Total 50,988 skills, AI & Machine Learning has 8538 skills
Showing 12 of 8538 skills
This skill should be used when the user asks to "build a RAG pipeline", "create retrieval augmented generation", "use ColBERTv2 in DSPy", "set up a retriever in DSPy", mentions "RAG with DSPy", "context retrieval", "multi-hop RAG", or needs to build a DSPy system that retrieves external knowledge to answer questions with grounded, factual responses.
Build LiveKit Agent backends in Python. Use this skill when creating voice AI agents, voice assistants, or any realtime AI application using LiveKit's Python Agents SDK (livekit-agents). Covers AgentSession, Agent class, function tools, STT/LLM/TTS models, turn detection, and multi-agent workflows.
Enable agents to use the Clawhub CLI (clawbub) to install, import, publish, and manage AgentSkills. Use when an agent needs to interact with Clawhub from the command line for skill development, publishing, or syncing.
Converts a Refound/Lenny Skill into a high-density, agent-executable Skill Pack (Agent Skills standard). Output must be in English.
Face swap and deepfake generation using ModelsLab's Deepfake API. Swap faces in images and videos with high-quality AI-powered face replacement technology.
Vision framework API, VNDetectHumanHandPoseRequest, VNDetectHumanBodyPoseRequest, person segmentation, face detection, VNImageRequestHandler, recognized points, joint landmarks, VNRecognizeTextRequest, VNDetectBarcodesRequest, DataScannerViewController, VNDocumentCameraViewController, RecognizeDocumentsRequest
Master fine-tuning of large language models for specific domains and tasks. Covers data preparation, training techniques, optimization strategies, and evaluation methods. Use when adapting models for specialized applications, reducing inference costs, or improving domain-specific performance.
Executes OpenAI Codex CLI for code analysis, refactoring, and automated editing. Activates when users mention codex commands, code review requests, or automated code transformations requiring advanced reasoning models.
End-to-end data science and ML engineering workflows: problem framing, data/EDA, feature engineering (feature stores), modelling, evaluation/reporting, plus SQL transformations with SQLMesh. Use for dataset exploration, feature design, model selection, metrics and slice analysis, model cards/eval reports, experiment reproducibility, and production handoff (monitoring and retraining).
Write structured VGL (Visual Generation Language) JSON prompts for Bria's FIBO image generation models. Use this skill when creating detailed image descriptions in JSON format for text-to-image generation, image editing, inpainting, outpainting, background generation, or captioning. Triggers include requests to write structured prompts, create VGL JSON, describe images for AI generation, or work with Bria/FIBO's structured_prompt format. Also use when converting natural language image requests into the deterministic JSON schema required by FIBO models.
An intelligent assistant designed for long-form online novel creation, supporting full-process management from setting generation to main text writing, including intelligent quality control and state synchronization. (Yunshu Optimized)
Design AI architectures, write Prompts, build RAG systems and LangChain applications