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Found 38 Skills
Prepare inputs for MTHDS methods. Use when user says "prepare inputs", "create inputs", "use my files", "generate test data", "template", "synthesize inputs", "mock inputs", "I have a PDF/image/document to use", "make sample data", or wants to create inputs.json for running a .mthds pipeline. Handles user-provided files, synthetic data generation, placeholder templates, and mixed approaches. Defaults to automatic mode.
Generate sample security events, attack scenarios, and synthetic alerts for Elastic Security. Use when demoing, populating dashboards, testing detection rules, or setting up a POC.
Generate database seed scripts with realistic sample data. Reads Drizzle schemas or SQL migrations, respects foreign key ordering, produces idempotent TypeScript or SQL seed files. Handles D1 batch limits, unique constraints, and domain-appropriate data. Use when populating dev/demo/test databases. Triggers: 'seed database', 'seed data', 'sample data', 'populate database', 'db seed', 'test data', 'demo data', 'generate fixtures'.
Create diverse synthetic test inputs for LLM pipeline evaluation using dimension-based tuple generation. Use when bootstrapping an eval dataset, when real user data is sparse, or when stress-testing specific failure hypotheses. Do NOT use when you already have 100+ representative real traces (use stratified sampling instead), or when the task is collecting production logs.
Creates test fixtures, mock data, and test scenarios for unit and integration tests. Use when setting up test data, creating mocks, or generating test fixtures.
Generate mock data and test fixtures from schemas
Project structure initialization and metadata generation. This skill should be used when creating a new project, initializing project structure based on type (golang or social profile), or generating/updating README documentation.
Property-based testing with Hypothesis for discovering edge cases automatically. Use when testing invariants, finding boundary conditions, implementing stateful testing, or validating data transformations.
factory_boy test data generation specialist. Covers Factory, DjangoModelFactory, SQLAlchemyModelFactory, all field declarations (Faker, LazyAttribute, Sequence, SubFactory, RelatedFactory, post_generation, Trait, Maybe, Dict, List), batch creation, pytest integration, and Celery task testing patterns. USE WHEN: user mentions "factory_boy", "test factory", "DjangoModelFactory", "SQLAlchemyModelFactory", asks about "test data generation", "factory traits", "SubFactory", "factory fixtures". DO NOT USE FOR: pytest internals - use `pytest`; Django setup - use `pytest-django`; Hypothesis property testing - use `pytest` with Hypothesis
Create or update database seed scripts for development and testing environments. Use when setting up test data, initializing development databases, creating demo environments, resetting to known state, or generating realistic sample data.
This skill should be used when users need to work with the Vercel AI SDK for building AI-powered applications. It provides comprehensive guidance on core APIs (generateText, streamText), UI components (useChat, useCompletion), tool calling, structured data generation, provider management, streaming protocols, and advanced features like middleware and custom providers.
Salesforce data operations with 130-point scoring. Use this skill to create, update, delete, bulk import/export, generate test data, and clean up org records using sf CLI and anonymous Apex. TRIGGER when: user creates test data, performs bulk import/export, uses sf data CLI commands, needs data factory patterns for Apex tests, or needs to seed/clean records in a Salesforce org. DO NOT TRIGGER when: SOQL query writing only (use platform-soql-query), Apex test execution (use platform-apex-test-run), or metadata deployment (use platform-metadata-deploy).