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Found 1,564 Skills
Accelerate LLM inference using speculative decoding, Medusa multiple heads, and lookahead decoding techniques. Use when optimizing inference speed (1.5-3.6× speedup), reducing latency for real-time applications, or deploying models with limited compute. Covers draft models, tree-based attention, Jacobi iteration, parallel token generation, and production deployment strategies.
Use this skill when you writing commands, hooks, skills for Agent, or prompts for sub agents or any other LLM interaction, including optimizing prompts, improving LLM outputs, or designing production prompt templates.
Expert skill for AI model quantization and optimization. Covers 4-bit/8-bit quantization, GGUF conversion, memory optimization, and quality-performance tradeoffs for deploying LLMs in resource-constrained JARVIS environments.
Retrieval-Augmented Generation patterns including chunking, embeddings, vector stores, and retrieval optimizationUse when "rag, retrieval augmented, vector search, embeddings, semantic search, document qa, rag, retrieval, embeddings, vector, search, llm" mentioned.
Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use when "building RAG, vector search, embeddings, semantic search, document retrieval, context retrieval, knowledge base, LLM with documents, chunking strategy, pinecone, weaviate, chromadb, pgvector, rag, embeddings, vector-database, retrieval, semantic-search, llm, ai, langchain, llamaindex" mentioned.
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".
Audit websites for SEO, technical, content, and security issues using SEOmator CLI. Returns LLM-optimized reports with health scores, broken links, meta tag analysis, and actionable recommendations. Use when analyzing websites, debugging SEO issues, or checking site health.
Build and run evaluators for AI/LLM applications using Phoenix.
Use when building secure AI pipelines or hardening LLM integrations. Defense-in-depth implements 8 validation layers from edge to storage with no single point of failure.
Agentic workflow patterns for autonomous LLM reasoning. Use when building ReAct agents, implementing reasoning loops, or creating LLMs that plan and execute multi-step tasks.
Integrate Portkey AI Gateway into TypeScript/JavaScript applications. Use when building LLM apps with observability, caching, fallbacks, load balancing, or routing across 200+ LLM providers.
Extract structured information from unstructured text using LLMs with source grounding. Use when extracting entities from documents, medical notes, clinical reports, or any text requiring precise, traceable extraction. Supports Gemini, OpenAI, and local models (Ollama). Includes visualization and long document processing.