Skill UI

CrewAI orchestrates autonomous multi-agent teams for role-based collaboration, sequential or hierarchical workflows, and production-grade orchestration with memory and tool integrations without LangChain.

High-performance Rust-backed tokenization toolkit with Python bindings, offering BPE/WordPiece/Unigram, custom vocabulary training, alignment tracking, and production-ready integrations for NLP pipelines.

Automate Insighto AI operations by discovering tool schemas via Rube MCP, validating Insighto AI connections, and executing workflows through Composio’s toolkit to keep integrations resilient.

This tool allows developers to integrate advanced AI video generation (using Kling AI) directly into continuous integration and continuous deployment pipelines (e.g., GitHub Actions, GitLab CI). Automate the creation of marketing videos, product demos, or regression video testing whenever code changes or a release occurs, ensuring consistent and timely video asset updates.

Master the LangChain framework, the industry standard for building sophisticated and production-grade Large Language Model (LLM) applications. This skill covers core concepts including autonomous agents, multi-step chains, memory management, Retrieval-Augmented Generation (RAG), and external tool integration, enabling the development of complex, stateful AI systems.

A comprehensive diagnostic toolkit designed for advanced troubleshooting of Lindy AI agents. This bundle systematically collects environment information (OS, Python, Node.js), tests API connectivity for webhooks and major integrations (Slack, Notion, Gmail), reviews task history, and guides the user in generating a full, structured support ticket for technical support.

Practical guide to integrating Claude models: covers API client usage, streaming responses, tool calls, RAG pipelines, document chunking, cost-aware model selection, and anti-pattern warnings for efficient LLM workflows.

Automate OpenAI API workflows through the Composio MCP integration, covering text and multimodal responses with structured outputs, embeddings, image generation, and model discovery.

This comprehensive guide covers all aspects of integrating the Google Gemini API into your applications using Python or Node.js. It details basic text generation, advanced multimodal inputs (text, image, audio), streaming responses, function calling/tool use, multi-turn chat implementation, and production best practices like model selection and robust error handling.

A comprehensive guide for developers to build advanced, declarative agents for Microsoft 365 Copilot. This skill details the integration process using a Model Context Protocol (MCP) server, allowing the agent to connect with external systems, select tools, and handle complex authentication (OAuth 2.0/SSO). You will learn to generate key JSON manifests required for deployment.

scvi-tools is a comprehensive Python framework for probabilistic deep generative models in single-cell genomics. Built on PyTorch and PyTorch Lightning, it enables batch correction, transfer learning, differential expression analysis with uncertainty, and multi-modal data integration. Best for advanced modeling of single-cell RNA-seq, ATAC-seq, CITE-seq, spatial transcriptomics, and specialized modalities like methylation and cytometry.

This comprehensive guide provides a deep dive into migrating AI applications from major LLM providers (like OpenAI or Anthropic) to the Cohere platform. It covers critical technical steps, including API call adaptation, re-vectorizing embeddings, migrating tool definitions, and implementing the Adapter Pattern for seamless, gradual re-platforming of existing AI services.