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Paper Poster Imagegen

Community
LEON-gittech
paper-poster-imagegen

Generate, critique, and iterate image-generation prompts for high-fidelity academic paper poster reference mockups before implementing HTML/PDF/PPTX. Use when Codex is asked to create a poster concept image, imagegen prompt, visual reference draft, layout mockup, or "生图" design for ACL/EMNLP/NeurIPS-style A0 posters from a paper, figures, metrics, logos, QR codes, or an existing poster screenshot.

Overview

PublisherLEON-gittech
RepositorySkill2Poster
Skill namepaper-poster-imagegen
Stars
96
Forks
3
Bundled files
2
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by LEON-gittech on GitHub. Read the source before you install it.

Installation

Install the Paper Poster Imagegen AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/LEON-gittech/Skill2Poster/tree/main/paper-poster-imagegen
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/LEON-gittech/Skill2Poster.git /tmp/Skill2Poster
mkdir -p .claude/skills
cp -r /tmp/Skill2Poster/paper-poster-imagegen .claude/skills/paper-poster-imagegen
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Paper Poster Imagegen in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Paper Poster Imagegen on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Paper Poster Imagegen is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Paper Poster ImageGen

Purpose

Use image generation to create a reference design image that is structurally close to a final editable poster. Do not treat the generated image as the source of truth for text, numbers, equations, or plots. The source of truth remains the paper, local assets, and later HTML/PPT implementation.

The goal is to make the first generated mockup already express:

  • Correct poster logic and reading order.
  • Realistic A0 portrait density.
  • Placeholders for actual paper figures/tables rather than invented visuals.
  • Balanced sections with no obvious overflow or large accidental whitespace.
  • Typography, QR/logo placement, and metric hierarchy close to what can be implemented.

Before Generating

Create a concise poster brief from the paper and assets:

  1. Title, subtitle, authors, affiliations, conference mark.
  2. Four top metric cards: label, value, comparison, icon concept.
  3. One thesis strip sentence.
  4. Section order and one-sentence purpose for each section.
  5. Paper figures/tables that must appear, with preferred placement.
  6. Logos and QR codes that must appear.
  7. Things to avoid from prior drafts or user feedback.

If exact content is not available, use readable placeholders in the prompt rather than inventing false values. For text-heavy elements, ask for "structured text blocks and table-like layouts" and later replace with real HTML/PPT text.

Poster Layout Target

Default to an A0 portrait academic poster with a clean ACL/top-conference style:

  • Canvas aspect: A0 portrait, approximately 841mm x 1189mm or 3179 x 4494 CSS px.
  • Palette: white/light-blue panels, deep navy section headers, restrained green/red accents for results.
  • Header: left conference/logo block, center title/subtitle/authors, right QR codes or institutional marks.
  • Metric strip: four parallel cards with icons, large green numbers, and one-line comparison text.
  • Thesis strip: one centered claim below metrics.
  • Main body: three columns, center column wider for method and mechanism figures.
  • Bottom: one concise conclusion/takeaway section; no metadata footer unless required.

Recommended content flow:

  • Left: motivation, problem/failure mode, method core or design constraints.
  • Center: method overview, key mismatch/diagnostic figure, mechanism evidence.
  • Right: main results, generalization, robustness/sensitivity, conclusion.

Prompting Rules

  • Specify the exact section list and order. Image models otherwise invent sections.
  • Describe panels as stable rectangles with navy headers and consistent spacing.
  • Ask for real paper-figure screenshots or provided figure thumbnails to be placed as framed images, not redrawn from scratch.
  • Preserve coherent figures. Do not ask the model to split or crop a figure just to fill space unless the crop remains complete and intentional.
  • Do not ask for small dense paragraphs everywhere. Use large readable text blocks and figure/table placeholders.
  • Keep fonts moderate-weight and professional. Avoid comic, handwritten, overly heavy display text, and tiny author/affiliation text.
  • Make the generated mockup balanced but not overly packed. If blank space exists, fill it by enlarging important figures or adding paper-backed context, not generic text.
  • QR codes should be large enough to scan and have simple captions when useful.
  • Every chart/table-like area must have a clear title or caption explaining what it measures.
  • No decorative footers, random contact strips, unindexed extra boxes, or duplicated result tables.

Image Model Limitations

Expect image models to distort exact text, equations, QR codes, and table numbers. Therefore:

  • Use generated output for layout, hierarchy, and visual density.
  • Replace text, figures, equations, QR codes, and logos with real assets in HTML/PPT.
  • Do not accept generated numbers or baselines as factual.
  • Do not judge a poster solely by aesthetic polish; verify whether the structure can be implemented.

Iteration Workflow

  1. Generate one reference mockup from the structured prompt.
  2. Compare it against the paper and target implementation constraints.
  3. Identify failure types:
    • Missing original paper figures.
    • Coherent figure split/cropped incorrectly.
    • Section order or numbering is illogical.
    • Text too small or too heavy.
    • QR/logos too small or misplaced.
    • Large whitespace handled by generic filler text.
    • Result table duplicated or chart lacks explanation.
  4. Revise the prompt with explicit negative constraints and section sizing.
  5. Only after layout is credible, implement in HTML/PPT with real assets.

For a copy-ready prompt template and review checklist, read references/prompt-template.md.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Paper Poster Imagegen AI skill do?

Generate, critique, and iterate image-generation prompts for high-fidelity academic paper poster reference mockups before implementing HTML/PDF/PPTX. Use when Codex is asked to create a poster concept image, imagegen prompt, visual reference draft, layout mockup, or "生图" design for ACL/EMNLP/NeurIPS-style A0 posters from a paper, figures, metrics, logos, QR codes, or an existing poster screenshot.

Why use Paper Poster Imagegen on TypingMind?

Because you install it once and use it with any model. Paper Poster Imagegen is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Paper Poster Imagegen in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/LEON-gittech/Skill2Poster/tree/main/paper-poster-imagegen. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Paper Poster Imagegen?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Paper Poster Imagegen?

As many as you like. As long as a model supports skills, you can use Paper Poster Imagegen with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Paper Poster Imagegen AI skill free?

It is published on GitHub by LEON-gittech. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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