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Photo Riso Poster

Community
luckdvr
photo-riso-poster

Turn a photo or a text theme into one quiet riso-print archive poster. For a photo, deconstruct its observable facts (counts, spacing, occlusion, direction, color roles) and rebuild them as a sparse 2–3 ink risograph composition on temperature-matched paper, with typography that also carries the evidence; for a text theme, distill one imageable idea. Aspect ratio follows the source photo. Generates the image, not prompt-only.

Overview

Publisherluckdvr
Repositoryphoto-riso-poster
Skill namephoto-riso-poster
Stars
60
Forks
2
Bundled files
26
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.

  • 26 bundled files

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

  • Open source

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

Installation

Install the Photo Riso Poster 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/luckdvr/photo-riso-poster/tree/main
  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/luckdvr/photo-riso-poster.git \
  .claude/skills/photo-riso-poster
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Photo Riso Poster 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 Photo Riso Poster 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 Photo Riso Poster 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.

Photo Riso Poster

Turn the user's input into ONE quiet riso-print poster image, generated by the built-in image generation tool from a text-only prompt. Never attach the source photo or any reference image to the generation call: evidence travels through your written analysis, style through the rules below.

Work autonomously through all steps without asking questions. Stop only if the image generation tool is unavailable — say so and never draw the poster with code.

Input

  • Photo: lock its file path, read it visually. If EXIF capture date is readable (e.g. via exiftool or PIL), use it for the date line; otherwise omit the date rather than inventing one.
  • Text theme (no photo): distill the theme into one imageable core idea, then invent modest archive facts (a count, a season, a weather feel) consistent with it.
  • Aspect: a photo's orientation and ratio decide the poster's — landscape → landscape, portrait → portrait, square → square, nearest ratio the tool supports. A text theme defaults to portrait 3:4 unless the user asks otherwise.

Abstraction level

Default is standard. Switch only when the user asks in plain words (抽象一点 / more abstract → minimal; 具象一点、像一点 / more faithful → faithful):

  • faithful — printed-illustration detail: fuller contours and interior texture, like a botanical or object plate from an old book.
  • standard — simplified silhouettes: identity readable, detail dropped.
  • minimal — identity dissolves: subjects become pure geometric marks (dots, short bars, blocks, arcs, hairlines); only counts, positions, size hierarchy, directions, intervals, and occlusion survive. No recognizable silhouettes at all.

Deconstruct

Record observable facts once, briefly:

  • Primary element and up to two secondary relationships: identity, shape, count, normalized position, scale, direction.
  • Structure: one dominant axis; rhythm (intervals, clusters, repetition); depth order; occlusion; meaningful empty field.
  • Color: the photo's LARGE-AREA temperature (cool: blue/gray sky, snow, water, green field → cool paper; warm: dusk, autumn, earth, indoor light → warm paper), plus 2–3 dominant color roles to become ink colors.
  • Archive facts for typography: season, time-of-day or weather feel, a count worth stating.

Map facts to marks and type

  • Every visual mark traces to a named fact; delete decorative marks. One primary motif family, at most two supporting.
  • Marks are printed-matter forms at the chosen abstraction level — faithful: detailed plate illustration; standard: simplified silhouettes, blocks, hairlines, dots, short bars; minimal: geometric marks only. At every level preserve relational invariants — counts, uneven spacing, size hierarchy, direction, asymmetry, occlusion, reflections and shadows as fainter echoes. Never trace the scene's full layout: the composition is a sparse cluster floating on paper, not a filled scene — no sky region, no ground plane running edge to edge; supporting elements sampled sparsely (3–5 representatives), never a full row.
  • Human figures become small anonymous silhouettes; animals featureless silhouettes; never faces.
  • Type is also an evidence outlet: a count mark like x 002 (append a noun like x 024 TRUNKS when it clarifies; use x 050+ when beyond exact counting), a date line, a weather/time note, a 2–4 word poetic English phrase, an archive number. A place name only when recognized with high confidence; otherwise omit.

Generate

Write one compact image prompt — four short paragraphs: canvas+paper / ink layers+cluster / typography / mood+avoids — then call the image generation tool. The prompt must state:

  • Canvas: the chosen aspect ratio; full-frame scanned flat matte paper with subtle fibers and faint mottling. Paper color follows the photo's temperature — state it explicitly: cool photo → pale blue-gray porcelain paper or cool snow-white paper; warm photo → warm pale cream paper. Light, never deep yellow, never stained. No border, mockup, or 3D.
  • Riso multi-ink system: the poster is a 2–3 color risograph print. Assign each sampled color role to one ink layer — e.g. deep indigo ink, leaf-green ink, vermilion-orange ink — printed as grainy silhouettes and fields with slight misregistration where layers overlap. Inks stay saturated and visibly colored; the most vivid ink covers the primary element or a substantial field, not a tiny dot. Gray/black hairlines may support.
  • Attention geometry: 65–85% empty paper; one visual cluster occupying 10–25%, off-center where the source structure suggests; no edge-hugging, no grid alignment, keep the source's uneven spacing.
  • Cluster treatment: shapes drawn as printed matter — photocopy softness, fine risograph grain, ink bleed at edges, slight layer misregistration.
  • Typography system: small typewriter/serif type around the cluster — the poetic phrase in 2–4 stacked lines pressed near the cluster; a tiny archive block set apart like a margin note, using the fixed vocabulary ARCHIVE NNN or NO. NNN plus the date and weather/time lines; the count mark; one or two tiny scattered dashes or dots. Type may print in one of the ink colors. Slight drift and misregistration welcome; every word short, simple, correctly spelled.
  • Mood: quiet, archival, diary-like, indie riso zine, nostalgic but chromatic.
  • Avoid: full-bleed scene or landscape painting, sky/ground regions filling the canvas, cartoon or children's-book flatness, commercial poster layout, clean UI white, cinematic light, 3D, neon, photorealism, long clean paragraphs, muddy browns.

Deliver

Return the generated image, then:

text
**最终 Prompt**
[the exact prompt sent to the tool]

**说明**
- 证据: [2–3 key source facts and where they landed — marks or type]
- 纸色: [cool/warm and why]
- 画幅: [ratio and why]

Before returning, check once: cluster small and off-center on mostly empty paper; every mark and every typographic fact traceable to the source; 2–3 inks visibly colored; paper temperature matches the photo; words spelled correctly. If ink color is washed out, text is garbled, or the layout collapsed into a filled scene, regenerate once with the failing rule stated more forcefully.

Examples

  • "用 $photo-riso-poster 把这张照片做成海报" (attach photo)
  • "用 $photo-riso-poster 做一张关于夏天傍晚的图" (text theme)

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 Photo Riso Poster AI skill do?

Turn a photo or a text theme into one quiet riso-print archive poster. For a photo, deconstruct its observable facts (counts, spacing, occlusion, direction, color roles) and rebuild them as a sparse 2–3 ink risograph composition on temperature-matched paper, with typography that also carries the evidence; for a text theme, distill one imageable idea. Aspect ratio follows the source photo. Generates the image, not prompt-only.

Why use Photo Riso Poster on TypingMind?

Because you install it once and use it with any model. Photo Riso Poster 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 Photo Riso Poster in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/luckdvr/photo-riso-poster/tree/main. 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 Photo Riso Poster?

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 Photo Riso Poster?

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

Is the Photo Riso Poster AI skill free?

Yes. It is published on GitHub by luckdvr 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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