Generate a brand-new Codogotchi pet with the FULL lite set from scratch: Codex (Tier 1), Lite-Basic (Tier 2), and Lite-Enhanced (Tier 3) sprite atlases plus `pet.json`, generated in tier order. Use when a user wants a complete new Codogotchi pet that includes the enhanced / polish animation rows, not just the minimal alive/ghost set.
Installation
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Generate a brand-new Codogotchi pet with the FULL lite set from scratch: Codex (Tier 1), Lite-Basic (Tier 2), and Lite-Enhanced (Tier 3) sprite atlases plus `pet.json`, generated in tier order. Use when a user wants a complete new Codogotchi pet that includes the enhanced / polish animation rows, not just the minimal alive/ghost set.
Paths in this skill — scripts/…, references/…, and README.md below are relative to this plugin's root (hatch-codogotchi/, two directories up from this file). cd to the plugin root before running the commands, or prefix each path with it.
I/O policy — scripts own all paths; the skill names none.image_gen cannot reliably store or re-find what it generates, so never tell it to save a row to a named directory and never hunt for an image-generation save folder. Pass whatever local/temp paths the caller picks into the script arguments and chain each script's output forward; the only artifact that must persist is the final magenta atlas the user keys and installs.
No model-side visual inspection. Do not use Computer Use, screenshots, or UI automation to eyeball generated output — the model cannot reliably see its own framing and it burns tokens. Frame geometry is enforced deterministically by slice_grid.py; visual review is the human's, on the script-produced contact sheet and previews.
hatch-codogotchi-codex-and-lite-full
Generate a brand-new Codogotchi pet from scratch with the full lite set — produces Codex (Tier 1), Lite-Basic (Tier 2), and Lite-Enhanced (Tier 3) in one project.
File
Tier
Grid
Dimensions
Rows
spritesheet.webp
1 — Codex
8 × 9
1536 × 1872
9
codogotchi-lite-basic-spritesheet.webp
2 — Lite-Basic
8 × 9
1536 × 1872
9 (incl. ghost)
codogotchi-lite-enhanced-spritesheet.webp
3 — Lite-Enhanced
8 × 8
1536 × 1664
8
pet.json
—
—
—
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Order matters: Codex → Lite-Basic → Lite-Enhanced. Lite-Enhanced requires a finished Lite-Basic sheet and uses it as an extra style reference. Want Codex + Basic only? → SKILL-codex-and-lite-basic.md.
Execution model (slot-first): for every tier, Codex uses its built-in image_gen tool to generate one full animation row per call as 8 invisible equal-size slots, 4 across and 2 down (read left-to-right, top row then bottom), all 8 filled, on the single flat chroma key chosen for this pet (see below). The slots are an invisible placement grid — the prompt says "slots," never "grid/cells," so the model paints one continuous flat field, not 8 separate cards with per-cell backdrops. prepare_pet_run.py also emits a layout-guide.png (a 4×2 placeholder image with slot boundaries, blue safe-margins, and centering crosshairs); attach it (and the seed) to every image_gen call as reference-only — it carries placement/centering/safe-margins as a picture, which is what keeps frames from clipping. image_gen need not hit an exact canvas size. Then run slice_grid.py (dimension-tolerant): it slices by fraction, shares one scale across all 8 frames, and emits the exact canonical 1536×208 row strip. compose_atlas.py stacks the strips into the atlas. Generate one row at a time; never ask for the whole atlas in a single image.
Non-negotiable row gate: finish one row completely (generate grid → slice_grid.py) before generating the next. If a grid comes back clipped, cramped, or off-model, regenerate the whole grid instead of patching forward. Do not compose until every row strip exists. The model does not screenshot or eyeball its own output — slice_grid.py enforces geometry, and prop clarity / face / scale / motion are reviewed by the human on the script-produced contact sheet after composing.
Chroma key — one auto-selected key for the whole pet, keyed by the user later.prepare_pet_run.py picks ONE chroma for the entire pet (every row of every tier shares it, because Studio keys each sheet uniformly): with a seed it auto-selects the canonical key (magenta #FF00FF, blue #0047BB, or green #00B140) farthest from the pet's own palette; with no seed (or --chroma magenta) it uses magenta. Reserved accent colors recolor automatically to avoid whatever key wins — the success ✓/stamp (default green) flips to blue under a green key, the ghost (default ethereal blue) flips to warm rose under a blue key — so intentional details are always preserved, never keyed out. The pipeline does not key the sheet. After QA, hand the flat-key atlas to the user to key in Codogotchi Studio (https://codogotchi.app/studio).
