schema▌

You are generating output schema files for an Apify Actor. The output schema tells Apify Console how to display run results. You will analyze the Actor's source code, create dataset_schema.json, output_schema.json, and key_value_store_schema.json (if the Actor uses key-value store), and update actor.json.

Guides implementation of Schema.org structured data (JSON-LD) for rich snippets, enhanced search results, and Generative Engine Optimization (GEO).

Industry best practices guide for designing intuitive, performant, and maintainable GraphQL schemas. \n \n Covers core design principles including client-centric type organization, explicit nullability patterns, and backwards-compatible evolution strategies \n Provides reference documentation on types, naming conventions, cursor-based pagination, error modeling, and security considerations \n Includes practical patterns for interfaces, unions, input types, mutations, and ID strategies with code

Design and generate Convex database schemas with proper validation, indexes, and relationships. \n \n Provides schema templates and patterns for one-to-many, many-to-many, and hierarchical relationships using ID references instead of nesting \n Includes validator reference for all Convex types ( v.string() , v.id() , v.union() , etc.) and guidance on when to use arrays versus relational structures \n Covers index strategy with single-field and compound index examples for common query patterns \n

Production-ready SQL and NoSQL schemas with normalization, indexing, and migration strategies built in. \n \n Generates complete schemas from natural language descriptions, covering entities, relationships, constraints, and indexes tailored to SQL or NoSQL databases \n Provides normalization guidance (1NF through 3NF), foreign key strategies with ON DELETE rules, and composite index planning based on access patterns \n Includes anti-patterns reference (FLOAT for money, missing FK indexes, non-re

Add, fix, and optimize schema markup to enable rich search results and help search engines understand page content. \n \n Covers all major schema types: Organization, Article, Product, FAQPage, HowTo, BreadcrumbList, LocalBusiness, Event, and SoftwareApplication with required and recommended properties \n Uses JSON-LD format (Google's recommended approach) with support for multiple schema types on one page via @graph \n Includes validation against Google Rich Results Test and Schema.org Validato

Generate Schema.org JSON-LD markup for search engine rich results and AI understanding. \n \n Supports 10+ schema types including FAQPage, HowTo, Article, Product, LocalBusiness, Organization, BreadcrumbList, Event, and Recipe with validation against Google Rich Results requirements \n Maps content to appropriate schema types based on page purpose (blog, product, FAQ, local business, etc.) and identifies eligible rich result opportunities \n Generates valid JSON-LD with all required and optional

Type-safe database schema definition with indexes, validation, and migration strategies for Convex. \n \n Supports 13+ validator types including strings, numbers, booleans, document references, arrays, objects, unions, and discriminated unions for flexible data modeling \n Enables single-field and compound indexes plus full-text search indexes for optimized query performance \n Provides optional and nullable field patterns with clear migration paths for adding required fields and backfilling dat

Design scalable, normalized database schemas with proper relationships, constraints, and data types. Includes normalization techniques, relationship patterns, and constraint strategies.

Comprehensive database schema design patterns for PostgreSQL and MySQL with normalization, relationships, constraints, and error prevention.