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Found 142 Skills
Security & compliance skill suite with OWASP scanning, CVE detection, GDPR audits, SOC2 readiness, threat modeling, and incident response workflows
End-to-end retail ETL pipeline using PySpark, SQL Server, and Medallion Architecture (Bronze/Silver/Gold layers) for data warehousing
Free 9-week data engineering course covering Docker, Terraform, Kestra, BigQuery, dbt, Spark, and Kafka with hands-on projects
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.
Transform raw data into analytical assets using ETL/ELT patterns, SQL (dbt), Python (pandas/polars/PySpark), and orchestration (Airflow). Use when building data pipelines, implementing incremental models, migrating from pandas to polars, or orchestrating multi-step transformations with testing and quality checks.
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.
Transform raw data into analytical assets using ETL/ELT patterns, SQL (dbt), Python (pandas/polars/PySpark), and orchestration (Airflow). Use when building data pipelines, implementing incremental models, migrating from pandas to polars, or orchestrating multi-step transformations with testing and quality checks.
Data engineering skill for building scalable data pipelines, ETL/ELT systems, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, and modern data stack. Includes data modeling, pipeline orchestration, data quality, and DataOps. Use when designing data architectures, building data pipelines, optimizing data workflows, implementing data governance, or troubleshooting data issues.
Data pipeline expert for ETL, Apache Spark, Airflow, dbt, and data quality
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.
V8 JIT Compilation, TurboFan, Maglev, Sparkplug. Load this when needing to understand V8's compilation pipeline, JIT optimization, or JITless mode.
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.