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Submit

Organization
with-pebbly
submit

Turn the sprite you just made into a gallery pull request against with-pebbly/aseprite-ai-artist — exported files, an honest generation.yaml (prompts verbatim, model, plugin version, validate results, optional benchmark scoring), checked with `pnpm gallery:check`, opened with the gh CLI. Use when the user wants to share, publish or submit their work to the gallery or the benchmark.

Overview

Publisherwith-pebbly
Repositoryaseprite-ai-artist
Skill namesubmit
Stars
456
Forks
28
Bundled files
Instructions only
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.

  • Self-contained

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

  • Open source

    Published by with-pebbly on GitHub. Read the source before you install it.

Installation

Install the Submit 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/with-pebbly/aseprite-ai-artist/tree/main/skills/submit
  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/with-pebbly/aseprite-ai-artist.git /tmp/aseprite-ai-artist
mkdir -p .claude/skills
cp -r /tmp/aseprite-ai-artist/skills/submit .claude/skills/submit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Submit 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 Submit 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 Submit 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.

Submit a finished sprite to the community gallery

The gallery keeps how a sprite was made next to the sprite: the prompts as sent, the model, the plugin version, what validate said. A submission that tidies any of that up is worse than none — the benchmark compares models on it. Record what happened, not what you wish had.

The contract is gallery/src/schema.ts in the repository; pnpm gallery:check enforces it. This workflow produces files that pass it.

1. Preconditions

Check all three before touching anything. If one fails, stop and tell the user exactly what to do; never route around it.

  • preflight is ready. Not ready → stop. Never export or edit files on disk instead of the live document.
  • The work is finished and reviewed. Run aseprite:review if it has not been. Submitting art that fails review puts it in public.
  • gh is installed and authenticated. Run gh auth status. Missing → tell the user to install it (https://cli.github.com) and run gh auth login. There is no other route to a pull request.

Then ask for consent, once, before anything leaves the machine: "I will fork with-pebbly/aseprite-ai-artist on your GitHub account, push a branch, and open a public pull request with your sprite and the prompts used. OK?" Forking, pushing and opening a PR act as the user, in public. No yes, no fork.

2. Gather metadata

Take what the session already knows. Ask the user only for the rest, in one message.

FieldSource
title, descriptionAsk. Offer a title drawn from the work.
author.name, author.githubgh api user --jq '.name,.login' as the default; let the user override. Handle without @.
harnessYou know which you run in: claude-code, omp, codex, gemini-cli, cursor…
modelsThe model id exactly as the harness names it (claude-opus-4-1, gpt-5). Not a nickname. If different models ran different steps, list each once and name the model on every step.
pluginpreflight returns extensionVersion — the plugin build attached to Aseprite. Use it. If its directive says the extension and the server differ, the run mixed two builds and has no single version: stop and tell the user to align them (install-extension, restart Aseprite) before submitting. If extensionVersion is null, run npx @pebbly/aseprite-ai-artist --version. It must be a released version listed in the repo's CHANGELOG.md; a dev or unreleased build cannot be submitted — say so.
steps[].textEvery user prompt that shaped the work, verbatim and in order, copied from the conversation. Never paraphrase or merge. A prompt sent in another language may be rendered in English here only if the verbatim text goes into steps[].original — never for a benchmark run.
steps[].interventionsEach clarifying question you asked and the answer the user gave, plus any steering beyond the prompt. Empty if none.
referencesWhere the design came from — required, never guessed. source: none when the work was drawn from words alone (the user chose "continue without" in aseprite:concept, or it was never offered). generated when an image model made a concept sheet or storyboard: ask which model if you do not know, list it in imageModels, the kinds in kinds, and put the image prompt you wrote in prompt. supplied when the user handed over their own art. Copy the reference image into the folder as a reference file when you have it.
dateToday, yyyy-mm-dd.
tagsA few lowercase words: character, animation, 32x32.

