安装

添加到你的 Claude Code skills 目录：

# Add to your Claude Code skills
git clone https://github.com/kimtth/awesome-azure-openai-llm

安全报告

已验证

上次扫描：—

{
  "status": "PASSED",
  "issues": []
}

README.md

Azure OpenAI + LLM

Name: awesome-azure-openai-llm
Author: kimtth

GitHub last commit GitHub Created At

A comprehensive, curated collection of resources for Azure OpenAI, Large Language Models (LLMs), and their applications.

🔹Concise Summaries: Each resource is briefly described for quick understanding
🔹Chronological Organization: Resources appended with date (first commit, publication, or paper release)
🔹Monthly Updates: The list is updated monthly; candidate entries before the update are tracked in the issue.

🧭 Quick Navigation (Propedia-style)

Layer / Era	What it controls	Jump to sections
Weights 2022-2023	Parametric knowledge baked into the model. `Themes: Pretraining, Scaling Laws, Fine-tuning, RLHF, Alignment, Instruction-following, Few-shot`	Foundations: Large Language Model Landscape, Large Language Model Collection, Foundation Model Providers Training: Large Language Model Training and Optimization, Model Training & Inference, Training & Fine-tuning Behavior and safety: Trust, Safety, and Security, Safety, Security & LLMOps
Context 2023-2024	What the model sees at inference time. `Themes: Prompting, Chain-of-Thought, RAG, Memory, Long Context, Knowledge Injection, Context Engineering`	Prompting: Prompt Engineering and Visual Prompts, Prompt Engineering & Tooling Retrieval: RAG, Azure AI Search, RAG Best Practices Memory and context windows: Context and Long-Context Limits, Memory, Data Processing & Memory
Harness 2025-2026	How the agent acts in the real world. `Themes: Function Calling, Tool Ecosystems, MCP, Skills, Workflow Graphs, Multi-agent, A2A protocols, Orchestration, Agent Infrastructure, Security`	Agent runtime: AI Application, Agent Frameworks, Agent Development, Agent Best Practices Protocols and tools: Agent Protocol, Coding & Research, Skills, Harness, Loop Engineering, Dev Tools, MCP & Extensions Apps and operations: Evaluating Large Language Models, LLMOps, Learning Resources & Workshops, Code Samples & Workshops

Refereces: DailyDoseOfDS - Evolution of the Agent Landscape

1. App & Agent

🚀 RAG Systems, LLM Applications, Agents, Frameworks & Orchestration

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2. Azure OpenAI & Copilot

🌌 Microsoft's Cloud-Based AI Platform and Services

Overview
- Azure OpenAI & Foundry Overview
Frameworks
- Orchestration Frameworks
- Agent Frameworks
Tooling
- Prompt Engineering & Tooling
- Dev Tools, MCP & Extensions
Products
Services
- Azure AI Search
- Microsoft Foundry & AI Services
Research
- Microsoft Research
Applications

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3. Research & Survey

🧠 LLM Landscape, Prompt Engineering, Finetuning, Challenges & Surveys

Landscape
Prompting
- Prompt Engineering and Visual Prompts
Training & Optimization
Impact & Products
Survey & Reference

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4. Datasets, Evaluation, and Extras

🛠️ Training Data, Datasets & Evaluation Methods

Data
- Datasets for LLM Training
Evaluation
Extras
- LLM for Robotics
- Awesome Demo

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5. Best Practices

📋 Curated Blogs, Patterns, and Implementation Guidelines

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🧭 Start Here

Category	Goal	Suggested path
RAG	Explore RAG patterns	RAG → GraphRAG → RAG Application → RAG Best Practices → RAG Research
AI Engineering	Build an AI Engineering Workflow	RAG → AI Application → Agent Protocol → Coding → Deep Research → Domain-Specific Agents → Skills → Harness → Loop Engineering
Skills & Harnesses	Extend a Coding Agent	Skills → Harness → Loop Engineering → Coding → Tool Use → Evaluation Metrics
Agents	Design an agent workflow	Top Agent Frameworks → Agent Design Patterns → Tool Use → Memory → Agent Research
Data & Analytics	Build a data or analytics agent	Data & Analytics Agents → Data Processing & OCR → Memory → Tool Use → Evaluating Large Language Models
Local LLMs	Build a local or self-hosted LLM application	Large Language Model Collection → Model Serving & Local Runtimes → Model Gateway → UI & No-Code Tool → Observability & LLMOps
MCP & Integration	Build MCP-enabled tools	Model Context Protocol → Dev Tools, MCP & Extensions → Safety, Security & LLMOps → Agent Best Practices
Developer Agents	Build coding or research agents	Coding → Deep Research → Skills → Harness → Loop Engineering → Tool Calling & Agentic
Azure / RAG	Build an Azure RAG application	Azure OpenAI & Foundry Overview → Azure AI Search → RAG Solution Design → Sample Applications → Evaluating Large Language Models
Azure / Agents	Build an Azure agent	Agent Frameworks → Agent Design Patterns → Model Context Protocol → Agent Development → Evaluating Large Language Models
Microsoft 365	Build a Microsoft 365 agent	Microsoft 365 Agent Development → Copilot Product Catalog → Dev Tools, MCP & Extensions → Agent Development
Production	Operate an AI application in production	Architecture Patterns & Use Cases → Safety, Security & LLMOps → LLMOps → Evaluating Large Language Models
Research	Learn the LLM landscape	Large Language Model Landscape → Survey and Reference → LLM Research
Model Development	Train or fine-tune a model	Large Language Model Collection → Model Training & Inference → Training & Fine-tuning → Datasets for LLM Training → Evaluating Large Language Models
Multimodal	Build a multimodal application	Multimodal Models → Data Processing & OCR → RAG Application → Vision & Multimodal
Evaluation	Choose and benchmark a model	Large Language Model Collection → Architecture Comparisons → Evaluating Large Language Models → LLM Evaluation Benchmarks → Evaluation Metrics

📖 Legend & Notation

Symbol	Meaning	Symbol	Meaning
	GitHub repository	🗄️	Archived files
💡🏆	Recommend	📺	Video content
📑	Academic paper	🤗	Huggingface

Info: Applications that have been archived or have had no commits for more than 12 months are listed in applications.old.md.

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常见问题

What is awesome-azure-openai-llm?⌄

awesome-azure-openai-llm is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by kimtth. A curated collection of resources for 🌌 Azure OpenAI, 🦙 LLMs (+RAG, Agents). Monthly Updates. It has 397 GitHub stars.

Is awesome-azure-openai-llm safe to use?⌄

Yes. awesome-azure-openai-llm passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.

How do I install awesome-azure-openai-llm?⌄

Clone the repository with "git clone https://github.com/kimtth/awesome-azure-openai-llm" and add it to your Claude Code skills directory (see the Installation section above).

What programming language is awesome-azure-openai-llm written in?⌄

awesome-azure-openai-llm is primarily written in Python. It is open-source under kimtth on GitHub, so you can review or fork the full source.

Are there alternatives to awesome-azure-openai-llm?⌄

Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh awesome-azure-openai-llm against similar tools.

awesome-azure-openai-llm

安装

快速入门

安全报告

README.md

Azure OpenAI + LLM

🧭 Quick Navigation (Propedia-style)

1. App & Agent

2. Azure OpenAI & Copilot

3. Research & Survey

4. Datasets, Evaluation, and Extras

5. Best Practices

🧭 Start Here

📖 Legend & Notation

常见问题

评论 (0)

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