Recommended production pattern: for each row, generate the minimum number of distinct keyframes needed for a readable, non-static loop, then reuse or mirror earlier stable frames to close the loop when that preserves motion quality. Many rows can be completed faster with ~4 strong keyframes plus a mirrored/reused closure instead of 8 fully independent generations. This is a recommended acceleration pattern, not a hard requirement.
Read first — the doctrines (full text in README.md + references/animation-rows-lite.md)
0. Motion restraint — row-kind aware (paramount). For standing/status rows, a calm pet with small, smooth motion always beats an expressive one that jitters; when they conflict, choose stability. Anchor the torso, head, hips, and both feet in nearly the same place across all 8 frames; confine motion to one element — the named prop, one arm, or the expression — at low amplitude with short, smooth arcs. For locomotion rows (running-right, running-left), animate a fluid run-in-place stride cycle: keep identity, scale, baseline, and facing stable and the character horizontally centered (no marching across the cell — lateral travel risks clipping), with all 8 frames as distinct, evenly-spaced stride phases that flow. Do not force planted feet, and reject the failure mode where 2–3 frames are nearly identical and then jump. For jumping, animate one full jump cycle across all 8 distinct frames (crouch → push-off → rise → airborne peak → descend → land → settle) with no standing/idle filler frames — give real vertical clearance (feet clearly airborne at the peak, ~24–40 px off baseline, not a shin-high hover that reads as floating), stay horizontally centered, and raise the arms into a gesture that stays below full overhead extension. "No static rows" is a floor (subtle smooth life so frames differ), not a push toward uncontrolled motion.
Prop doctrine — NOT charades. Emotion states (idle, failed→sad, celebrations) lead with expression; every other state is carried by one clearly-visible prop — never mimed/"invisible", never an A/B prop choice. Same prop, all 8 frames.
Scale consistency. Same character size in all 8 cells of a row. Ask image_gen for a shared head height / body scale across the grid; slice_grid.py shares one scale across the row and the contact sheet surfaces any residual drift.
Visual identity checklist. Every frame must preserve the same age/proportions, hair silhouette, outfit/accessories, palette, and linework as the seed artifact. This is an eyeball pass on the contact sheet — there is no automated identity gate.
Alignment stability. Keep the character on a stable horizontal axis in every 192×208 frame; the pet must not hop left/right. Vertically align ordinary standing rows to a shared bottom baseline near cell_h - 8, not the vertical center. Confirm on the contact sheet / previews.
Plus: don't fake frames from the seed; grid-first, one row at a time; never whole-atlas generation; keep the magenta background perfectly flat.
Quality caveats for the recommended pattern:
Some rows and some tiers need more unique motion than others; add distinct frames whenever the loop reads weakly.
Reuse/mirroring is only acceptable if the prop remains clear, the row stays visibly animated, and loop closure does not feel cheap.
Script validation and human visual review both matter; either can reject a mechanically valid but visually weak mirrored closure.
Workflow (three sheets, in order)
Use the same pipeline per sheet — prepare → generate one 4×2 grid per row → slice_grid.py → compose → validate → slim QA gate (human reviews the contact sheet, not the model). Run it three times, in tier order. Seed source: a 192 × 208 neutral pose on a solid #FF00FF magenta background, or a --description (generate Codex idle first, use frame 1 as the seed artifact). Every row is on flat #FF00FF; the finished atlases are magenta-background and keyed by the user afterward.
