apify-generate-output-schema

Generate Actor output 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.

Core principles

• Analyze code first: Read the Actor's source to understand what data it actually pushes to the dataset — never guess
• Every field is nullable: APIs and websites are unpredictable — always set "nullable": true
• Anonymize examples: Never use real user IDs, usernames, or personal data in examples
• Verify against code: If TypeScript types exist, cross-check the schema against both the type definition AND the code that produces the values
• Reuse existing patterns: Before generating schemas, check if other Actors in the same repository already have output schemas — match their structure, naming conventions, description style, and formatting
• Don't reinvent the wheel: Reuse existing type definitions, interfaces, and utilities from the codebase instead of creating duplicate definitions

Phase 1: Discover Actor structure

Goal: Locate the Actor and understand its output

Initial request: $ARGUMENTS

Actions:

1. Create todo list with all phases
2. Find the .actor/ directory containing actor.json
3. Read actor.json to understand the Actor's configuration
4. Check if dataset_schema.json, output_schema.json, and key_value_store_schema.json already exist
5. Search for existing schemas in the repository: Look for other .actor/ directories or schema files (e.g., **/dataset_schema.json, **/output_schema.json, **/key_value_store_schema.json) to learn the repo's conventions — match their description style, field naming, example formatting, and overall structure
6. Find all places where data is pushed to the dataset:
   • JavaScript/TypeScript: Search for Actor.pushData(, dataset.pushData(, Dataset.pushData(
   • Python: Search for Actor.push_data(, dataset.push_data(, Dataset.push_data(
7. Find all places where data is stored in the key-value store:
   • JavaScript/TypeScript: Search for Actor.setValue(, keyValueStore.setValue(, KeyValueStore.setValue(
   • Python: Search for Actor.set_value(, key_value_store.set_value(, KeyValueStore.set_value(
8. Find output type definitions — reuse them directly instead of recreating from scratch:
   • TypeScript: Look for output type interfaces/types (e.g., in src/types/, src/types/output.ts). If an interface or type already defines the output shape, derive the schema fields from it — do not create a parallel definition
   • Python: Look for TypedDict, dataclass, or Pydantic model definitions. Use the existing field names, types, and docstrings as the source of truth
9. Check for existing shared schema utilities or helper functions in the codebase that handle schema generation or validation — reuse them rather than creating new logic
10. If inline storages.dataset or storages.keyValueStore config exists in actor.json, note it for migration

Present findings to user: list all discovered dataset output fields, key-value store keys, their types, and where they come from.

Phase 2: Generate dataset_schema.json

Goal: Create a complete dataset schema with field definitions and display views

File structure

{
    "actorSpecification": 1,
    "fields": {
        "$schema": "http://json-schema.org/draft-07/schema#",
        "type": "object",
        "properties"

Consistency with existing schemas

If existing output schemas were found in the repository during Phase 1 (step 5), follow their conventions:

• Match the description writing style (sentence case vs. lowercase, period vs. no period, etc.)
• Match the field naming convention (camelCase vs. snake_case) — this must also match the actual keys produced by the Actor code
• Match the example value style (e.g., date formats, URL patterns, placeholder names)
• Match the view structure (number of fields in overview, display format choices)
• Match the JSON formatting (indentation, property ordering, spacing) — all schemas in the same repository must use identical formatting, including standalone Actors

When the Actor code already has well-defined TypeScript interfaces or Python type classes, derive fields directly from those types rather than re-analyzing pushData/push_data calls from scratch. The type definition is the canonical source.

Hard rules (no exceptions)

Warning — most common mistakes:

1. Only including fields that appear in the overview view. The fields.properties must list ALL output fields, even if they are not in the views section.
2. Only adding "required": [] and "additionalProperties": true on nested object-type properties but forgetting them on the top-level fields object. Both levels need them.

Note: nullable is an Apify-specific extension to JSON Schema draft-07. It is intentional and correct.

