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Found 92 Skills
Install and configure NVIDIA NemoClaw (sandboxed OpenClaw agent platform) on Linux. Handles cloudflared tunnels, Docker cgroup fixes, OpenShell, sandbox creation, remote access via Cloudflare Tunnel, and known bug workarounds. Triggers: "install nemoclaw", "setup nemoclaw", "nvidia nemoclaw", "openclaw setup", "nemoclaw on spark", "nemoclaw on dgx".
Guide for creating data visualizations in Shopify Apps using the Polaris Viz library. Use this skill when building charts, graphs, dashboards, or any data visualization components that need to integrate with the Shopify Admin aesthetic. Covers BarChart, LineChart, DonutChart, SparkLineChart, and theming.
Community registry of agent configurations for the AIBTC platform — browse reference configs for arc0btc, spark0btc, iris0btc, loom0btc, and forge0btc, or copy the template to bootstrap a new agent.
Build scalable data pipelines, modern data warehouses, and real-time streaming architectures. Implements Apache Spark, dbt, Airflow, and cloud-native data platforms. Use PROACTIVELY for data pipeline design, analytics infrastructure, or modern data stack implementation.
Expert-level Databricks platform, Apache Spark, Delta Lake, MLflow, notebooks, and cluster management
Implement end-to-end Medallion Architecture (Bronze/Silver/Gold) lakehouse patterns in Microsoft Fabric using PySpark, Delta Lake, and Fabric Pipelines. Use when the user wants to: (1) design a Bronze/Silver/Gold data lakehouse, (2) set up multi-layer workspace with lakehouses for each tier, (3) build ingestion-to-analytics pipelines with data quality enforcement, (4) optimize Spark configurations per medallion layer, (5) orchestrate Bronze-to-Silver-to-Gold flows via notebooks. Triggers: "medallion architecture", "bronze silver gold", "lakehouse layers", "e2e data pipeline", "end-to-end lakehouse", "data lakehouse pattern", "multi-layer lakehouse", "build medallion", "setup medallion".
Build event streaming and real-time data pipelines with Kafka, Pulsar, Redpanda, Flink, and Spark. Covers producer/consumer patterns, stream processing, event sourcing, and CDC across TypeScript, Python, Go, and Java. When building real-time systems, microservices communication, or data integration pipelines.
Implement, review, or improve data visualizations using Swift Charts. Use when building bar, line, area, point, pie, or donut charts; when adding chart selection, scrolling, or annotations; when plotting functions with vectorized BarPlot, LinePlot, AreaPlot, or PointPlot; when customizing axes, scales, legends, or foregroundStyle grouping; or when creating specialized visualizations like heat maps, Gantt charts, stacked/grouped bars, sparklines, or threshold lines.
Firecrawl produces cleaner markdown than WebFetch, handles JavaScript-heavy pages, and avoids content truncation. This skill should be used when fetching URLs, scraping web pages, converting URLs to markdown, extracting web content, searching the web, crawling sites, mapping URLs, LLM-powered extraction, autonomous data gathering with the Agent API, or fetching AI-generated documentation for GitHub repos via DeepWiki. Provides complete coverage of Firecrawl v2.8.0 API endpoints including parallel agents, spark-1-fast model, and sitemap-only crawling.
Implement Syncfusion Windows Forms Sparkline controls for compact data visualization. Use this when working with sparklines, trend displays in condensed format, or high-density data visualization. This skill covers line, column, and WinLoss chart types, data point markers, high/low value highlighting, and lightweight graphical representations in Windows Forms applications.
Manage the full lifecycle of Alibaba Cloud EMR Serverless StarRocks instances — create, scale, configure, maintain and diagnose. Use this Skill when operations engineers, SREs, or architects need to manage StarRocks instances. Typical scenarios include: "create a StarRocks", "check instance status", "scale up CU", "modify configuration", "restart instance", "diagnose issues", etc. Not applicable for: writing SQL/DDL, data import/export, query tuning, materialized view configuration, or managing non-StarRocks products (EMR clusters, Spark, Milvus, ClickHouse, Doris, RDS, ECS).
Databricks SQL query optimizer: analyzes a slow SQL query, rewrites it for speed using SQL-level optimizations only, validates byte-for-byte result equivalence, and benchmarks both versions with statistical significance testing. Use this skill whenever the user wants to optimize, speed up, tune, or benchmark a SQL query on Databricks. Trigger on: "/databricks-sql-autotuner", "optimize this SQL", "make this query faster", "tune my Databricks query", "benchmark SQL on Databricks", "speed up this spark SQL", "SQL performance on Databricks", "EXPLAIN this query", "why is my query slow on Databricks", "SQL query optimization Databricks", or whenever a user pastes a SQL query and mentions performance, slowness, or runtime.