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Found 375 Skills
Async communication patterns using message brokers and task queues. Use when building event-driven systems, background job processing, or service decoupling. Covers Kafka (event streaming), RabbitMQ (complex routing), NATS (cloud-native), Redis Streams, Celery (Python), BullMQ (TypeScript), Temporal (workflows), and event sourcing patterns.
Use when writing or refactoring Ruby code that integrates Claude Code via the claude-agent-sdk gem (ClaudeAgentSDK.query, ClaudeAgentSDK::Client, streaming input, ClaudeAgentOptions configuration, tools/permissions, MCP servers, hooks, structured output, budgets, sandboxing, session resumption/rewind, and Rails patterns like jobs or ActionCable).
Quicknode blockchain infrastructure including RPC endpoints (80+ chains), Streams (real-time data), Webhooks, IPFS storage, Marketplace Add-ons (Token API, NFT API, DeFi tools), Solana DAS API (Digital Asset Standard), Key-Value Store, gRPC streaming (Yellowstone for Solana, Hypercore for Hyperliquid), and x402 pay-per-request RPC. Use when setting up blockchain infrastructure, configuring real-time data pipelines, processing blockchain events, storing data on IPFS, using Quicknode-specific APIs, querying Solana NFTs/tokens/compressed assets, persisting state with Key-Value Store, or building low-latency gRPC streams. Triggers on mentions of Quicknode, Streams, qn_ methods, IPFS pinning, Quicknode add-ons, DAS API, Digital Asset Standard, compressed NFT, cNFT, getAssetsByOwner, searchAssets, Key-Value Store, KV store, qnLib, Yellowstone, gRPC, Geyser, Hypercore, Hyperliquid, HYPE, evm, rpc, ethereum, blockchain, solana, or x402.
Use this skill when building real-time or near-real-time data pipelines. Covers Kafka, Flink, Spark Streaming, Snowpipe, BigQuery streaming, materialized views, and batch-vs-streaming decisions. Common phrases: "real-time pipeline", "Kafka consumer", "streaming vs batch", "low latency ingestion". Do NOT use for batch integration patterns (use integration-patterns-skill) or pipeline orchestration (use data-orchestration-skill).
Video/audio/image processing with FFmpeg and ImageMagick. Tools: FFmpeg (video/audio), ImageMagick (images). Capabilities: format conversion, encoding (H.264/H.265/VP9/AV1), streaming (HLS/DASH), filters, effects, thumbnails, watermarks, batch processing, hardware acceleration (NVENC/QSV). Actions: convert, encode, resize, crop, compress, extract, merge, stream, transcode media. Keywords: FFmpeg, ImageMagick, video encoding, audio extraction, image resize, thumbnail, watermark, HLS, DASH, H.264, H.265, VP9, AV1, codec, bitrate, framerate, resolution, aspect ratio, filter, overlay, concat, trim, fade, batch processing. Use when: converting video/audio formats, encoding with specific codecs, generating thumbnails, creating streaming manifests, extracting audio from video, batch processing images, adding watermarks, optimizing file sizes.
When the user wants to run CTV, OTT, or streaming TV ads. Also use when the user mentions "CTV ads," "connected TV," "OTT advertising," "streaming ads," "TV ads," "Hulu ads," "Roku ads," "YouTube TV ads," or "programmatic TV."
Use when the user wants tool use, MCP access, HTTP or streaming API exposure, auto-function helpers, or wait-for-key behavior through Agently-native extension surfaces rather than custom wrappers first.
Use this skill when building data pipelines, ETL/ELT workflows, or data transformation layers. Triggers on Airflow DAG design, dbt model creation, Spark job optimization, streaming vs batch architecture decisions, data ingestion, data quality checks, pipeline orchestration, incremental loads, CDC (change data capture), schema evolution, and data warehouse modeling. Acts as a senior data engineer advisor for building reliable, scalable data infrastructure.
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.
Turbo pipeline operations reference — lifecycle commands (pause, resume, restart, delete), pipeline states, checkpoint behavior, streaming vs job-mode differences, CLI syntax for `inspect`/`logs`, TUI shortcuts, and error pattern lookup. Triggers on: 'how do I pause/restart/delete', 'will deleting lose my data', 'what does this error mean', 'inspect TUI shortcuts'. For interactive diagnosis of a broken pipeline, use /turbo-doctor.
Develop Lakeflow Spark Declarative Pipelines (formerly Delta Live Tables) on Databricks. Use when building batch or streaming data pipelines with Python or SQL. Invoke BEFORE starting implementation.
Create data analytics and data pipeline diagrams using PlantUML syntax with analytics/database stencil icons. Best for ETL pipelines, data lakes, real-time streaming, data warehousing, and BI dashboards. NOT for simple flowcharts (use mermaid) or general cloud infra (use cloud skill).