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leon-gittech/skill2poster

18Audience · researcherComplexity · 2/5Setup · moderate

TLDR

A two-step AI workflow for making academic conference posters: first generate a visual layout mockup with an image generator, then rebuild it accurately as HTML using real text and figures from your paper.

Mindmap

mindmap
  root((skill2poster))
    Step 1 Design
      AI image generator
      Layout prompt
      Visual mockup
    Step 2 Build
      HTML export
      A0 dimensions
      Real figures
    Quality checks
      Font readability
      Column alignment
      Figure cropping
    Audience
      Researchers
      Conference speakers
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Code map

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Things people build with this

USE CASE 1

Create an A0-format HTML conference poster from your research paper with accurate figures, equations, and text.

USE CASE 2

Generate a visual layout mockup to iterate on poster design before committing to building the real HTML poster.

USE CASE 3

Use the included quality checklist to review your poster for panel overflow, font size, and alignment issues before printing.

Tech stack

YAMLHTMLOpenAI

Getting it running

Difficulty · moderate Time to first run · 30min

Requires an AI agent environment compatible with OpenAI-format tooling to run the YAML skill files.

In plain English

Skill2Poster is a two-step workflow for creating academic conference posters from research papers. It is written primarily in Chinese but the concepts are straightforward: first use an AI image generator to quickly explore layout options, then rebuild the poster in HTML using real text, figures, and data from the actual paper. The repository provides two reusable agent skills that guide each step, along with example output images. The motivation behind the split approach is that AI image generators are good at producing visually coherent poster layouts but tend to distort text, numbers, equations, tables, and QR codes. Using a generated image as the final product would give you a poster that looks polished but contains fabricated content. Skill2Poster treats the generated image as a design sketch only and then rebuilds everything accurately in HTML. The first skill, paper-poster-imagegen, helps you construct a detailed prompt describing the poster's structure: section order, metric cards at the top, a thesis statement strip, and placeholder areas for real figures. The output is a visual mockup showing what the layout should look like. If the mockup has problems such as unreadable tiny text or invented charts, you adjust the prompt and regenerate before moving on. The second skill, academic-poster-builder, uses that mockup as a reference and constructs the actual poster as an HTML file sized to A0 dimensions (the standard large-format size for academic conferences). All text, figures, tables, equations, logos, and QR codes come from the paper's own materials rather than from the image generator. The skill includes a quality checklist covering panel overflow, font readability, column alignment, and figure cropping. The repository includes example images showing both the design reference and the finished HTML export. No scripts or application code are included, the skills are YAML-defined agent configurations intended to work with OpenAI-compatible tooling.

Copy-paste prompts

Prompt 1
Using the paper-poster-imagegen skill, help me write a prompt to generate a visual layout for a machine learning paper poster with a title at top, 3 metric cards, a method diagram in the center, and a results table at the bottom.
Prompt 2
I have a visual mockup of my conference poster. Use the academic-poster-builder skill to help me create an A0-sized HTML poster for a paper with four sections: Introduction, Method, Experiments, and Conclusion.
Prompt 3
My AI-generated poster mockup has unreadable tiny text in the metrics section. Help me rewrite the paper-poster-imagegen prompt to fix this issue and regenerate a better layout reference.
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