name: scenario-chatgpt-pet-create description: "Use when creating a ChatGPT pet or Codex pet with Scenario: hatching an animated companion from a text idea, a character, mascot or brand cue, or reference photos and art; making the 1536x2288 v2 pet sprite sheet with nine animation states and sixteen look directions, the 1536x1872 v1 sheet ChatGPT web uploads, a pixel-perfect pixel-art pet, an animated GIF of the pet, or a Codex pet.json package." license: MIT

Scenario ChatGPT Pet: Create

Overview

A ChatGPT pet is one transparent sprite sheet: 192x208 cells, 8 columns, nine animation rows (v1, 1536x1872) plus two rows of sixteen look directions (v2, 1536x2288). Rows, frame counts, what each state must show, the look directions, the verdict file and the package layout: references/sheet-contract.md. Read it before the first generation.

This skill delivers, next to the run: a v2 sheet (unless the user wants v1 only), a v1 copy for ChatGPT web upload, pet.gif on a soft background and pet-transparent.gif, and a Codex pet.json package, installed only when the user says yes. Every image is generated through the Scenario MCP server; the frame cutting, sheet assembly, validation, GIFs and packaging run locally in the shipped scripts, because the deliverable is a file for ChatGPT or Codex and no MCP tool assembles a pet sheet. An image model never draws the whole sheet: it draws one row at a time, and the scripts place every frame.

Connection, scope and the core loop: the scenario skill. Changing a pet that already has a sheet: scenario-chatgpt-pet-update. If a sibling skill named here is missing from your available skills, ask the user to install it (npx skills add scenario-labs/skills --skill <name>); unattended, proceed from tool schemas and flag the gap.

Scripts, run by the agent with Python 3, Pillow and NumPy (pip install pillow numpy, in a virtual environment outside the run folder when the system Python refuses), by their path in this skill's scripts/ folder from the user's working folder (the commands below shorten that path); each prints JSON, or error: ... with exit code 1, and --help lists every option:

• pet_prepare.py: run folder, background key, one prompt and request size per job (init), extra prompt sentences (note), pixel grid and palette (pixel).
• pet_frames.py: cuts a generated strip into frames and reports problems (extract); reads an existing sheet (split, used by the update skill).
• pet_build.py: assembles the sheet with one pet height, ground line and planted anchor on every row.
• pet_check.py: validates the exact file to deliver.
• pet_preview.py: contact sheet, labeled look sheet, GIFs.
• pet_package.py: package and optional Codex install.
• pet_common.py: shared layout facts, imported by the others.

Quick reference

| Step | Do | | -------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | Brief | Text, reference images, or both; one round of at most four questions | | Set up | pet_prepare.py init; upload_asset each file a job lists in references | | Model, in this order | model_openai-gpt-image-2-5-sunburst, then model_google-gemini-nano-banana-2-1; recommend only when both are blocked | | Each generation | model_schema_get once per model, dry_run, model_run with the job's prompt and sizes, jobs_wait, asset_download format: "png" to the job's output | | Pixel-perfect | After the base is approved and before any row: Pixel Snapper (model_pixel-snapper) on the base once, then pet_prepare.py pixel | | Each strip | pet_frames.py extract RUN --job <id>; read the JSON; look at frames/<id>/preview.png | | Rows 0-8 done | pet_build.py RUN --version 1 --out RUN/qa/checkpoint, then pet_preview.py contact and gif | | Finish | pet_build.py RUN, pet_preview.py looks, verdicts, pet_check.py, pet_preview.py gif, pet_package.py make |

The model ids are named on purpose: both keep one character across a row of eight poses from a reference image, which is what this job needs, and a team that cannot run either falls back to recommend with the need and the families (GPT Image, Nano Banana). Pixel Snapper is Scenario's tool for snapping generated pixel art to a clean grid and palette. Read every schema rather than trusting the sizes below: at authoring time Sunburst took referenceImages (an array, up to 10), width and height (16 to 3840, steps of 16), background (opaque here) and quality (left at its default in every run behind these notes), and widened extreme shapes on its own (asked for 3584x512, it returned 3584x1200), which is why init asks for a height of at least a third of the width; Nano Banana took up to 14 referenceImages and an aspectRatio preset (4:1, 8:1). Budget from the dry_run quote, which is what each job is charged: the cuCost that jobs_wait reports can leave out add-ons such as a project's Quality Gate, so tracking it understates spend. At authoring time the quotes were 13.75 CU for the base, 13.75 to 17.75 CU per strip (wider strips cost more) and 6.75 CU for a Snapper call, so a pixel-perfect v2 pet (12 generations with running-left mirrored, plus the Snapper) came to about 198 CU before any repair, about 216 CU when running-left has to be generated: agree a budget with room for two or three repaired rows. Unattended, never launch a job that would take the quotes already spent plus those still needed past the budget: if only the look rows are left, deliver v1 without them; otherwise stop and report.

