Create, repair, QA, and package Codex-compatible animated pet spritesheets.
This skill appears to be an open-source pet spritesheet workflow with no required secrets and no declared remote endpoints, so the overall risk is low. The main consideration is local image/file handling during generation, but the provided materials show no clear red flags for data exfiltration, credential abuse, or supply-chain compromise.
The materials explicitly state that no keys or environment variables are required. There is no sign of API keys, tokens, account credentials, or instructions to collect or transmit secrets, so credential exposure appears low.
The listing declares no remote endpoint hosts. The README references an installed `$imagegen` system skill rather than directly contacting named third-party hosts, and the provided materials do not show evidence of sending user data to unknown or unrelated external endpoints.
The README says it uses bundled scripts for deterministic spritesheet assembly and mentions parent-owned shell/`jq` steps for validation, packaging, and cleanup. This implies local script/command execution within the workflow. That is a normal tool capability, and the materials do not show requests for system privileges beyond the stated purpose.
The documentation explicitly references reading a local skill file at `${CODEX_HOME:-$HOME/.codex}/skills/.system/imagegen/SKILL.md` and writing, copying, and deleting generated images under paths such as `${CODEX_HOME:-$HOME/.codex}/generated_images` and `decoded/`. This indicates limited local file access and cleanup, but based on the materials the scope appears tied to generated assets and skill directories rather than excessive access.
The source is the GitHub repository openai/skills, and the system marks it as open-source with roughly 22k community stars. That provides strong positive trust signals because the source is reviewable and widely adopted. The unspecified license and unknown maintenance status are minor caveats, but they are not by themselves high-risk red flags.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "hatch-pet" skill from askskill: 1. Download https://raw.githubusercontent.com/openai/skills/main/skills/.curated/hatch-pet/SKILL.md 2. Save it as ~/.claude/skills/hatch-pet/SKILL.md 3. Reload skills and tell me it's ready
Create a Codex-compatible animated pet from a concept, brand cue, company/prospect name, one or more reference images, or any combination of those inputs. This workflow keeps the deterministic hatch-pet pipeline for atlas geometry, validation, visual QA, and packaging, while using concise state-specific prompts and allowing any pet-safe visual style.
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.
Use $imagegen for all normal visual generation.
Before generating base art, row strips, or repair rows, load and follow the installed image generation skill:
${CODEX_HOME:-$HOME/.codex}/skills/.system/imagegen/SKILL.md
Do not call the Image API, image CLI, or any other image-generation path directly. Let $imagegen choose its own built-in-first path and fallback rules. If $imagegen says a fallback requires confirmation, ask the user before continuing.
When invoking $imagegen, pass the generated pet prompt as the authoritative visual spec. Pet prompts should stay concise, state-specific, sprite-production oriented, and grounded in the listed input images. Keep longer policy and QA rules in this skill and the deterministic review scripts rather than expanding them into every image prompt. Do not wrap prompts in the generic $imagegen shared prompt schema.
Use this skill's scripts for deterministic image work only: preparing layout guides and prompts, mirroring approved running-left, extracting frames, validating rows, composing the final atlas, and creating contact-sheet plus motion-preview QA media. Parent-owned shell/jq steps handle manifest updates, packaging, and cleanup.
The built-in $imagegen path stores generated PNG bytes in the rollout that invokes it, even when it also writes a file under ${CODEX_HOME:-$HOME/.codex}/generated_images. Deleting files later reduces filesystem use, but it does not shrink an already-written rollout. Keep image generation isolated and bounded:
selected_source=... and qa_note=...; they must not include Markdown image previews, base64, or extra visual attachments in their final response.decoded/, remove the selected original from ${CODEX_HOME:-$HOME/.codex}/generated_images when it lives there, then remove its now-empty generation directory if possible.$imagegen CLI fallback when available. That path requires local API credentials and explicit user confirmation, but it can avoid built-in image payloads being embedded in rollout events.If the user provides a brand, company, product, or prospect name rather than a concrete avatar description or reference image, run a lightweight discovery subagent before preparing the pet run. The discovery worker must use web search and prefer official sources such as the brand site, product pages, docs, about pages, press pages, or brand pages. Use reputable secondary sources only when official pages are too thin. Keep the search narrow: enough to extract visual and personality cues, not a market-research brief.
Skip discovery when the user already provides a concrete mascot/avatar description or reference images, unless the user explicitly asks for brand research.
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