Type-stable vector operations and custom vector classes using vctrs. Use this skill when building R packages with custom types, need guaranteed output types regardless of input values, implementing consistent coercion/casting rules, or creating vector classes that work seamlessly with data frames. Covers when to use…
Object-oriented programming in R: S7, S3, S4, and vctrs class design. Use this skill when designing classes for R projects, choosing between OOP systems, building class hierarchies with inheritance, or migrating between systems. Covers S7 class definitions and methods, the decision matrix for choosing S7 vs S3 vs S4…
Building robust R packages with modern tidyverse patterns. Use this skill when creating or maintaining R packages, designing APIs, choosing dependencies, implementing input validation, writing error messages, or deciding between internal and exported functions. Covers dependency strategy, tidyverse API design…
Tidy evaluation and programmatic tidyverse patterns using rlang. Use this skill when writing functions that accept column names as arguments, building tidyverse-compatible APIs, or working with data-masking and injection operators. Covers embracing with {{}}, injection (!! and !!!), dynamic dots, .data/.env pronouns…
R performance profiling, benchmarking, and optimization strategies. Use this skill when code is running slowly, comparing alternative implementations, deciding between dplyr/data.table/base R, or implementing parallel processing. Covers profvis and bench usage, performance workflow, parallel processing with…
Modern tidyverse patterns, style guide, and migration guidance for R development. Use this skill when writing R code with dplyr, reviewing tidyverse code, updating legacy R code to modern patterns, or enforcing consistent style. Covers native pipe usage, joinby() syntax, .by grouping, pick/across/reframe operations…
Database object patterns for views, stored procedures, functions, triggers, transactions, and dynamic SQL. Use this skill when designing database logic, managing data integrity with transactions, building reusable SQL components, or implementing audit and automation patterns. Covers views, materialized views, stored…
Window functions, analytical queries, and advanced aggregation patterns for business intelligence and reporting. Use this skill when writing ranking queries, running totals, moving averages, percentile calculations, period-over-period comparisons, gap-and-island analysis, or pivot/crosstab transformations. Covers all…
Schema design, normalization, constraints, indexing, and data warehouse modeling patterns. Use this skill when designing database schemas, choosing between normalized and denormalized structures, implementing star schemas, handling slowly changing dimensions, defining constraints, or planning index strategies. Covers…
Patterns for connecting to databases from Python, R, and dbt — covering ORMs, connection management, parameterized queries, and data pipeline best practices.
Core SQL query patterns for SELECT, JOINs, WHERE, GROUP BY, and subqueries. Use this skill when writing basic to intermediate SQL queries, need to understand join types, or work with filtering and aggregation. Covers SELECT fundamentals, all join types, subqueries, CTEs, CASE expressions, and common table operations.
Query performance tuning, EXPLAIN plans, indexing strategies, and optimization techniques. Use this skill when queries are slow, you need to read execution plans, design indexes, write sargable predicates, optimize JOINs, implement partitioning, or maintain database statistics. Covers PostgreSQL, MySQL, and SQL Server…
ETL patterns, data cleaning, type conversions, and string/date manipulation for data transformations. Use this skill when building data pipelines, cleaning messy data, standardizing formats, or performing complex transformations. Covers data quality patterns, type conversions, string operations, date/time handling…
Patterns for building and managing cloud data infrastructure on AWS and GCP using Infrastructure as Code, data lake architectures, cost optimization, and security best practices.
Data quality validation, observability, and monitoring for data pipelines. Use this skill when implementing data quality checks with Great Expectations or Soda Core, designing schema contracts, building anomaly detection, or establishing data observability practices. Covers validation frameworks, quality metrics…
Streaming data patterns for event-driven architectures and real-time processing. Use this skill when building Kafka pipelines, implementing CDC, designing event sourcing systems, or working with stream processing frameworks like Flink and Kafka Streams. Covers delivery guarantees, backpressure, dead letter queues, and…
Testing patterns for data engineering pipelines and transformations. Use this skill when writing tests for SQL transforms, dbt models, data contracts, pipeline integration tests, or managing test data. Covers pytest-sql, dbt testing, contract testing, regression testing, and synthetic data generation for reliable data…
Patterns and best practices for cloud data warehouses (Snowflake, BigQuery, Redshift), lakehouse architectures, Data Vault 2.0, and ELT pipeline design.
Production-ready patterns for continuous integration and continuous deployment pipelines across GitHub Actions, GitLab CI, and general pipeline design principles.
Docker containerization patterns including Dockerfile best practices, Compose orchestration, image optimization, networking, volumes, and security hardening for production workloads.
Monitoring, observability, and alerting for production systems. Use this skill when implementing structured logging, Prometheus metrics, OpenTelemetry tracing, alerting strategies, SLOs/SLIs, or dashboard design. Covers the three pillars of observability, PromQL, distributed tracing, error budgets, and alert fatigue…
Terraform infrastructure-as-code patterns covering module design, state management, resource lifecycle, variables, CI/CD integration, and migration strategies for AWS and GCP.
Advanced Git operations for daily development workflows. Use this skill when performing interactive rebases, recovering lost commits, configuring hooks, managing worktrees, working with stashes, or setting up Git LFS. Covers reflog recovery, autosquash, hook automation, parallel development with worktrees, and large…
Git workflow patterns for team collaboration and release management. Use this skill when setting up branching strategies, writing commit messages, managing pull requests, resolving merge conflicts, or automating releases. Covers trunk-based development, Conventional Commits, rebasing strategies, and semantic…