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Found 398 Skills
Expert-level Apache Kafka, event streaming, Kafka Streams, and distributed messaging
DEPRECATED - Use chatkit-backend skill instead. SSE streaming is now part of the chatkit-backend skill for ChatKit integration.
tvOS platform-specific development with focus system, large screen UI, Siri Remote, and media playback. Use when building Apple TV apps, video streaming, or living room experiences.
Use when running live streaming campaigns on Xiaohongshu, selling products through live commerce, hosting live events, or using live streaming for brand promotion and engagement
Transcribe audio to text using Sarvam AI's Saaras model. Handles speech recognition, transcription, and voice interfaces for 23 Indian languages. Supports 5 output modes, auto language detection, WebSocket streaming, and batch diarization. Use when converting speech to text or building voice-enabled apps.
Design and architect Goldsky Turbo pipelines. Use this skill for 'should I use X or Y' decisions: kafka source vs dataset source, streaming vs job mode, which resource size (xs/s/m/l/xl/xxl) for my workload, postgres vs clickhouse vs kafka sink, fan-in vs fan-out data flow, one pipeline vs many, dynamic table vs SQL join, how to handle multi-chain deployments. Also use when the user asks 'what's the best way to...' for a pipeline design problem, or is unsure how to structure their pipeline before building it.
Expert guidance for building production-grade AI agents and workflows using Pydantic AI (the `pydantic_ai` Python library). Use this skill whenever the user is: writing, debugging, or reviewing any Pydantic AI code; asking how to build AI agents in Python with Pydantic; asking about Agent, RunContext, tools, dependencies, structured outputs, streaming, multi-agent patterns, MCP integration, or testing with Pydantic AI; or migrating from LangChain/LlamaIndex to Pydantic AI. Trigger even for vague requests like "help me build an AI agent in Python" or "how do I add tools to my LLM app" — Pydantic AI is very likely what they need.
Run 397B parameter Mixture-of-Experts LLMs on a MacBook using pure C/Metal with SSD streaming
AI Elements component library guidance — pre-built React components for AI interfaces built on shadcn/ui. Use when building chat UIs, message displays, tool call rendering, streaming responses, reasoning panels, or any AI-native interface with the AI SDK.
Real-time and streaming AI image generation — instant results for interactive use. Use when the user requests "Real-time generation", "Fast generation", "Streaming image", "Instant image", "Live generation", "Realtime".
Manages MongoDB Atlas Stream Processing (ASP) workflows. Handles workspace provisioning, data source/sink connections, processor lifecycle operations, debugging diagnostics, and tier sizing. Supports Kafka, Atlas clusters, S3, HTTPS, and Lambda integrations for streaming data workloads and event processing. NOT for general MongoDB queries or Atlas cluster management. Requires MongoDB MCP Server with Atlas API credentials.
Braze platform help — Canvas Flow journey orchestration, email/push/in-app/SMS/WhatsApp/Content Cards campaigns, BrazeAI (predictive, generative, agentic), Braze Data Platform (CDI, Currents), real-time segmentation, Catalogs, Feature Flags, transactional email API, Liquid templating, Connected Content, Braze Alloys integrations, SCIM, REST API. Use when asking 'how do I do X in Braze', configuring Canvas flows, building segments, setting up Currents data streaming, using the Braze API, or migrating from Appboy. Do NOT use for building prospect lists (use /sales-prospect-list), designing outbound cadence strategy (use /sales-cadence), cross-platform deliverability (use /sales-deliverability), transactional email strategy (use /sales-transactional-email), push notification strategy (use /sales-push-notification), in-app messaging strategy (use /sales-in-app-messaging), or email marketing strategy (use /sales-email-marketing).