Pixel Face logo

Pixel Face

Organization
Cotal-AI
pixel-face

Create or improve a 32×32 pixel-art persona face for the Frontier Faces demo (examples/04-frontier-faces/personas.mjs) — the animated agent avatars rendered by face-term.mjs / the browser <cotal-face> / the gallery. Use when the user asks to add a new persona/face/avatar, improve an existing one (hair, eyes, likeness, expression), make it "look more like" someone, or fix a face that "doesn't read". Drives the example's own tools (img2rows.mjs, render-png.mjs) and follows FACE-DESIGN.md; uses a multi-agent variant loop for likeness.

Overview

PublisherCotal-AI
RepositoryCotal
Skill namepixel-face
Stars
338
Forks
43
Bundled files
Instructions only
LicenseApache-2.0
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Cotal-AI on GitHub. Read the source before you install it.

Installation

Install the Pixel Face 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/Cotal-AI/Cotal/tree/main/.claude/skills/pixel-face
  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/Cotal-AI/Cotal.git /tmp/Cotal
mkdir -p .claude/skills
cp -r /tmp/Cotal/.claude/skills/pixel-face .claude/skills/pixel-face
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Pixel Face 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 Pixel Face 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 Pixel Face 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.

pixel-face — author & refine the Frontier Faces pixel art

A persona face is pure data in examples/04-frontier-faces/personas.mjs (one entry per persona). The same data drives the terminal renderer (face-term.mjs), the browser element (web/cotal-face.js), and the PNG tool — so edit personas.mjs once and it's live everywhere. Always work from examples/04-frontier-faces/.

Where things are

  • personas.mjs — the entries (rows, colors, glow, mouths, expr, eyes, lines). Single source of truth.
  • FACE-DESIGN.md — the full conventions (grid zones, color keys, eye recipe, visemes). Read it first.
  • tools/img2rows.mjs — rough a reference photo into a 32×32 rows+palette.
  • tools/render-png.mjs — render a 7-state contact sheet (PNG) for review. This is the validator.
  • tools/face-template.mjs — copy-me starter entry that encodes the conventions.
  • gallery.html (all personas, original-vs-pixel) and preview.html?p=<key> (one persona) — served via python3 -m http.server or tools/serve-wall.mjs; for visual QA.
  • assets/<persona-key>.<ext> — reference photos. Gitignored / local-only — never commit them.

Persona key ≠ agent name. The roster uses agent-file basenames; the face comes from each agent's face: frontmatter: elon→musk, steve→jobs, rayan→ray (others match). Persona keys: neon david ray sven garry jobs musk dario mira bernie michelle.

Conventions (condensed — full detail in FACE-DESIGN.md)

  • Grid 32×32, . = transparent, face centered ~col 15. Zones: hair 1–9 · brows 10–11 (drawn by expr, not rows) · eyes 12–13 (drawn by eyes()) · nose 14–16 · mouth 18–20 (drawn by mouths) · chin 21–24 · collar 26–31.
  • Color keys: skin P light / S mid / s shadow · E pupil · W/w catchlight · R lip · m lip-shadow · b brow · h hair · L cyan + M magenta = the cotal glow ribbon (glow: { L: 8, M: 6 } blurs them). Use rng(row, c1, c2, key) (exported from personas.mjs) for runs.
  • Eye recipe: 2-wide iris (E) on the iris row; row below = catchlight (W/w, OUTER corner)
    • lid-shadow (s, INNER). jobs is the "alive" benchmark — match its eye, don't use flat black blocks.
  • Surprise = raised brows + open mouth, eyes stay open (growing the iris behind glasses looks wrong). Don't reintroduce a wide eye style.
  • The face renders at a fixed 32×16 (not scaled to the pane), so faces look the same size as long as the pane is ≥ ~32 cols.

Tools (exact)

sh
# rough a reference photo → 32×32 rows + palette (macOS: uses sips)
node tools/img2rows.mjs --in assets/<key>.png --w 24 --h 30 [--colors 12] [--x0 4] [--y0 0]

# render a 7-state contact sheet (neutral·happy·sad·angry·surprised·talking·blink) to review
node tools/render-png.mjs --file <draft>.mjs --out /tmp/<key>.png --scale 8     # draft: export const entry = {…}
node tools/render-png.mjs --file personas.mjs --persona <key> --out /tmp/<key>.png --scale 8   # existing

A draft module must export const entry = {…} and (if it uses rng in expr/mouths) import { rng } from '/abs/path/examples/04-frontier-faces/personas.mjs'. After every render, Read the PNG and compare it to assets/<key>.<ext>. Iterate.

Create a new persona

  1. Pick a key. If you have a photo, save it as assets/<key>.png and img2rows it for a rough base; otherwise copy tools/face-template.mjs.
  2. Hand-edit a /tmp/<key>-draft.mjs (export const entry) following the conventions above.
  3. render-png → Read the PNG → compare to the reference → iterate until all 7 states read well.
  4. Splice the finished entry into personas.mjs (keep the file's style; reuse color keys).
  5. Review in the gallery; confirm no other persona changed.

Improve an existing persona

  1. Read its entry in personas.mjs + its reference assets/<key>.<ext> and the current render.
  2. Edit only the in-scope rows (e.g. hair = rows 1–9). Preserve eyes(), glasses/beard, expr, mouths, colors of other features, and the collar/glow — change the minimum.
  3. render-png the draft → Read → compare → iterate (≥3 passes). Then splice the rows back into personas.mjs and confirm the diff is limited to that entry.

Multi-agent variant loop (use for likeness — "doesn't look like them")

When matching a real person, generate options instead of guessing:

  1. Spawn ~3 subagents on the main working tree (NOT git worktrees — assets/ is gitignored, so reference photos are absent from a fresh worktree). Give each: the reference path, the current entry, the conventions, and a distinct direction.
  2. Each agent writes its OWN /tmp/<key>-<n>.mjs draft (never edits personas.mjs — avoids conflicts), renders it, Reads the PNG, compares to the photo, iterates, and returns the final rows.
  3. You pick the best, splice it in, review in the gallery. Refine one axis per pass (e.g. head shape, then hair). This is the loop used for garry/musk.

QA bar

  • Always review visually — the render-png contact sheet and the gallery.html original-vs-pixel view — never blind. Judge: does it read / look like the person across all 5 expressions + blink?
  • jobs is the reference standard. Keep faces front-facing/symmetric unless intentional.
  • Don't commit assets/ (reference photos stay local). Persona edits to personas.mjs ARE committable.

Frequently asked questions

What does the Pixel Face AI skill do?

Create or improve a 32×32 pixel-art persona face for the Frontier Faces demo (examples/04-frontier-faces/personas.mjs) — the animated agent avatars rendered by face-term.mjs / the browser <cotal-face> / the gallery. Use when the user asks to add a new persona/face/avatar, improve an existing one (hair, eyes, likeness, expression), make it "look more like" someone, or fix a face that "doesn't read". Drives the example's own tools (img2rows.mjs, render-png.mjs) and follows FACE-DESIGN.md; uses a multi-agent variant loop for likeness.

Why use Pixel Face on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Cotal-AI/Cotal/tree/main/.claude/skills/pixel-face. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Pixel Face?

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 Pixel Face?

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

Is the Pixel Face AI skill free?

Yes. It is published on GitHub by Cotal-AI under the Apache-2.0 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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