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Found 92 Skills
Comprehensive guide for implementing Syncfusion WPF Sparkline (SfSparkline) controls in Windows Presentation Foundation applications. Use this when working with sparklines, mini charts, or trend visualization. This skill covers sparkline types (line, column, area, WinLoss), markers, track ball, range bands, axis controls, and segment customization for compact data visualization in WPF applications.
Guide users through the Amore CLI for macOS app distribution — setup, releasing, code signing, notarization, DMG creation, S3 hosting, Sparkle updates, licensing, and configuration. Use this skill whenever the user mentions Amore, amore CLI, macOS app distribution outside the App Store, Sparkle updater setup, appcast.xml, notarization workflows, DMG creation, or self-publishing macOS apps. Also use when the user asks about release automation, S3 bucket hosting for app updates, EdDSA signing keys, or licensing with Stripe for macOS apps.
Diagnose, compare, and optimize Apache Spark applications and SQL queries using Spark History Server data. Use this skill whenever the user wants to understand why a Spark app is slow, compare two benchmark runs or TPC-DS results, find performance bottlenecks (skew, GC pressure, shuffle spill, straggler tasks), get tuning recommendations, or optimize Spark/Gluten configurations. Also trigger when the user mentions 'diagnose', 'compare runs', 'why is this query slow', 'tune my Spark job', 'benchmark comparison', 'performance regression', or asks about executor skew, shuffle overhead, AQE effectiveness, or Gluten offloading issues.
Develops and executes Spark code on Dataproc Clusters and Serverless. Reads and writes data using BigLake Iceberg catalogs, BigQuery and Spanner. Debugs execution failures. Use when: - Writing Spark ETL pipelines on GCP. - Training or running inference with ML models with spark on GCP. - Managing Spark clusters, jobs, batches, and interactive sessions. Don't use when: - Writing generic Python scripts that don't use Spark. - Performing simple SQL queries that can be done directly in BigQuery.
Migrate Databricks workloads from classic compute to serverless compute. Scans code for serverless compatibility issues, provides concrete fixes for the serverless Spark Connect architecture, and guides the full migration to serverless environments. Use for classic-to-serverless migrations, serverless code compatibility checks, or writing new serverless-compatible notebooks and jobs. Not for classic DBR version upgrades or cluster configuration changes within classic compute.
Use when reading from or writing to Neo4j with Apache Spark or Databricks using the Neo4j Connector for Apache Spark (org.neo4j:neo4j-connector-apache-spark). Covers SparkSession setup, DataFrame reads via labels/Cypher/relationship scan, DataFrame writes with SaveMode, node.keys for MERGE, relationship write mapping, partition and batch tuning, PySpark and Scala examples, Databricks cluster config, Databricks secrets for credentials, Delta Lake to Neo4j pipelines. Does NOT handle Cypher authoring — use neo4j-cypher-skill. Does NOT handle the Python bolt driver — use neo4j-driver-python-skill. Does NOT handle GDS algorithms — use neo4j-gds-skill.
Implement Syncfusion React Sparkline components for compact, inline data visualization. Use this when working with sparklines, mini charts, or trend indicators in constrained spaces. This skill covers all 5 sparkline types (line, column, area, win-loss, pie), tooltips, markers, data labels, range bands, axis customization, and themes. Ideal for displaying data trends within grids, dashboards, or tables without full-sized charts.
Implement Syncfusion Angular Sparkline component for compact data visualization. Use this skill whenever the user needs to create sparkline charts, visualize small datasets inline, add markers or data labels, implement different sparkline types (Line, Column, Area, Pie, Win-Loss), or handle sparkline customization like tooltips, axis settings, and theme styling. Covers installation, basic rendering, type selection, marker configuration, data label formatting, advanced features, accessibility, and migration from EJ1.
Freelancer / solo operator persona for Spark. Multi-client management, invoice follow-ups, availability, and quick responses.
Generate a paid social creative brief from a whitelisted or Spark Ad creator post, covering hook analysis, messaging angle, audience targeting, caption variants, and placement recommendations. This skill should be used when turning a creator post into a paid ad brief, writing a creative brief for whitelisted content, briefing the paid team on creator content, generating Spark Ads briefs from organic posts, creating paid media briefs from influencer content, translating UGC into a paid social strategy, building a media buyer brief from a creator video, preparing whitelisted content for ad spend, or generating placement recommendations for boosted creator content. For adapting captions into ad copy variants, see paid-ad-copy-adapter. For organic repost captions, see organic-repost-caption-writer. For FTC compliance, see ftc-disclosure-spot-checker.
Comprehensive guide to Spark Structured Streaming for production workloads. Use when building streaming pipelines, implementing real-time data processing, handling stateful operations, or optimizing streaming performance.
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.