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claude-sdk-expert
Build autonomous AI agents using Claude Agent SDK with computer use, tool calling, MCP integration, and production best practices
Skills IA tools llm ai
Découvrez les skills tools llm ai, comparez leurs dépôts et consultez leurs instructions.
Catégories: tools llm ai
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Explorer
GitHub
azure-ai-services-expert
Build AI applications on Azure using Azure OpenAI, Cognitive Services, and ML services with enterprise patterns
GitHub
genai-dac-specialist
Expert in OCI Generative AI Dedicated AI Clusters - deployment, fine-tuning, optimization, and production operations
GitHub
openai-agentkit-expert
Build production-ready multi-agent systems using OpenAI AgentKit and Agents SDK with best practices for agent orchestration, handoffs, and routines
GitHub
oracle-agent-spec-expert
Design framework-agnostic AI agents using Oracle's Open Agent Specification for portable, interoperable agentic systems with JSON/YAML definitions
GitHub
multi-cloud-ai-architect
Design and deploy AI workloads across AWS, Azure, GCP, and OCI with intelligent routing, cost optimization, and cross-cloud patterns
GitHub
langgraph-patterns-expert
Build production-grade agentic workflows with LangGraph using graph-based orchestration, state machines, human-in-the-loop, and advanced control flow
GitHub
agentic-orchestration
Patterns for multi-agent coordination, task decomposition, handoffs, and workflow orchestration. Best practices for building and managing agent systems.
GitHub
skill-template
Template for creating new Claude Code skills following official specification. Provides structure, YAML frontmatter format, and best practices for skill development.
GitHub
claude-sdk-expert
Build autonomous AI agents using Claude Agent SDK with computer use, tool calling, MCP integration, and production best practices
GitHub
rag-expert
Expert in Retrieval-Augmented Generation systems - knowledge bases, chunking strategies, embedding optimization, and production RAG architectures
GitHub
huggingface-model-trainer
Train and fine-tune LLMs using HuggingFace TRL, Transformers, and cloud GPU infrastructure with SFT, DPO, GRPO methods
GitHub
azure-ai-services-expert
Build AI applications on Azure using Azure OpenAI, Cognitive Services, and ML services with enterprise patterns
GitHub
agentic-orchestration
Patterns for multi-agent coordination, task decomposition, handoffs, and workflow orchestration. Best practices for building and managing agent systems.
GitHub
nvidia-nim
NVIDIA NIM inference microservices for deploying AI models with OpenAI-compatible APIs, self-hosted or cloud
GitHub
aws-ai-services-expert
Build AI applications on AWS using Bedrock, SageMaker, and AI/ML services with best practices for enterprise deployment
GitHub
ai-security-expert
Enterprise AI security - OWASP LLM Top 10, prompt injection defense, guardrails, PII protection
GitHub
multi-cloud-ai-architect
Design and deploy AI workloads across AWS, Azure, GCP, and OCI with intelligent routing, cost optimization, and cross-cloud patterns
GitHub
genai-dac-specialist
Expert in OCI Generative AI Dedicated AI Clusters - deployment, fine-tuning, optimization, and production operations
GitHub
advanced-statusline
Implement AI-powered statusline with session tracking, plan detection, workspace emojis, and intelligent caching for Claude Code
GitHub
notebooklm-audio
LEGACY SKILL - Manual NotebookLM web interface workflow. The primary workflow now uses notebooklm-enterprise-api for automated audio generation. Use this skill only when the API is unavailable.
GitHub
notebooklm-enterprise-api
Generate podcast audio using NotebookLM Enterprise API via Google Cloud. Automates the manual NotebookLM workflow - creates notebook, uploads sources, generates audio with custom focus prompt. Requires Google C...
GitHub
chatgpt-deep-research
DEPRECATED - Use gpt-researcher skill instead. This browser automation approach has been replaced with the local GPT-Researcher framework using OpenAI o1.
GitHub
podcast-audio-processing
Process NotebookLM audio files: convert, transcribe, add chapters, and prepare for publishing.
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.