Worked example: Biscuit, a corgi in a yellow raincoat, from a photo

1. Brief. The user sends a photo and a sentence. Ask once, only what changes the art: the name and one-line description (default: infer), the style (auto, pixel, plush, clay, sticker, flat-vector, 3d-toy, painterly, brand-inspired), pixel-perfect or not, and v2 with look directions (default) or v1 only. A brand name with no visual cues: ask for colors, shapes and attitude; never copy a logo or readable text. Unattended, infer and record the answers, make every approval below yourself against the brief (recording why), and never install. Resolve the team and project, then create one collection for the pet (collection_create, then collection_add_assets for every kept output, per the scenario skill).
2. Set up. python3 scripts/pet_prepare.py init --name Biscuit --notes "corgi in a yellow raincoat, red collar" --reference photo.jpg --out pets/biscuit (add --pixel for pixel-perfect: it sets the pixel style in every prompt; --version 1 skips the look rows). It picks a background key that contrasts with the references (or with the notes: a pink pet gets green, never magenta) and writes jobs.json: per job an id, frames, prompt, retry_prompt, references (run-relative files), sizes per model, depends_on and output. Just before a job runs, upload_asset each local file it lists under references/ (once per file, noting the id); a generated/ file is already an asset, so pass the id its job returned instead of uploading the download.
3. Base. Run the base job with referenceImages set to the uploaded photo ids (none for a text-only pet), its prompt verbatim, and its Sunburst width/height, background: "opaque". Download to generated/base.png, run pet_frames.py extract RUN --job base, which writes the clean identity reference references/base.png, and show the base with asset_display. Nothing else starts before the user approves it; a warning that the pet is close to the key color means init --force --chroma-key with another key and a new base.
4. Pixel-perfect variant. Run model_pixel-snapper on the approved base asset as generated, background and all (image, colors 24 unless the user wants fewer, any fixed seed), download it, then pet_prepare.py pixel RUN --snapped snapped.png --colors <same>. It picks a grid of 8, 4, 2 or 1 screen pixels per art pixel, keeps the palette, and rewrites references/base.png as the enlarged pixel version (upload that new file), so every row is drawn from clean pixels and the build snaps every frame to the same grid and palette. Snap only the base: snapping each frame puts frames on different grids and costs a call per frame.
5. Animation rows. After the base, every standard row is independent except running-left, which waits for running-right. Launch idle and running-right first as identity and gait checks, then the rest in waves under the team's concurrency limit (wait: false, one jobs_wait over the batch; a 429 naming parallel-custom-jobs gives the limit, per the scenario skill). Each row: referenceImages = the uploaded references/base.png plus the user's photos, the job's prompt and sizes. After each download, extract, read its JSON and look at the preview: an error (wrong count, a pose touching the image edge) or a wrong pose means regenerating that row now, with retry_prompt or a sentence added by pet_prepare.py note RUN --jobs <id> --text "...". When the pet looks the same mirrored (nothing handed, lettered or leaning to one side, so not a corgi with a one-sided patch or a robot with a tilted leaf), skip generating running-left and pass --mirror-left to every pet_build.py call instead.
6. Checkpoint. pet_build.py RUN --version 1 --out RUN/qa/checkpoint, then pet_preview.py contact RUN/qa/checkpoint.png --out RUN/qa/contact.png and pet_preview.py gif RUN/qa/checkpoint.png --out-dir RUN/qa/checkpoint-previews; pet_check.py RUN/qa/checkpoint.png --run RUN --json-out RUN/qa/checkpoint-check.json catches height and jump errors before the look rows are paid for. Show the GIF (open the local file, or upload it and share the app_url from asset_display); feedback goes into the affected rows, never into the sheet.
7. Look rows (v2). Decide how this pet looks around (what stays planted, what leads, how the eyes and ears move), add it with note --jobs look-cardinals,look-9,look-10, then run look-cardinals (four poses: up, right, down, left). Extract it and approve all four by eye before anything else: a cardinal that does not read at pet size is regenerated now. Then look-9, extract, pet_build.py RUN --out RUN/qa/checkpoint (it warns that look-10 is still empty) and pet_preview.py looks RUN/qa/checkpoint.png --out RUN/qa/looks.png to look at it; then look-10, whose references include generated/look-9.png so the sweep continues.
8. Finish. pet_build.py RUN writes final/spritesheet.png and final/spritesheet.webp with one edge cleanup pass. pet_preview.py looks RUN/final/spritesheet.webp --out RUN/qa/looks.png, look at it, and write qa/direction-semantics.json (format in the contract