Field type patterns

String field:

Number field:

Boolean field:

Array field:

Nested object field:

Enum field:

Union type (e.g., TypeScript ObjectType | string):

Anonymized example values

Use realistic but generic values. Follow platform ID format conventions:

Views section

• transformation.fields: List 8–12 most important field names (order = column order in UI)
• display.properties: One entry per overview field with label and format
• Available formats: "text", "number", "date", "link", "boolean", "image", "array", "object"

Pick fields that give users the most useful at-a-glance summary of the data.

Phase 3: Generate key_value_store_schema.json (if applicable)

Goal: Define key-value store collections if the Actor stores data in the key-value store

Skip this phase if no Actor.setValue() / Actor.set_value() calls were found in Phase 1 (beyond the default INPUT key).

File structure

How to identify collections

Group the discovered setValue / set_value calls by key pattern:

1. Fixed keys (e.g., "RESULTS", "summary") — use "key" (exact match)
2. Dynamic keys with a prefix (e.g., "screenshot-${id}", f"image-{name}") — use "keyPrefix"

Each group becomes a collection.

Collection properties

Examples

Single file output (e.g., a report):

Multiple files with prefix (e.g., screenshots):

Phase 4: Generate output_schema.json

Goal: Create the output schema that tells Apify Console where to find results

For most Actors that push data to a dataset, this is a minimal file:

Critical: Each property entry must include "type": "string" — this is an Apify-specific convention. The Apify meta-validator rejects properties without it (and rejects "type": "object" — only "string" is valid here).

If key_value_store_schema.json was generated in Phase 3, add a second property:

Available template variables

• {{links.apiDefaultDatasetUrl}} — API URL of default dataset
• {{links.apiDefaultKeyValueStoreUrl}} — API URL of default key-value store
• {{links.publicRunUrl}} — Public run URL
• {{links.consoleRunUrl}} — Console run URL
• {{links.apiRunUrl}} — API run URL
• {{links.containerRunUrl}} — URL of webserver running inside the run
• {{run.defaultDatasetId}} — ID of the default dataset
• {{run.defaultKeyValueStoreId}} — ID of the default key-value store

Phase 5: Update actor.json

Goal: Wire the schema files into the Actor configuration

Actions:

1. Read the current actor.json
2. Add or update the storages.dataset reference:

   "storages": {
       "dataset": "./dataset_schema.json"
   }
3. If key_value_store_schema.json was generated, add the reference:

   "storages": {
       "dataset": "./dataset_schema.json",
       "keyValueStore": "./key_value_store_schema.json"
   }
4. Add or update the output reference:

   "output": "./output_schema.json"
5. If actor.json had inline storages.dataset or storages.keyValueStore objects (not string paths), migrate their content into the respective schema files and replace the inline objects with file path strings

Phase 6: Review and validate

Goal: Ensure correctness and completeness

Checklist:

• Every output field from the source code is in dataset_schema.json fields.properties — not just the overview view fields but ALL fields the Actor can produce
• Every field has "nullable": true
• The top-level fields object has both "additionalProperties": true and "required": []
• Every nested object within properties also has "additionalProperties": true and "required": []
• Every field has a "description" and an "example"
• All example values are anonymized
• "type" is present on every field that has "nullable"
• Views list 8–12 most useful fields with correct display formats
• output_schema.json has "type": "string" on every property
• If key-value store is used: key_value_store_schema.json has collections matching all setValue/set_value calls
• If key-value store is used: each collection uses either key or keyPrefix (not both)
• actor.json references all generated schema files
• Schema field names match the actual keys in the code (camelCase/snake_case consistency)
• If existing schemas were found in the repo, the new schema follows their conventions (description style, example format, view structure)
• Schema fields are derived from existing type definitions (interfaces, TypedDicts, dataclasses) where available — no duplicated or divergent field definitions

Present the generated schemas to the user for review before writing them.

Phase 7: Summary

Goal: Document what was created

Report:

• Files created or updated
• Number of fields in the dataset schema
• Number of collections in the key-value store schema (if generated)
• Fields selected for the overview view
• Any fields that need user clarification (ambiguous types, unclear nullability)
• Suggested next steps (test locally with apify run, verify output tab in Console)**