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Found 127 Skills
Format prompts for different LLM providers with chat templates and HNSW-powered context retrieval
Manage shell hooks — user scripts that run at agent lifecycle points to block, rewrite, or warn on actions, via the /hooks command.
AI and machine learning development with PyTorch, TensorFlow, and LLM integration. Use when building ML models, training pipelines, fine-tuning LLMs, or implementing AI features.
Backend development agent for Resume Matcher. Handles FastAPI endpoints, Pydantic schemas, TinyDB operations, LiteLLM integration, and Python service logic. Use when creating or modifying backend code.
Step-by-step guide for adding support for a new LLM in Dust. Use when adding a new model, or updating a previous one.
Comprehensive skill for building, deploying, and managing multi-agent AI systems with Agno framework
Expert in CrewAI - the leading role-based multi-agent framework used by 60% of Fortune 500 companies. Covers agent design with roles and goals, task definition, crew orchestration, process types (sequential, hierarchical, parallel), memory systems, and flows for complex workflows. Essential for building collaborative AI agent teams. Use when: crewai, multi-agent team, agent roles, crew of agents, role-based agents.
Complete reference for the Portkey AI Gateway Python SDK with unified API access to 200+ LLMs, automatic fallbacks, caching, and full observability. Use when building Python applications that need LLM integration with production-grade reliability.
Provides comprehensive guidance for Spring AI Alibaba including Alibaba Cloud AI services integration, model APIs, and AI application development. Use when the user asks about Spring AI Alibaba, needs to use Alibaba Cloud AI services, or integrate AI capabilities in Spring applications.
Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration
Expert in Natural Language Processing, designing systems for text classification, NER, translation, and LLM integration using Hugging Face, spaCy, and LangChain. Use when building NLP pipelines, text analysis, or LLM-powered features. Triggers include "NLP", "text classification", "NER", "named entity", "sentiment analysis", "spaCy", "Hugging Face", "transformers".
Build AI-powered Ruby applications with RubyLLM. Full lifecycle - chat, tools, streaming, Rails integration, embeddings, and production deployment. Covers all providers (OpenAI, Anthropic, Gemini, etc.) with one unified API.