apify-generate-output-schema

Generate Apify JSON schemas by analyzing your Actor's source code to discover output fields and types, then create dataset_schema.json and output_schema.json files. The process marks all fields as nullable, adds additionalProperties, includes empty required arrays, and wires the files back into actor.json.

Broken Apify console displays usually occur when dataset_schema.json and output_schema.json do not match the actual fields pushed by your Actor. Generating schemas directly from source code ensures the console accurately reflects your dataset output and key-value store artifacts.

Apify requires all dataset and output schema fields to be marked as nullable, with additionalProperties and empty required arrays added at the correct schema levels. These JSON Schema validation patterns ensure the console displays Actor run results without UI errors.

Yes, generating key-value store schemas is supported alongside dataset and output schemas. The process analyzes your Actor code to enumerate key-value store keys and produces key_value_store_schema.json that wires back into actor.json for complete storage validation.

Yes, schema generation aligns with repository conventions by reusing existing schema patterns from other Actors. This keeps formatting, naming, and examples consistent across your Apify project while ensuring generated examples remain anonymized and free of real user identifiers.

The best way to update actor.json schemas is to regenerate dataset_schema.json and output_schema.json by analyzing your updated Actor source code. This discovers new fields and types automatically, applies required nullability, and wires the resulting files back into actor.json.