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Found 1,126 Skills
Build DAG-based AI pipelines connecting Gradio Spaces, HuggingFace models, and Python functions into visual workflows. Use when asked to create a workflow, build a pipeline, connect AI models, chain Gradio Spaces, create a daggr app, build multi-step AI applications, or orchestrate ML models. Triggers on: "build a workflow", "create a pipeline", "connect models", "daggr", "chain Spaces", "AI pipeline".
Build effective study habits with spaced repetition, active recall, and session tracking
Explains client-side routing, server-side routing, file-based routing, and navigation patterns in web applications. Use when implementing routing, understanding how navigation works in SPAs vs MPAs, or configuring routes in meta-frameworks.
Data engineering patterns for ETL pipelines, data warehousing, Apache Spark, and data quality validation
Use this skill when designing or implementing audio systems for games - sound effects, adaptive music, spatial/3D audio, and middleware integration with FMOD or Wwise. Triggers on sound design, audio implementation, adaptive music systems, spatial audio, HRTF, audio middleware setup, sound event architecture, audio mixing, dynamic soundscapes, and game audio optimization. Covers FMOD Studio, Audiokinetic Wwise, and engine-native audio APIs.
Geospatial Analysis provides workflows for satellite imagery processing, GIS operations with GeoPandas, spatial statistics, and Earth observation data analysis.
Query and analyze distributed traces and spans using DataPrime syntax. Use this skill whenever the user wants to investigate request latency, find slow operations, debug service-to-service calls, look up a trace ID, analyze span durations, check error spans, examine distributed traces, investigate OpenTelemetry/Jaeger tracing data, or query Coralogix spans in any way - even if they don't explicitly mention "DataPrime" or "cx spans".
Meeting manager persona for Spark. Meeting preparation, transcript review, follow-up drafts, and scheduling.
Builds Moran's I spatial autocorrelation workflows in CARTO. Triggers when the user mentions spatial autocorrelation, Moran's I, spatial dependency, spatial correlation, spatial outliers, HH HL LH LL quadrants, high-high clusters, low-low clusters, spatial weight matrix, "is there clustering", "are values spatially correlated", local indicators of spatial association, LISA, spatial randomness test, or wants to determine whether a variable exhibits spatial clustering, dispersion, or randomness across a gridded dataset. Also relevant when the user needs to classify locations into cluster types (HH, HL, LH, LL) rather than just identifying hotspots and coldspots.
Guides the user through spatial enrichment workflows — triggered by requests to enrich, add demographics, estimate population around locations, compute spatial features, sociodemographic analysis, "what's around" queries, buffer/isochrone + join patterns, or trade area enrichment.
Sparse4D for multi-camera temporal 3D object detection and tracking. Uses sparse queries with deformable attention across camera views and time for end-to-end 3D perception, with an instance bank for temporal tracking. Use when training, evaluating, exporting, quantizing, or running inference for a TAO Sparse4D model. Trigger phrases include "train Sparse4D", "multi-camera 3D detection", "temporal 3D tracker", "sparse query 3D perception".
Use when one Python service must send each agent's, tenant's, team's, or request's spans to its correct Arize space and project using application metadata. Covers dynamic OpenTelemetry routing for custom agent builders and multi-tenant applications, including register_with_routing, set_routing_context, multi-space tracing, and custom span routing.