parquet-coder
Columnar file patterns including partitioning, predicate pushdown, and schema evolution.
Discover devops data engineering skills, compare source repositories and read the instructions for each workflow.
Columnar file patterns including partitioning, predicate pushdown, and schema evolution.
Record-level data validation using Pydantic models. Field validators, model validators, and batch validation patterns.
Production ETL patterns orchestrator. Routes to core reliability patterns and incremental load strategies.
DataFrame schema validation using pandera. Schema definitions, column checks, and decorator-based validation.
dbt (data build tool) patterns for model organization, incremental strategies, and testing.
Data validation patterns and pipeline helpers. Custom validation functions, schema evolution, and test assertions.
Generate comprehensive data profiles for DataFrames. Use for EDA, data discovery, and understanding dataset characteristics.
DataFrame manipulation with chunked processing, memory optimization, and vectorized operations.
Generate synthetic test data with edge cases for ETL pipeline testing.
Incremental data loading patterns including backfill strategies, CDC, timestamp-based loads, and pipeline orchestration.
This skill guides configuring Litestream for continuous SQLite backup in Rails 8+ apps. Use when setting up production backups for SQLite databases (Solid Queue, Solid Cache, Solid Cable).
Data validation using Great Expectations. Expectation suites, checkpoints, and data docs for pipeline monitoring.
Core ETL reliability patterns including idempotency, checkpointing, error handling, chunking, retry logic, and logging.
This skill should be used when the user asks about Polars DataFrame library (Apache Arrow) for Python or Rust. Triggers: "polars expressions", "lazy vs eager", "scan_parquet streaming", "convert pandas to polar...
Build resilient data ingestion pipelines from APIs. Use when creating scripts that fetch paginated data from external APIs (Twitter, exchanges, any REST API) and need to track progress, avoid duplicates, handle...
Designs optimal filtering UX for data tables. Use when building a table that needs filters - analyzes the data columns and determines the best filter type for each. Outputs a unified filter field with inline he...
A comprehensive guide for interacting with PostgreSQL 16 databases. Use this skill for constructing standard and advanced SQL queries, optimizing performance, debugging errors, managing schema objects, and intr...
Execute evidence-based decision-making through 6-stage causal flow. Use for known processes, operational execution, and decisions with clear cause-effect chains.
Design database schemas for Grey Haven multi-tenant SaaS - SQLModel models, Drizzle schema, multi-tenant isolation with tenant_id and RLS, timestamp fields, foreign keys, indexes, migrations, and relationships....
Apply Grey Haven database conventions - snake_case fields, multi-tenant with tenant_id and RLS, proper indexing, migrations for Drizzle (TypeScript) and SQLModel (Python). Use when designing schemas, writing da...
Create comprehensive ontological documentation for Grey Haven systems - extract domain concepts from TanStack Start and FastAPI codebases, model semantic relationships, generate visual representations of system...
Comprehensive data validation using Pydantic v2 with data quality monitoring and schema alignment for PlanetScale PostgreSQL. Use when implementing API validation, database schema alignment, or data quality ass...
Transform data into compelling narratives using visualization, context, and persuasive structure. Use when presenting analytics to stakeholders, creating data reports, or building executive presentations.
Expert database optimizer specializing in modern performance tuning, query optimization, and scalable architectures. Masters advanced indexing, N+1 resolution, multi-tier caching, partitioning strategies, and c...
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