.00182 |
Grade A, and why
article-to-infographic scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 163 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Article → Knowledge Visual Notes
将深度长文 / 测评 / 行业分析自动转化为 3-5 张高信息密度「视觉笔记图」的 Prompt 生成器。
核心能力
- 知识拆解：提取黄金标题、对标矩阵、核心逻辑、趋势演进
- 分镜规划：合理分配内容到 3-5 张图中
- 高阶 Prompt 生成：每张图 4 模块英文 Prompt
- 风格定制：支持手写笔记 / 思维导图 / 架构图 / 对比矩阵
触发词
知识笔记 / 文章转图 / 思维导图 / 视觉笔记 / 长文转图 / 知识卡片 / 文章可视化 / 生成笔记图 / article to infographic / knowledge note
使用方法
# 1. 从文章 Markdown 生成提示词套图
python3 ~/.openclaw/skills/article-to-infographic/scripts/plan_visual_notes.py \
--article ~/articles/my-article.md \
--style hand-written \
--count 4 \
--output ~/output/prompts/
# 2. 从纯文本输入
python3 ~/.openclaw/skills/article-to-infographic/scripts/plan_visual_notes.py \
--text "$(cat article.txt)" \
--style mixed \
--output ~/output/
# 3. 生成 Prompt 后接力生图
python3 ~/.openclaw/skills/vectronode-image/scripts/vectronode_image.py generate \
--prompt "$(cat ~/output/prompts/frame-01-prompt.txt)" \
--output ~/output/images/frame-01.png \
--size 1536x1024
参数
| Flag | 说明 | 默认 |
|---|---|---|
--article |
文章文件路径 | — |
--text |
纯文本输入 | — |
--style |
视觉风格 (见下方) | mixed |
--count |
图片数量 3-5 | 4 |
--output |
输出目录 | ./visual-notes/ |
四种视觉风格
| 风格 | --style |
视觉形态 |
|---|---|---|
| 手写笔记 | hand-written |
手机屏幕/纸张上的高密度手写图表，不同颜色笔迹 |
| 思维导图 | mind-map |
手绘感白板/平板思维导图，中心放射结构 |
| 架构/逻辑图 | architecture |
专业系统演算图，带批注和连接线 |
| 对比矩阵 | comparison |
高对比度参数对比表，重点标红 |
| 混合风格 | mixed |
自动为每张图选最优风格 |
分镜规划原则
| 图序 | 内容定位 | 风格倾向 |
|---|---|---|
| 图1 | 核心概念/黄金标题/定调 | 手写笔记/标题页 |
| 图2 | 核心竞争力/数据矩阵 | 对比矩阵/数据图表 |
| 图3 | 逻辑拆解/竞争分析 | 思维导图/双边对比 |
| 图4 | 趋势演进/成本分析/总结 | 架构图/手写红框总结 |
| 图5（可选） | 金句/行动建议 | 手写笔记 |
Prompt 四模块结构
每个生成的 Prompt 严格包含：
[模块1] Main Description — 环境、视角、材质、光线
[模块2] Content & Layout — 精确的文字位置、排版层级、颜色
[模块3] Context & Environment — 真实感环境细节
[模块4] Quality & Style — 画质、风格、技术关键词
详见 Prompt 模板。
工作流示例
以 Claude Fable 5 测评文章为例：
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 163 lines · 144 tokens per session scan A 0a4ae8c28a15
article-to-infographic is a skill published in the GitHub repository aAAaqwq/AGI-Super-Team (91 stars, last pushed yesterday), licensed MIT. It adds 144 tokens to every session and 1,818 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-05.
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