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Everything your AI needs, in one place.

Ready-made agents, skills, personas, prompts, templates and tools. Each one is checked before it goes live, works with any model, and installs in a click. Rate what you use so the best rises to the top.

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8 results
Skill

byok-custom-model

Register a custom LLM endpoint with your own API key for chat in Starchild. Use when adding a personal Anthropic, OpenAI, Grok, Qwen, DeepSeek, Meta (Muse Spark), NEAR AI, or Venice key as a chat mode

by starchild-ai-agentskills.sh
Not rated yet
Free
Skill

moe-training

Train Mixture of Experts (MoE) models using DeepSpeed or HuggingFace. Use when training large-scale models with limited compute (5× cost reduction vs dense models), implementing sparse architectures l

by davila7skills.sh
Not rated yet
Free
Skill

thinking-liangwenfeng

蒸馏梁文峰(DeepSeek/幻方量化)思维模式的实用框架:中国量化先驱、AI+量化融合、极致效率

by aaaaqwqskills.sh
Not rated yet
Free
Skill

moe-training

Train Mixture of Experts (MoE) models using DeepSpeed or HuggingFace. Use when training large-scale models with limited compute (5× cost reduction vs dense models), implementing sparse architectures l

by orchestra-researchskills.sh
Not rated yet
Free
Skill

pdf-ocr-skill

支持四引擎的PDF OCR识别技能,可从影印版PDF文件和图片文件中提取中英文文字内容 | PDF OCR Skill with quadruple-engine support, capable of extracting Chinese and English text from scanned PDF files and image files

by yejinleiskills.sh
Not rated yet
Free
Skill

moe-training

Train Mixture of Experts (MoE) models using DeepSpeed or HuggingFace. Use when training large-scale models with limited compute (5× cost reduction vs dense models), implementing sparse architectures l

by ovachieverskills.sh
Not rated yet
Free
Skill

moe-training

Train Mixture of Experts (MoE) models using DeepSpeed or HuggingFace. Use when training large-scale models with limited compute (5× cost reduction vs dense models), implementing sparse architectures l

by ihatesea69GitHub
Not rated yet
Free
Skill

moe-training

Train Mixture of Experts (MoE) models using DeepSpeed or HuggingFace. Use when training large-scale models with limited compute (5× cost reduction vs dense models), implementing sparse architectures l

by nota-americaGitHub
Not rated yet
Free
1

Find

Search or browse by kind. Every card shows who made it, how many people installed it and what they think.

2

Install

One click. You get a manifest the router understands, plus copy-paste snippets for the CLI, Python and YAML.

3

Rate and publish

Leave a star rating after you have used it. Made something useful? Publish it - free listings go live immediately.

Prefer the terminal? osr stack apply registry://starter installs the starter template.