hatch-pet-v2

hatch-pet-v2 is a skill for Codex from legeling/awesome-codex-pet. It costs 103 tokens per session (19,085 once invoked), scanned A, original, MIT.

A workflow for creating, repairing, checking, and packaging animated Codex pets from artwork, brand references, or generated images. A spritesheet is one image containing the separate frames used for animation.

In plain words
What is it for?
Use it to create a new mascot, adapt a character or brand style, upgrade an existing pet, validate animation frames, and package a compatible pet.
Why use it?
It turns visual references into pets with the required animation layout and package format, while checking the result before delivery. It can also repair existing pet files.

Skill for Codex

Written for Codex: agents/openai.yaml present. Also seen: mentions subagents; installed under .agents/ (shared by several agents); mentions Codex.

Good fit Use it to create a new mascot, adapt a character or brand style, upgrade an existing pet, validate animation frames, and package a compatible pet.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/legeling/awesome-codex-pet/hatch-pet-v2
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add legeling/awesome-codex-pet --skill hatch-pet-v2
Clone the repo
git clone --depth 1 https://github.com/legeling/awesome-codex-pet

Made for: Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for hatch-pet-v2

README.md
[![agentmods](https://agentmods.dev/badge/skills/legeling/awesome-codex-pet/hatch-pet-v2/github.svg)](https://agentmods.dev/skills/legeling/awesome-codex-pet/hatch-pet-v2)
Your own site
<a href="https://agentmods.dev/skills/legeling/awesome-codex-pet/hatch-pet-v2"><img src="https://agentmods.dev/badge/skills/legeling/awesome-codex-pet/hatch-pet-v2/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for hatch-pet-v2

Your own site · 80×15
<a href="https://agentmods.dev/skills/legeling/awesome-codex-pet/hatch-pet-v2"><img src="https://agentmods.dev/badge/skills/legeling/awesome-codex-pet/hatch-pet-v2.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 103 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 19,085 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00103 $0.19085
Opus 5 $0.00051 $0.09543
Sonnet 5 $0.00021 $0.03817
Haiku 4.5 $0.00010 $0.01909

Measured 9d ago against content hash d930148adb25, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

hatch-pet-v2 scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

The scan reads SKILL.md. This mod also ships 25 executable files (scripts/assemble_extended_atlas.py, scripts/background_utils.py, scripts/combine_direction_blind_verdicts.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.agents/skills/hatch-pet-v2/SKILL.md · 938 lines

How it starts

The opening of the file, as written. The whole thing — 938 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Hatch Pet v2

Overview

Create a Codex-compatible v2 animated pet from a concept, brand cue, company/prospect name, one or more reference images, or any combination of those inputs. Every newly hatched pet is an 8x11 atlas with the 9 standard animation rows plus 16 clockwise look directions and is packaged with spriteVersionNumber: 2. The intermediate 8x9 atlas exists only to assemble and review rows 0-8; never package it as a new pet.

User-facing inputs are optional. If the user omits a pet name, infer one from the concept, brand, company, or reference filenames; if that is not possible, choose a short friendly name. If the user omits a description, infer one from the concept or references. If the user omits reference images, generate the base pet from text first, then use that base as the canonical reference for every animation row.

Existing Inputs And Upgrades

Treat character art, generated images, standard or v2 atlases, contact sheets, and built-in pet art as first-class grounding inputs.

  • Preserve user-provided art as a generation reference; do not assume it already has final cell geometry.
  • For an existing valid 8x9 atlas, use it as the rows 0-8 intermediate after deterministic and visual validation, then generate rows 9-10 and package the result as v2.
  • For an existing 8x11 atlas, preserve approved standard rows. If a look cell fails, correct the complete containing 8-frame row before deterministic reassembly. Never package a newly generated one-off repair cell beside cells from another generation.
  • For a built-in pet, extract and use its atlas or neutral/idle cell as the canonical identity reference.
  • Include every image that defines head shape, face, palette, markings, material, flame/ears/hair, props, or look mechanics in look-direction generation.
  • When a renderer or source provides a dedicated neutral/front frame, pass it through --neutral-cell; otherwise use the approved idle/default frame. The 16 directional cells never treat 000 as neutral.

Read the full file on GitHub · 938 lines

Files

What ships with it

30 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 9d ago First seen · 938 lines · 103 tokens per session scan A d930148adb25

Subscribe to this mod's changes

hatch-pet-v2 is a skill published in the GitHub repository legeling/awesome-codex-pet (869 stars, last pushed yesterday), licensed MIT. It adds 103 tokens to every session and 19,085 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.