article-poster

name	article-poster
description	Generate a beautifully designed infographic poster from an article URL or text content. Trigger when user says "article poster", "文章海报", "infographic", "信息图", "make a poster", "生成海报", "visual summary", or requests to convert an article/blog into a shareable image. NOT for generic poster design.
user-invocable	true
argument-hint	文章URL或文本内容

Article Poster

Converts an article (URL or pasted text) into a beautifully designed infographic poster image.

How It Works

1. Read the article — Fetch URL via web_fetch or use provided text
2. Summarize into JSON — Output a compact JSON data structure (see schema below)
3. Render & screenshot — Run render.py which injects JSON into template.html and screenshots to PNG

This design minimizes token cost: the HTML/CSS template is a static asset (~0 generation tokens). You only generate the JSON content.

Step 1: Read the Article

• If given a URL: use web_fetch to retrieve content
• If given textual or image content: use it directly
• Read and deeply understand the article before summarizing

Step 2: Save Poster Data as JSON

Write the poster data to workspace/outputs/posters/poster_data.json using the Write tool. Do NOT generate a Python script — just write the JSON file directly.

IMPORTANT: Do NOT create a gen_poster.py or any intermediate HTML generation script. render.py handles template injection internally. Your only job is to write poster_data.json.

Follow this schema exactly:

{
  "source": "ANTHROPIC ENGINEERING",
  "category": "HARNESS DESIGN",
  "title": "Harness 设计：突破 Agent 编码前沿的关键架构",
  "subtitle": "Anthropic 用一套受 GAN 启发的多智能体架构……核心不是更聪明的单 Agent，而是如何为强模型设计正确的 harness。",
  "layout": "single",
  "sections": [
    {
      "number": 1,
      "color": "brown",
      "title": "为什么简单方案会失效？",
      "cards": [
        {
          "type": "text",
          "heading": "上下文焦虑",
          "body": "上下文一满，模型就会倾向于提前结束工作……"
        },
        {
          "type": "text",
          "heading": "自我评估偏差",
          "body": "让模型评价自己的产出时……"
        }
      ]
    },
    {
      "number": 2,
      "color": "olive",
      "title": "架构核心：生成器 / 评估器分离",
      "cards": [
        {
          "type": "highlight",
          "heading": "受 GAN 启发，但不只是`多加一个 Agent`",
          "body": "Anthropic 把`创造`与`评判`彻底解耦……"
        }
      ]
    },
    {
      "number": 4,
      "color": "teal",
      "title": "全栈开发 V1：三智能体分工",
      "cards": [
        {
          "type": "tags",
          "items": [
            { "tag": "Planner", "text": "把 1-4 句需求扩展成完整产品规格……" },
            { "tag": "Generator", "text": "按 Sprint 逐步实现……" },
            { "tag": "Evaluator", "text": "通过 Playwright 测运行中的应用……" }
          ]
        }
      ]
    },
    {
      "number": 5,
      "color": "amber",
      "title": "架构演进：从复杂到精简",
      "cards": [
        {
          "type": "compare",
          "left": { "heading": "V1：重型评估架构", "body": "单个 DAW 应用约 6 小时 / $200……" },
          "right": { "heading": "V2：Claude 4.6 后减负", "body": "去掉 Sprint、把评估器后置……" }
        }
      ]
    },
    {
      "number": 6,
      "color": "sage",
      "title": "深层方法论",
      "cards": [
        {
          "type": "bullets",
          "items": [
            "**主观质量的可操作化：** 把`这设计好吗`转成`是否符合设计原则`。",
            "**结构化产物传递：** 重置上下文时，用文件而不是对话历史传状态。",
            "**持续压力测试：** 每个组件都编码了对模型能力的假设，而这些假设会随新模型迭代失效。"
          ]
        },
        {
          "type": "callout",
          "label": "关键洞察",
          "body": "评估器是否必要，不取决于`它是不是好设计`，而取决于任务是否仍处在模型能力边界之外。"
        }
      ]
    }
  ]
}

JSON Schema Rules

layout (optional) — Layout mode:

• "single": Single column layout (default)
• "double": Two column layout (odd sections left, even sections right)

sections[] — Any number of sections based on content needs. Each section has:

• number: Display number (1-based)
• color: One of brown, olive, terracotta, teal, amber, sage, slate, rose
• title: Section heading (concise, < 20 chars ideal)
• cards[]: Array of cards. Card types:

type	fields	description
text	heading, body	Simple card with title + paragraph
highlight	heading, body	Card with colored left border accent
tags	items[{tag, text}]	Labeled tag + description list (like Planner/Generator/Evaluator)
compare	left{heading,body}, right{heading,body}	Side-by-side comparison boxes
bullets	items[]	Bullet list (supports bold: prefix)
callout	label, body	Highlighted callout box with label badge

Content Guidelines

• Language: Match the user's language. Default Chinese for Chinese users.
• Conciseness: Each body should be 1-3 sentences. This is a poster, not a blog.
• Bold keywords: Use **keyword** for emphasis in body text.
• Section count: Use as many sections as needed to cover the content effectively (typically 3-10).
• Layout: Choose "single" for focused content or "double" for side-by-side comparison.
• Chinese punctuation: Use full-width punctuation for Chinese content (，、。：！？""）.

Width Selection

Choose width based on layout:

• narrow (1080px): Single column, mobile-friendly
• medium (1200px): Single or double column, balanced (DEFAULT)
• wide (1600px): Double column, desktop-optimized

Height adjusts automatically based on content.

Step 3: Render to PNG

Run render.py with the exact flags below — do NOT guess flags, do NOT use --input or --width:

cd ~/.agentara/workspace/outputs/posters && \
python3 ~/.claude/skills/article-poster/render.py \
  --data poster_data.json \
  --output poster.png \
  --ratio medium

Valid flags: --data (JSON file path), --output (PNG path), --ratio (narrow|medium|wide). Never use: --input, --width, --template unless you have a specific reason.

If Playwright is not installed, present the poster.html generated by render.py to the user for manual screenshot.

Token Budget

The entire generation cost is just the JSON (~300-600 tokens). The template HTML (~8KB) and render script are static assets loaded from disk — zero generation tokens. This makes the skill extremely efficient compared to generating full HTML each time.