# ---- Tier 1: Codex ----
python scripts/prepare_pet_run.py --seed <seed> --pet-name "My Pet" --tier codex
# use built-in image_gen to generate 9 codex 4x2 grids, then slice each into a# 1536x208 row strip with slice_grid.py before composing (paths caller-chosen/ephemeral)
python scripts/compose_atlas.py --rows-dir <codex-strips> --tier codex --out <work>/spritesheet.png
cwebp -lossless -exact <work>/spritesheet.png -o <work>/spritesheet.webp
python scripts/validate_atlas.py --atlas <work>/spritesheet.webp --tier codex --out-json <validation-json>
python scripts/make_contact_sheet.py --atlas <work>/spritesheet.webp --tier codex
python scripts/render_animation_previews.py --atlas <work>/spritesheet.webp --tier codex
python scripts/pre_install_qa_gate.py --atlas <work>/spritesheet.webp --tier codex
# ---- Tier 2: Lite-Basic (uses Codex/seed as style ref) ----
python scripts/prepare_pet_run.py --seed <seed> --pet-name "My Pet" --pet-id <slug> --tier lite-basic
# generate 9 lite-basic 4x2 grids, slice each into a 1536x208 strip via slice_grid.py
python scripts/compose_atlas.py --rows-dir <lite-basic-strips> --tier lite-basic --out <atlas-png>
cwebp -lossless -exact <atlas-png> -o <atlas-webp>
python scripts/validate_atlas.py --atlas <atlas-webp> --tier lite-basic --out-json <validation-json>
python scripts/make_contact_sheet.py --atlas <atlas-webp> --tier lite-basic
python scripts/render_animation_previews.py --atlas <atlas-webp> --tier lite-basic
python scripts/pre_install_qa_gate.py --atlas <atlas-webp> --tier lite-basic
# ---- Tier 3: Lite-Enhanced (REQUIRES the Basic sheet; attach BOTH the seed artifact AND the Basic sheet as refs) ----
python scripts/prepare_pet_run.py --seed <seed> --pet-name "My Pet" --pet-id <slug> --tier lite-enhanced
# generate 8 lite-enhanced 4x2 grids, slice each into a 1536x208 strip via slice_grid.py
python scripts/compose_atlas.py --rows-dir <lite-enhanced-strips> --tier lite-enhanced --out <atlas-png>
cwebp -lossless -exact <atlas-png> -o <atlas-webp>
python scripts/validate_atlas.py --atlas <atlas-webp> --tier lite-enhanced --out-json <validation-json>
python scripts/make_contact_sheet.py --atlas <atlas-webp> --tier lite-enhanced
python scripts/render_animation_previews.py --atlas <atlas-webp> --tier lite-enhanced
python scripts/pre_install_qa_gate.py --atlas <atlas-webp> --tier lite-enhanced
# ---- Write pet.json (metadata only; smart defaults for description) ----
python scripts/prepare_pet_run.py --write-pet-json --run-dir <work>/
pet.json generation — smart defaults for description:
prepare_pet_run.py --write-pet-json reads run-config.json and generates pet.json with id, displayName, description, and spritesheetPath.
If you provided --description during setup, that description is used directly.
If no description was provided, the script auto-generates a smart default from the pet name and style (e.g., "A pixel art My Pet companion for focused work and productivity."). This ensures pet.json is always complete and ready to import without requiring manual editing.
Per-row slice (run for every row before composing its tier; paths are caller-chosen/ephemeral):
Final step — keying is the user's. All three composed *.webp atlases still have their flat magenta background. Do not install them. Direct the user to https://codogotchi.app/studio to key each atlas (load → tune knobs → export transparent sheet), then install the three keyed sheets + pet.json and quit-reopen Codogotchi.
See references/animation-rows-codex.md and references/animation-rows-lite.md for per-row motion + props.
Fix a bad frame
If any frame in a grid is wrong, regenerate the whole grid for that row, re-run slice_grid.py, and re-run the QA scripts. Fix by regenerating the grid, never by editing individual frames.
Flat #FF00FF background, perfectly uniform across each atlas; no static rows (gated); loops close
Standing/status rows anchor body/feet with one low-amplitude moving element; locomotion rows (running-right, running-left) are a centered in-place stride cycle with stable scale/baseline/direction and all 8 frames distinct/evenly-spaced (no teleport frame)
Stable motion (paramount): standing/status rows keep body/feet anchored with one low-amplitude moving element; locomotion rows use a centered in-place stride cycle; each row avoids jitter, hopping, limb flailing, lateral travel, and static-static-static-jump timing
Character/content horizontal center is stable for standing/status rows; ordinary standing rows share a bottom foot baseline near cell_h - 8 (eyeballed)
Each prop-led row shows its single named prop clearly in all 8 frames
Character size consistent across all 8 frames of every row (eyeballed)
Per-frame visual QA passed: same age/proportions, hair silhouette, outfit/accessories, palette, and linework as the seed artifact
Validation JSON, contact sheet, previews, and pre_install_qa_gate.py pass for every atlas
Character consistent across all 26 rows; pet.json present with "spritesheetPath": "spritesheet.webp"
Every row generated as a single 4×2 grid (8 cells, no empty cell), sliced to a 1536×208 strip by slice_grid.py
Before saying done, report: rows generated or regenerated; validation command/result; contact sheet and preview directory paths for every atlas; known compromises. State clearly that the delivered atlases are magenta-background (pre-key) and the user must key them at https://codogotchi.app/studio before installing. If a tier was completed unusually quickly, state what was compressed, reused, or skipped. Validation alone is not QA.
