Loading...
Loading...
Found 109 Skills
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 assurance. Triggers: 'validation', 'Pydantic', 'schema', 'data quality'.
Data validation patterns including schema validation, input sanitization, output encoding, and type coercion. Use when implementing validate, validation, schema, form validation, API validation, JSON Schema, Zod, Pydantic, Joi, Yup, sanitize, sanitization, XSS prevention, injection prevention, escape, encode, whitelist, constraint checking, invariant validation, data pipeline validation, ML feature validation, or custom validators.
QA an analysis before sharing with stakeholders — methodology checks, accuracy verification, and bias detection. Use when reviewing an analysis for errors, checking for survivorship bias, validating aggregation logic, or preparing documentation for reproducibility.
Provides comprehensive guidance for input validation, data serialization, and ID management in backend APIs. This skill should be used when designing validation schemas, transforming request/response data, mapping database IDs to external identifiers, and ensuring type safety across API boundaries.
Generate code for EDC export validation, clinical data range checks, cross-form consistency checks, SDTM structure validation, controlled terminology verification, and define.xml generation. Use when the user asks to validate clinical trial data exports, check vital sign or lab value ranges, verify AE date consistency, validate SDTM datasets against CDISC rules, generate define.xml, or build an automated data review pipeline. Triggers include "EDC validation", "range check clinical data", "cross-form consistency", "SDTM validation", "controlled terminology check", "define.xml generation", "Medidata Rave export", "Oracle InForm", "Veeva Vault CDMS", "clinical data cleaning", "edit check", "data query", "SDTM structure check", "lab range check", "vital signs validation", "AE date check", "protocol deviation detection", "OpenCDISC", "Pinnacles 21", "P21 validation".
Use when invalid data causes failures deep in execution, requiring validation at multiple system layers - validates at every layer data passes through to make bugs structurally impossible
Provides practical Zod v4 validation utilities and schema patterns for TypeScript applications. Use when designing validation layers for API payloads, forms, configuration, and domain input parsing with strong type inference.
Migrates databases between providers (Postgres, MySQL, Supabase, PlanetScale, MongoDB). Reads source schema, generates migration scripts, handles data type mapping, foreign keys, indexes, triggers, stored procedures. Validates migration with row counts and checksums. Generates migration-plan.md with step-by-step execution guide, rollback procedures, estimated downtime.
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.
Creates dbt models following project conventions. Use when working with dbt models for: (1) Creating new models (any layer - discovers project's naming conventions first) (2) Task mentions "create", "build", "add", "write", "new", or "implement" with model, table, or SQL (3) Modifying existing model logic, columns, joins, or transformations (4) Implementing a model from schema.yml specs or expected output requirements Discovers project conventions before writing. Runs dbt build (not just compile) to verify.
Generates comprehensive synthetic fine-tuning datasets in ChatML format (JSONL) for use with Unsloth, Axolotl, and similar training frameworks. Gathers requirements, creates datasets with diverse examples, validates quality, and provides framework integration guidance.
Zod v4 schema validation for TypeScript. Covers primitives, string formats, objects, arrays, unions, coercion, transforms, refinements, parsing, type inference, error customization, JSON Schema, file validation, and metadata. Use when writing schemas, validating input, parsing data, inferring types, or converting schemas with Zod.