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Found 63 Skills
Create Pydantic models following the multi-model pattern with Base, Create, Update, Response, and InDB variants. Use when defining API request/response schemas, database models, or data validation in Python applications using Pydantic v2.
Shuffle repetitive JSON objects safely by validating schema consistency before randomising entries.
Create safe, reversible database migration scripts with rollback capabilities, data validation, and zero-downtime deployments. Use when changing database schemas, migrating data between systems, or performing large-scale data transformations.
Security-first WordPress development with nonces, sanitization, validation, and escaping to prevent XSS, CSRF, and SQL injection vulnerabilities.
Design and generate Convex database schemas with proper validation, indexes, and relationships. Use when creating schema.ts or modifying table definitions.
Working effectively with JSON data structures.
Standardize and format phone numbers with international support, validation, and multiple output formats.
Compare two datasets to find differences, added/removed rows, changed values. Use for data validation, ETL verification, or tracking changes.
Python data validation using type hints and runtime type checking with Pydantic v2's Rust-powered core for high-performance validation in FastAPI, Django, and configuration management.
Expert in data pipelines, ETL processes, and data infrastructure
Guidance for counting tokens in datasets, particularly from HuggingFace or similar sources. This skill should be used when tasks involve counting tokens in datasets, understanding dataset schemas, filtering by categories/domains, or working with tokenizers. It helps avoid common pitfalls like incomplete field identification and ambiguous terminology interpretation.
Validate at every layer data passes through to make bugs impossible. Use when invalid data causes failures deep in execution, requiring validation at multiple system layers.