Data360 Segment

Implement, review, or improve data persistence using SwiftData. Use when defining @Model classes with @Attribute, @Relationship, @Transient, #Unique, or #Index; when querying with @Query, #Predicate, FetchDescriptor, or SortDescriptor; when configuring ModelContainer and ModelContext for SwiftUI or background work with @ModelActor; when planning schema migrations with VersionedSchema and SchemaMigrationPlan; when setting up CloudKit sync with ModelConfiguration; or when coexisting with or migrating from Core Data.

Answer questions by searching the compiled Obsidian wiki. Use this skill when the user asks a question about their knowledge base, wants to find information across their wiki, asks "what do I know about X", "find everything related to Y", or wants synthesized answers with citations from their wiki pages. Also use when the user wants to explore connections between topics in their wiki, or asks a multi-hop "how is X connected to Y", "what links X to Y", "trace the chain from X to Z", or "what does X depend on transitively" question — answered by walking typed edges across multiple hops. Works from any project. Includes an index-only fast mode triggered by "quick answer", "just scan", "don't read the pages", "fast lookup" — returns answers from page summaries and frontmatter without reading page bodies. Accepts inline named-vault routing like "wiki-query @work what do I know about X" via the shared Config Resolution Protocol.

Use the `superwall` CLI to manage apps, products, entitlements, campaigns, paywalls, App Store Connect, and ClickHouse analytics. Also covers documentation lookup, dashboard links, SDK source inspection, and integration, migration, review, and dashboard workflows. Use for Superwall API or CLI tasks, data analysis, SDK setup, provider migration, webhook events, and SDK debugging.

World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics. Expertise in Python (NumPy, Pandas, Scikit-learn), R, SQL, statistical methods, A/B testing, time series, and business intelligence. Includes experiment design, feature engineering, model evaluation, and stakeholder communication. Use when designing experiments, building predictive models, performing causal analysis, or driving data-driven decisions.

Generate high-quality images via Gemini models with structured prompts, aspect ratios, and brand validation.

Use when writing Spark jobs, debugging performance issues, or configuring cluster settings for Apache Spark applications, distributed data processing pipelines, or big data workloads. Invoke to write DataFrame transformations, optimize Spark SQL queries, implement RDD pipelines, tune shuffle operations, configure executor memory, process .parquet files, handle data partitioning, or build structured streaming analytics.