Explore skills

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.

PostgreSQL patterns for reviewing migrations and writing efficient queries. Use when reviewing Alembic migrations, optimizing queries, or debugging database issues.

Design a PostgreSQL-specific schema. Covers best-practices, data types, indexing, constraints, performance patterns, and advanced features

PostgreSQL database optimization workflow for query tuning, indexing strategies, performance analysis, and production database management.

Design a PostgreSQL-specific schema. Covers best-practices, data types, indexing, constraints, performance patterns, and advanced features

Prisma ORM expert for schema design, migrations, query optimization, relations modeling, and database operations. Use PROACTIVELY for Prisma schema issues, migration problems, query performance, relation design, or database connection issues.

Expert guidance for Prisma ORM v7 (7.0+). Use when working with Prisma schema files, migrations, Prisma Client queries, database setup, or when the user mentions Prisma, schema.prisma, @prisma/client, database models, or ORM. Covers ESM modules, driver adapters, prisma.config.ts, Rust-free client, and migration from v6.

Run PostgreSQL queries and meta-commands via CLI.

Async/await patterns for FastAPI and SQLAlchemy. Use when working with async code, database sessions, concurrent operations, or debugging async issues in Python.

Pydantic v2 patterns for validation and serialization. Use when creating schemas, validating data, or working with request/response models.

Data validation and settings management using Python type annotations with Pydantic v2

Complete mass spectrometry analysis platform. Use for proteomics workflows feature detection, peptide identification, protein quantification, and complex LC-MS/MS pipelines. Supports extensive file formats and algorithms. Best for proteomics, comprehensive MS data processing. For simple spectral comparison and metabolite ID use matchms.