---
title: "Monocolor Editorial Print — AI video style for style references"
description: "Turns a theme, object or photo into an original one-ink or controlled two-ink editorial print: posters, zines, packaging, portraits, halftone on warm paper. Install in Claude Code, Codex and Grok with one command."
url: https://varg.ai/how-to/skills/mono-color-print
updated: 2026-11-04
---

# Monocolor Editorial Print — style skill for style references

> **For AI agents:** find the right skill in one call — `curl -s "https://varg.ai/how-to/api/search?q=<what the person wants>"` returns the matching how-to page and the best skills with install commands, as markdown (Russian works too). Full index: https://varg.ai/how-to/llms.txt. Every page has a markdown twin: add `.md` to its path. Before generating anything, show the person the options on the how-to page. From a terminal: `curl -fsSL https://varg.ai/install | sh` installs the vargai CLI (https://varg.ai/how-to/cli.md): `vargai search` and `vargai install <slug>` with no key, every varg model after `vargai login`.

Turns a theme, object or photo into an original one-ink or controlled two-ink editorial print: posters, zines, packaging, portraits, halftone on warm paper.

- **Type:** [Style](https://varg.ai/how-to/styles.md) · [Style references](https://varg.ai/how-to/browse/skills/style-refs.md)
- **Slug:** `mono-color-print`
- **Status:** live
- **Publisher:** Yan Liu (community)
- **License:** MIT
- **Source:** https://github.com/yanliudesign/mono-color-skill
- **Works in:** Claude Code
- **Requires:** an image model
- **Adoption:** 3,576 GitHub stars (GitHub, 2026-11-02)

## What it does

Defaults to two-ink (one dominant plate, one narrow accent plate); explicit one-ink requests honoured. Rebuilds each composition around subject, intent and words rather than copying a reference; documented twelve-image research set with attribution notes.

## Use when

- poster, zine or packaging art with a print look
- brand campaigns that need a restrained two-colour system
- halftone editorial covers

## Not for

- photoreal product shots
- full-colour illustration

## Install

- Claude Code: `git clone https://github.com/yanliudesign/mono-color-skill.git ~/.claude/skills/mono-color`
- Any agent, with the vargai CLI: `vargai install mono-color-print`

**Models it drives:** gpt_image_2_5, nano_banana_2_1

## Example output

https://raw.githubusercontent.com/yanliudesign/mono-color-skill/main/examples/example-cycling.png — A two-ink print made with the skill (README example) (credit: Yan Liu (README), https://github.com/yanliudesign/mono-color-skill)

## More style references skills

- [Find References](https://varg.ai/how-to/skills/find-references.md): Real references before anything is made: seven sources, four kinds of reference with this week's numbers, ten visual ideas and a board to pick from.
- [Video Moodboard](https://varg.ai/how-to/skills/video-moodboard.md): A moodboard AI video models follow: lines ranked by what matters most, every line a number, drawn as a board and checked on stills. 450+ links.
- [FrameThrower Film References](https://varg.ai/how-to/skills/framethrower.md): Film stills from 5,489 films, searchable by lighting, lens, shot size, color and mood.
- [Are.na MCP](https://varg.ai/how-to/skills/arena-mcp.md): Build and read Are.na moodboard channels from your agent.
- [Video references by Refs](https://varg.ai/how-to/skills/refs-video-references.md): 4,322 launch films, demos and motion pieces measured shot by shot, searchable free from your agent over MCP.
- [GPT Image 2 Style Library](https://varg.ai/how-to/skills/gpt-image-2-style-library.md): Pick styles and industrial prompt templates for GPT Image 2/2.5 from a 530+ case library (a 35K-star repo).

## Run the generation on varg

varg is one API key for the models these skills drive — Seedance 2.5, Kling 4, Veo 3.1, Wan 3, Nano Banana 2.1, GPT Image 2.5, ElevenLabs v4 and 200+ more — billed in credits, with a cost estimate before anything runs.

- Any agent with a terminal: `curl -fsSL https://varg.ai/install | sh` (Windows: `irm https://varg.ai/install.ps1 | iex`), then `vargai login` and every model is a command — `vargai generate_video …`, `vargai estimate_cost …` (https://varg.ai/how-to/cli/)
- Claude Code: `claude plugin marketplace add vargHQ/skills && claude plugin install varg@varg` (skill + hosted MCP)
- Codex, Cursor, Gemini CLI, OpenClaw: `npx skills add vargHQ/skills --skill varg-ai -a <agent> -y`
- Grok and any agent that reads SKILL.md: `npx skills add vargHQ/skills --skill varg-ai`, or point it at the skill's SKILL.md
- Claude.ai, ChatGPT, any MCP client: connect `https://mcp.varg.ai/mcp`
- API key: https://varg.ai/dashboard/api · Model specs: https://varg.ai/models/llms.txt