Total 30,483 skills, AI & Machine Learning has 4926 skills
Showing 12 of 4926 skills
Skill for working with the Lucid Agents SDK - a TypeScript framework for building and monetizing AI agents. Use this skill when building or modifying Lucid Agents projects, working with agent entrypoints, payments, identity, or A2A communication. Activate when: Building or modifying Lucid Agents projects, working with agent entrypoints, payments, identity, or A2A communication, developing in the lucid-agents monorepo, creating new templates or CLI features, or questions about the Lucid Agents architecture or API.
Optimizing vector embeddings for RAG systems through model selection, chunking strategies, caching, and performance tuning. Use when building semantic search, RAG pipelines, or document retrieval systems that require cost-effective, high-quality embeddings.
Create custom Genfeed nodes using the SDK. Triggers on "create a new node", "add a custom node type", "build a node for X".
Generate optimized prompts for AI image and video generation. Triggers on "generate a prompt for", "write me a prompt", "create an image prompt", "create a video prompt", "optimize this prompt".
Generate rich personality profiles from social media data exports (Twitter/X, LinkedIn, Instagram). Use when a user wants to analyze their social media presence, create a personality profile for AI personalization, understand their communication patterns, or extract insights from their digital footprint. Triggers on requests like "analyze my Twitter data", "create a personality profile", "what can you learn about me from my posts", "personalize an AI for me", or when users provide social media export files.
Quick onboarding for Genfeed focused on first content creation. Triggers on "how do I use genfeed", "getting started", "what is this app", "help me create my first content".
Data pipelines, feature stores, and embedding generation for AI/ML systems. Use when building RAG pipelines, ML feature serving, or data transformations. Covers feature stores (Feast, Tecton), embedding pipelines, chunking strategies, orchestration (Dagster, Prefect, Airflow), dbt transformations, data versioning (LakeFS), and experiment tracking (MLflow, W&B).
Strategic guidance for operationalizing machine learning models from experimentation to production. Covers experiment tracking (MLflow, Weights & Biases), model registry and versioning, feature stores (Feast, Tecton), model serving patterns (Seldon, KServe, BentoML), ML pipeline orchestration (Kubeflow, Airflow), and model monitoring (drift detection, observability). Use when designing ML infrastructure, selecting MLOps platforms, implementing continuous training pipelines, or establishing model governance.
Analyze other agents' sessions and construct targeted corrective prompts to fix mistakes, correct context drift, or drive home task requirements
LLM and ML model deployment for inference. Use when serving models in production, building AI APIs, or optimizing inference. Covers vLLM (LLM serving), TensorRT-LLM (GPU optimization), Ollama (local), BentoML (ML deployment), Triton (multi-model), LangChain (orchestration), LlamaIndex (RAG), and streaming patterns.
Vector database implementation for AI/ML applications, semantic search, and RAG systems. Use when building chatbots, search engines, recommendation systems, or similarity-based retrieval. Covers Qdrant (primary), Pinecone, Milvus, pgvector, Chroma, embedding generation (OpenAI, Voyage, Cohere), chunking strategies, and hybrid search patterns.
Connect to Genfeed.ai to create AI videos, images, articles, and more. Use when "genfeed", "create content", "generate video", "generate image", "publish content" mentioned.