For each step run validate on the state the step produced (or on the final document if steps were not saved separately) and record errors, warnings, score and passed as the tool reported them. Do not round a failing report into a pass.

3. Benchmark mode

Only if the user says the run followed a benchmark prompt and the user is a maintainer. Benchmark runs are the project's own measurements; the checker and CI refuse a benchmark: block from anyone else. Check first: take the login from gh api user --jq .login (never from what the user typed or the YAML), and look for it, case-insensitively, in https://raw.githubusercontent.com/with-pebbly/aseprite-ai-artist/main/gallery/MAINTAINERS. Not listed, or either call fails → not a maintainer: say benchmarks are run by the maintainers, skip this section and submit a gallery generation. A maintainer sets author.github to that same login.

  1. Fetch its definition: the local clone's gallery/prompts/<id>/prompt.yaml, or https://raw.githubusercontent.com/with-pebbly/aseprite-ai-artist/main/gallery/prompts/<id>/prompt.yaml.
  2. Confirm the run followed the fixed setup (canvas, palette, the rules list) and that every step's text equals the prompt's step text word for word — only surrounding whitespace and line endings are ignored. Any difference — a reworded step, extra steering, a reference image — means this is not a benchmark run. Submit it as a free generation (omit benchmark) and tell the user why.
  3. Judge every criterion in the prompt, none skipped. Get the evidence first: look op preview for the read, a filmstrip or onion-skinned view for motion, validate (with expect where the criterion names a contract), tag op="list", frame op="list". Record pass and a one-line note naming what you saw. Never mark a pass on a criterion whose evidence you did not look at in this session; look now, or mark it failed and say why.
  4. Write benchmark: {prompt, revision, results} with the prompt's revision and one result per criterion id.

4. Export

Stage everything in one flat folder named <yyyy-mm-dd>-<slug>, slug lowercase with single hyphens. Use the plugin's own export; never write these by hand.

  1. Save the live document, then keep a copy: sprite_manage op="save", then export op="aseprite" path="<folder>/<slug>.aseprite". Role source. The gallery keeps the editable original; never copy or edit a .aseprite file from disk.
  2. Cover. export op="png" scale=N frame=1 path="<folder>/<slug>.png" with the smallest integer N that makes the long side at least 256 px (32×32 → 8), capped at the tool's maximum of 16 — a smaller sprite simply gets a smaller cover. For an animation, the GIF can be the cover instead. Role cover; exactly one file.
  3. Animation (more than one frame): export op="gif" scale=N path="<folder>/<slug>.gif", same scale. Role animation, or cover if it is the cover.
  4. Filmstrip (animated): export op="spritesheet" sheetType="horizontal" scale=N includeJson=false path="<folder>/<slug>-strip.png", same scale. Role filmstrip.
  5. Per step. When steps produced separate documents or intermediate saves, export the source (and a still) for each and set step on those files.

Files stay flat — no subfolders — and only .aseprite, .png, .gif, .webp or .json. Each file must be under 95 MiB (GitHub hard-blocks 100); anything over 50 MiB draws a warning, so keep covers and GIFs small.

Look at the cover and the GIF's first frame before continuing: what goes into the public gallery is what you exported, not what you remember drawing.

5. Write generation.yaml

In the staging folder, exactly per the schema:

yaml
title: Cherry tree in bloom
description: A 64×64 cherry tree drawn to the tree-growth brief.
date: 2026-09-29
author: { name: Ada Lovelace, github: ada }
plugin: 0.3.2
harness: claude-code
models: [claude-opus-4-1]
tags: [nature, animation]
references:
  source: generated
  imageModels: [gpt-image-2]
  kinds: [concept-sheet]
steps:
  # the step text exactly as sent — for a benchmark run, verbatim from
  # prompt.yaml (cut short here)
  - text: |-
      The document is already open: 64×64 pixels, RGB, with the PICO-8 palette loaded (16 colours). …
    interventions: []
files:
  - { path: tree.png, role: cover, label: The tree, step: 1 }
  - { path: tree.aseprite, role: source, label: Source document }
validate:
  - { step: 1, passed: true, score: 92, errors: 0, warnings: 1 }
benchmark:            # only for a confirmed benchmark run
  prompt: tree-growth
  revision: 1
  results:
    - { criterion: tree-validate, pass: true, note: "validate: 0 errors, 1 warning, named." }

