---
title: "paper-figure"
description: "Design, refine and rebuild scientific figures in one Codex conversation. Editable SVG, PDF and PPTX."
canonical: https://agentpluginsdirectory.com/plugins/paper-figure
last-updated: 2026-09-11
---

# paper-figure
Design, refine and rebuild scientific figures in one Codex conversation. Editable SVG, PDF and PPTX.
- Slug: paper-figure
- Publisher: GuangSTrip
- Repository: https://github.com/GuangSTrip/paper-figure
- Manifest: plugin.json
- Version: 2.1.1
- License: MIT
- Category (editorial): other
- Skills: 1 (paper-figure)
- MCP servers: 0
- Stars: 1
- Repository created: 2026-09-09
- Repository last pushed: 2026-09-11
- Publisher type: User
- Listing: https://agentpluginsdirectory.com/plugins/paper-figure
- Schema: https://agent-plugins.org/schemas/1.0.0/plugin.schema.json

## What paper-figure does, in the publisher's words

Describe the figure. Refine a preview. Say "use this". Get editable SVG, PDF and PPTX.

Paper Figure is a small Codex skill backed by local build tools. It keeps design, confirmation and reconstruction in one conversation, without asking you to fill forms, move files between chats, or configure a separate image API.

> v2.1.1 · Preview release. Local export, state and safety checks are tested. > Native Codex image generation and arbitrary scientific-figure reconstruction > are not covered by these automated tests. A host image tool is required for > generated concepts; this skill cannot grant a tool the client does not provide.

From the project README, punctuation lightly normalized. Full text: https://raw.githubusercontent.com/GuangSTrip/paper-figure/HEAD/README.md

## Skills

- paper-figure: Create and revise scientific paper figures from intent, sketches or reference images. Iterate on concepts in Codex, record the user's approval, and rebuild editable SVG, PDF and PPTX from one source. Use for method diagrams, spatial-reasoning illustrations, paper figure reconstruction and local fig…

Descriptions come from the frontmatter of each SKILL.md, punctuation lightly normalized.