Awesome Codex Pet is a community gallery and source catalogue of installable animated pets made for Codex, with previews, creator information, and collection metadata. Codex users can browse, install, request, and contribute pets across supported desktop platforms. The catalogue entries provide skills and an instruction for working with this pet collection.
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.
npx skills add legeling/awesome-codex-pet --skill hatch-pet-v2git clone --depth 1 https://github.com/legeling/awesome-codex-petWrote 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.
[](https://agentmods.dev/skills/legeling/awesome-codex-pet/hatch-pet-v2)<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.
<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>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00103 | $0.19749 |
| Opus 5.5 | $0.00041 | $0.07900 |
| Sonnet 5.5 | $0.00021 | $0.03950 |
| Haiku 4.5 | $0.00010 | $0.01975 |
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 26d ago.
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.
How it starts
The opening of the file, as written. The whole thing — 958 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
0-8intermediate after deterministic and visual validation, then generate rows9-10and 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 treat000as neutral.
What ships with it
31 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.
- agents/openai.yaml 252 B
- LICENSE.txt 11 KB
- references/animation-rows.md 2.5 KB
- references/codex-pet-contract.md 1.3 KB
- references/qa-rubric.md 2.5 KB
- scripts/assemble_extended_atlas.py 26 KB runs code
- scripts/background_utils.py 5.0 KB runs code
- scripts/combine_direction_blind_verdicts.py 1.9 KB runs code
- scripts/compose_atlas.py 5.6 KB runs code
- scripts/compose_cardinal_anchor_strip.py 1.4 KB runs code
- scripts/derive_running_left_from_running_right.py 5.1 KB runs code
- scripts/despill_chroma_edges.py 20 KB runs code
- scripts/extract_cardinal_anchors.py 4.8 KB runs code
- scripts/extract_strip_frames.py 14 KB runs code
- scripts/inspect_frames.py 9.4 KB runs code
- scripts/make_contact_sheet.py 3.6 KB runs code
- scripts/make_direction_blind_qa_sheet.py 5.2 KB runs code
- scripts/make_direction_qa_sheet.py 4.8 KB runs code
- scripts/measure_direction_continuity.py 6.5 KB runs code
- scripts/prepare_pet_run.py 52 KB runs code
- scripts/render_animation_previews.py 2.5 KB runs code
- scripts/validate_atlas.py 12 KB runs code
- scripts/validate_direction_blind_verdicts.py 3.7 KB runs code
- scripts/verify_native_alpha.py 1.3 KB runs code
- tests/test_assemble_extended_atlas.py 9.4 KB runs code
- tests/test_chroma_matte_decontamination.py 11 KB runs code
- tests/test_direction_acceptance_policy.py 7.6 KB runs code
- tests/test_direction_blind_consensus.py 1.5 KB runs code
- tests/test_look_row_safe_box_prompt.py 4.2 KB runs code
- tests/test_native_alpha_pipeline.py 14 KB runs code
- tests/test_single_final_chroma_pass.py 1.5 KB runs code
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.
- 26d ago Changed · +16 lines 68d6e5fb56f8
- 1mo ago Changed · +4 lines 411fe9ab2d2d
- 1mo ago First seen · 938 lines · 103 tokens per session scan A d930148adb25
hatch-pet-v2 is a skill published in the GitHub repository legeling/awesome-codex-pet (1,177 stars, last pushed yesterday), licensed MIT. It adds 103 tokens to every session and 19,749 once invoked, about $0.0004 per session on Opus 5.5. 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.
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