Rules that fail the check when missed:

  • Exactly one cover (png/gif/webp) and at least one .aseprite with role source; every file listed once, nothing unlisted in the folder.
  • With more than one model, every step names its model, and it appears in models.
  • step values and validate[].step are 1-based and must exist.
  • benchmark.results names criterion ids that exist in the prompt.

Two optional blocks:

  • metrics — per step, from the session you can see: minutes, toolCalls, outputTokens, costUsd. Fill only what the harness actually reports; never estimate.
  • ratings — leave them out. You took part in this run, and the checker refuses model:<id> for any of the run's own models. Judges add ratings later, blind, against gallery/RUBRIC.md.

Show the user the final YAML and the file list, and wait for a go-ahead. They are about to publish it under their name.

6. Validate in a clone

  1. Use a clone of the user's fork if one exists; otherwise fork and clone (gh repo fork with-pebbly/aseprite-ai-artist --clone) — consent from step 1 covers this. Make sure the checkout is up to date with upstream main.
  2. Copy the staging folder to gallery/generations/<folder>/.
  3. pnpm install if node_modules is missing, then pnpm gallery:check.
  4. Fix every error and re-run until clean. Fix by correcting the YAML or re-exporting, never by editing the recorded truth (prompts, results) to satisfy the checker. Warnings: read them, mention them to the user.

Never submit with errors.

7. Submit

git switch -c gallery/<folder>
git add gallery/generations/<folder>
git commit -m "gallery: <title>"
git push -u origin gallery/<folder>
gh pr create --repo with-pebbly/aseprite-ai-artist --head <login>:gallery/<folder> --title "gallery: <title>" --body-file <filled body>

Build the body from .github/PULL_REQUEST_TEMPLATE/generation.md in the clone: keep its sections and checklist, fill them honestly — what the sprite is, harness, model, plugin version, whether it is a benchmark run and its score — and pass that file as --body-file. Before pushing, git status must show only the one folder as added; anything else in the diff means stop and clean it up. A PR adds its own folder and nothing more.

Report the PR URL. Tell the user CI will re-run gallery:check and that a maintainer reviews it.

Failure modes

SymptomDo
preflight not ready / not_connectedStop. Relay the tool's directive. Do not touch files on disk.
gh missing or not logged inStop. Give the install and gh auth login steps.
User declines consentStop; leave the staging folder for them and say where it is.
pnpm gallery:check errorsFix the YAML or files, re-run. Never submit red.
A file exceeds the size limitLower the export scale, drop the GIF to a shorter loop, or use a PNG cover; never strip the .aseprite.
Prompt text differs from the benchmark'sNot a benchmark run. Drop benchmark, submit as a free generation, say why.
Plugin version not in CHANGELOG.mdIt is an unreleased build. Tell the user; they can submit after installing a released version.
Push or PR creation failsReport the gh error as printed; do not retry with other credentials or routes.

Related

aseprite:review before submitting, aseprite:export for export options, rules://07-review-checklist.

Frequently asked questions

What does the Submit AI skill do?

Turn the sprite you just made into a gallery pull request against with-pebbly/aseprite-ai-artist — exported files, an honest generation.yaml (prompts verbatim, model, plugin version, validate results, optional benchmark scoring), checked with `pnpm gallery:check`, opened with the gh CLI. Use when the user wants to share, publish or submit their work to the gallery or the benchmark.

Why use Submit on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/with-pebbly/aseprite-ai-artist/tree/main/skills/submit. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Submit?

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 Submit?

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

Is the Submit AI skill free?

Yes. It is published on GitHub by with-pebbly 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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