skill
Hatch Pet
نبذة
# Hatch Pet
## Overview
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.
## Generation Delegation
Use `$imagegen` for all normal visual generation.
Before generating base art, row strips, or repair rows, load and follow the installed image generation skill:
```text ${CODEX_HOME:-$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.
## Storage Controls
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:-$HOME/.codex}/generated_images`. Deleting files later reduces filesystem use, but it does not shrink an already-written rollout. Keep image generation isolated and bounded:
- Use one lightweight generation worker per visual job. Do not batch multiple base/row jobs into the same worker. - Workers must return only `selected_source=...` and `qa_note=...`; they must not include Markdown image previews, base64, or extra visual attachments in their final response. - The parent must not open every generated PNG visually. Use worker QA for each job and inspect only the final contact sheet. - After copying the selected generated output into `decoded/`, remove the selected original from `${CODEX_HOME:-$HOME/.codex}/generated_images` when it lives there, then remove its now-empty generation directory if possible. - For storage-sensitive full runs, ask the user whether to use the `$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.
## Brand Discovery
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.
Discovery worker responsibilities:
- search the web for 2-4 relevant sources, preferring official pages - write an adaptive markdown brief rather than a rigid field dump - cover identity/category, audience/use context, visual system, personality/tone, product/domain motifs, mascot translation cues, avoidances, and evidence/confidence - mark mascot guidance that is inferred from sources as inference - avoid copying logos, readable marks, UI screenshots, slogans, or text - end with a compact `Generation handoff` section containing only `brand_name`, `brand_brief`, `avatar_seed`, `avoid`, and `brand_sources` - do not generate images, prepare run folders, or edit unrelated files
Use this discovery worker prompt:
```text Research a brand for hatch-pet mascot creation.
Brand/product/prospect: <brand name> User context: <short user request> Output file: <absolute path to brand-discovery.md>
Use web search. Prefer official brand, product, docs, about, press, or brand pages. Use reputable secondary sources only if of
التثبيت
شغل هذا الأمر
git clone https://github.com/openai/skills && cp -r skills/skills/.curated/hatch-pet ~/.claude/skills/يعمل مع
خطوات التثبيت
Clone the repository and copy the `skills/.curated/hatch-pet` folder into your Claude skills directory. Compatible with Claude Code, Cursor, Codex, and any Agent Skills-compatible agent.
أسئلة شائعة
كيف أثبت Hatch Pet؟
شغل هذا الأمر في الطرفية:
git clone https://github.com/openai/skills && cp -r skills/skills/.curated/hatch-pet ~/.claude/skills/مع أي أدوات ذكاء اصطناعي تعمل Hatch Pet؟
تعمل مع claude_app، claude_code، claude_api، cursor، codex، windsurf، cline، zed.
من طور Hatch Pet؟
طورها OpenAI، وتصدر بترخيص Apache-2.0.
هل Hatch Pet مجانية؟
نعم، يمكنك استخدامها مجانا وفق ترخيص Apache-2.0.
npx skills add qu-skills/superpowers
npx skills add qu-skills/superpowers
npx skills add qu-skills/superpowers
npx skills add coreyhaines31/marketingskills
npx skills add coreyhaines31/marketingskills
npx skills add higgsfield-ai/skills
افحص قبل التثبيت
شغل أي مصدر عبر فحوصاتنا - الظهور في الذكاء الاصطناعي والأمان والأداء واكتشاف التقنيات.
فحص أمني تلقائي للموقع
الأمان
محلل سرعة الصفحة
الأداء
اختبار جودة المحتوى العربي بالذكاء الاصطناعي
جودة المحتوى
مختبر وكلاء الذكاء الاصطناعي
اختبار الذكاء الاصطناعي
كاشف منصة الموقع
الترحيل
تدقيق الظهور في محركات الذكاء الاصطناعي
الظهور في الذكاء الاصطناعي
مولد ملف llms.txt
الظهور في الذكاء الاصطناعي
مقياس سهولة القراءة بالعربية
جودة المحتوى
منشئ البيانات المنظمة
الظهور في الذكاء الاصطناعي
حاسبة تكاليف الذكاء الاصطناعي
اختبار الذكاء الاصطناعي
محلل العناوين العربية
جودة المحتوى