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Paperbanana

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PlutoLei
paperbanana

Generate and revise academic figures, data plots, or image slides through a configured PaperBanana backend; inspect their automated review status. Use for PaperBanana requests and academic image generation, not general presentation editing or code-only plotting.

Overview

PublisherPlutoLei
Repositorypaperbanana-skill
Skill namepaperbanana
Stars
60
Forks
3
Bundled files
1
LicenseMIT
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.

  • 1 bundled files

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

  • Open source

    Published by PlutoLei on GitHub. Read the source before you install it.

Installation

Install the Paperbanana 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/PlutoLei/paperbanana-skill/tree/master/plugins/paperbanana/skills/paperbanana
  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/PlutoLei/paperbanana-skill.git /tmp/paperbanana-skill
mkdir -p .claude/skills
cp -r /tmp/paperbanana-skill/plugins/paperbanana/skills/paperbanana .claude/skills/paperbanana
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Paperbanana 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 Paperbanana 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 Paperbanana 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.

PaperBanana

Translate a research brief into an image-generation task, then inspect both the output and its review. For a complete deck or native editable PowerPoint, use the separately installed paperbanana-slide-deck skill when available.

Establish the task

  1. Identify the requested output, input material, destination and existing run. For a review or plan-only request, return findings without creating files or calling generation tools. Reuse the user's approved choices.
  2. Preserve explicit and saved image-provider/model choices, including Gemini or Vertex configuration. The agent host does not select the image provider. If no choice exists, use the configured backend; ask only if an actual choice is missing. Historical examples do not establish a current model ranking.
  3. Locate the user-selected Python backend/environment. Check the relevant command's --help before using version-dependent flags. Do not scan another agent's private directories or read credentials into the conversation.
  4. Separate native PowerPoint editing from raster image generation. A screenshot or embedded SVG is not a native editable text box, table or chart.

Choose the command

IntentCommandRequired material
Method, architecture or workflow figuregenerateSource text/PDF and communicative intent
Statistical plotplotActual CSV/JSON data and plot intent
One image slideslideSlide prompt
Several image slidesslide-batchPrompt directory
Compare an image with a referenceevaluateBoth images, source context and caption
Dataset information/download/removaldataRequested operation; removal needs its own scope
Provider setupsetupA configuration request; use the host's credential workflow

See command examples for invocation patterns. Use the selected checkout's help as the CLI authority. Code-only plotting requests can use the host's plotting tools without an image-model call.

Image-provider flags use factory names such as google_imagen, openai_imagen, bedrock_imagen and openrouter_imagen. VLM flags use names such as gemini, openai and anthropic. Verify names against the installed version.

Generate or revise

  • Keep supplied labels, arrows, quantities and scientific meaning traceable to the input. Mark invented example data as illustrative. Do not repair missing research facts by making them up. For mixed-language figures, carry an explicit list of required labels into the prompt and check them in the returned image.
  • Write inline input into a task-owned text file when execution is authorized. Pass file paths as quoted arguments; do not interpolate user prose into shell commands. Keep the original input and references intact.
  • Use the authorized provider and iteration limits. Do not add paid optimization, a provider comparison, a large dataset download or an unbounded retry loop to a request that did not authorize it. Reuse existing authorization within scope.
  • For edits, identify the exact prior run/image and reference files first. Use that command's supported continuation/edit mechanism. If the backend lacks reference editing, explain the limitation before substituting regeneration.
  • For a single changed slide, regenerate that slide and reassemble the deck; preserve unrelated pages and source prompts. A failed batch does not authorize discarding successful pages or regenerating the entire deck.

Recover without changing the task

Observed resultNext action
Rate limit or temporary service errorUse the backend's bounded retry policy within the approved limit; report exhausted attempts
Authentication/configuration errorStop that call and report the required setup; do not print keys or try another account
Timeout with uncertain generation outcomeInspect the run and existing output before retrying, to avoid duplicate work/charges
Requested provider unavailableKeep the selection; present a concrete alternative only if a new choice is needed
Critic timeout, malformed output or unavailable reviewPreserve the image and mark it UNREVIEWED; never treat missing feedback as acceptance
Partial batchList successful/missing pages and resume only the missing or requested pages using supported commands

Do not switch providers merely because a fallback credential exists. A retry or fallback must preserve the user's routing and spending choices. Continuation flags are command-specific: generate --continue-run is not evidence that slide-batch --continue exists.

Inspect and deliver

Open the actual output when the host has an image viewer. Check requested labels, connections, numbers, cropping and legibility. If it cannot be inspected, say so. Report the artifact, run identifier when available, provider/model only as actually known, and review state separately. A successful API call, a Critic score and scientific acceptance are different claims.

When revisions are needed, identify the concrete defect and revise the affected input/run within scope. Do not repeatedly rerun generation without a new diagnosis. Keep failures and unreviewed pages visible in the final report.

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 Paperbanana AI skill do?

Generate and revise academic figures, data plots, or image slides through a configured PaperBanana backend; inspect their automated review status. Use for PaperBanana requests and academic image generation, not general presentation editing or code-only plotting.

Why use Paperbanana on TypingMind?

Because you install it once and use it with any model. Paperbanana 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 Paperbanana in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/PlutoLei/paperbanana-skill/tree/master/plugins/paperbanana/skills/paperbanana. 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 Paperbanana?

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 Paperbanana?

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

Is the Paperbanana AI skill free?

Yes. It is published on GitHub by PlutoLei under the MIT license. 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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