---
title: "redcube-ai"
description: "Image-first visual content design, generation, review, and delivery."
canonical: https://agentpluginsdirectory.com/plugins/redcube-ai
last-updated: 2026-09-25
---

# redcube-ai
Image-first visual content design, generation, review, and delivery.
- Slug: redcube-ai
- Publisher: Gao Feng
- Repository: https://github.com/gaofeng21cn/redcube-ai
- Manifest: plugins/redcube-ai/plugin.json
- Version: 0.2.20
- License: Apache-2.0
- Category (editorial): other
- Skills: 1 (redcube-ai)
- MCP servers: 0
- Stars: 4
- Repository created: 2026-03-22
- Repository last pushed: 2026-09-23
- Publisher type: User
- Listing: https://agentpluginsdirectory.com/plugins/redcube-ai
- Schema: https://agent-plugins.org/schemas/1.0.0/plugin.schema.json

## What redcube-ai does, in the publisher's words

An AI workspace for formal visual deliverables, keeping source material, drafts, review, revisions, and exported files on one traceable delivery line. Slides · Xiaohongshu Notes · Posters

When a task moves from "make a few images" to "produce a visual deliverable I can actually use," the hard part is usually the full workflow, not one page:

- Source material, notes, screenshots, reference images, and old drafts are scattered. How do they become one coherent deliverable?
- After many generated versions, which review comments were addressed, and which version should be rerun?
- Slides, Xiaohongshu notes, and posters need different routes. Can the system choose the right creation path for the deliverable type?
- During longer generation, review, and export runs, can the user still understand what is happening?

From the project README, punctuation lightly normalized. Full text: https://raw.githubusercontent.com/gaofeng21cn/redcube-ai/HEAD/README.md

## Skills

- redcube-ai: Use when Codex needs RedCube AI (RCA) to create, revise, review, or package a visual deliverable such as a slide deck/PPT, poster, social visual, or visual handoff.

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