Loading...
Loading...
Found 73 Skills
Activated when the user wants to create a data model, validate data, serialize JSON, create Pydantic models, add validators, define settings, or create request/response schemas. Covers Pydantic v2 BaseModel, Field, validators, data validation, JSON schema generation, serialization, deserialization, and settings management.
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.
pytest, data validation, Great Expectations, and quality assurance for data systems
Search and extract contact information for people or companies including names, phone numbers, emails, job titles, and LinkedIn profiles. Aggregates data from multiple sources and provides enriched contact details. Use when users need to find contact information, build prospect lists, or enrich existing contact data.
Use to define schemas, topic tags, and lineage metadata for enriched signals.
Automatically discover data pipeline and ETL skills when working with ETL, data pipelines, streaming, batch processing, data validation, or pipeline orchestration. Activates for data development tasks.
Probability, distributions, hypothesis testing, and statistical inference. Use for A/B testing, experimental design, or statistical validation.
Use when invalid data causes failures deep in execution - validates at every layer data passes through to make bugs structurally impossible rather than temporarily fixed
Professional Pydantic v2.12 development for data validation, serialization, and type-safe models. Use when working with Pydantic for (1) creating or modifying BaseModel classes, (2) implementing validators and serializers, (3) configuring model behavior, (4) handling JSON schema generation, (5) working with settings management, (6) debugging validation errors, (7) integrating with ORMs or APIs, or (8) any production-grade Python data validation tasks. Includes complete API reference, concept guides, examples, and migration patterns.
Digital archiving workflows with AI enrichment, entity extraction, and knowledge graph construction. Use when building content archives, implementing AI-powered categorization, extracting entities and relationships, or integrating multiple data sources. Covers patterns from the Jay Rosen Digital Archive project.
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.
Use when building joi schemas, custom validators, extensions, or working with joi's validation pipeline. Covers all types, references, templates, errors, and the extension API.