apify-generate-output-schema

Reads an Actor's code to work out what data it really produces, then writes the description of that output that makes Apify's console show a run's results as a readable table instead of raw data.

WHEN TO USE

SCOPE

USE FOR

Generate a dataset schema that lists every field an Actor's code actually pushes.
Build the Console overview table that shows a run's most useful fields at a glance.
Document key-value store collections for an Actor that saves files or screenshots.
Match a new Actor's schema style to conventions already used elsewhere in the repo.

This is a saved copy of the skill's SKILL.md instructions. It may differ from the upstream file or the version you install.

In this document (9)

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:

Create todo list with all phases
Find the .actor/ directory containing actor.json
Read actor.json to understand the Actor's configuration
Check if dataset_schema.json, output_schema.json, and key_value_store_schema.json already exist
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
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(
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(
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
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
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": {
            // ALL output fields here — every field the Actor can produce,
            // not just the ones shown in the overview view
        },
        "required": [],
        "additionalProperties": true
    },
    "views": {
        "overview": {
            "title": "Overview",
            "description": "Most important fields at a glance",
            "transformation": {
                "fields": [
                    // 8-12 most important field names
                ]
            },
            "display": {
                "component": "table",
                "properties": {
                    // Display config for each overview field
                }
            }
        }
    }
}

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)

Rule	Detail
All fields in `properties`	The `fields.properties` object must contain every field the Actor can output, not just the fields shown in the overview view. The views section selects a subset for display — the `properties` section must be the complete superset
`"nullable": true`	On every field — APIs are unpredictable
`"additionalProperties": true`	On the top-level `fields` object AND on every nested object within `properties`. This is the most commonly missed rule — it must appear at both levels
`"required": []`	Always empty array — on the top-level `fields` object AND on every nested object within `properties`
Anonymized examples	No real user IDs, usernames, or content
`"type"` required with `"nullable"`	AJV rejects `nullable` without a `type` on the same field

Warning — most common mistakes:

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.

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:

"title": {
    "type": "string",
    "description": "Title of the scraped item",
    "nullable": true,
    "example": "Example Item Title"
}

Number field:

"viewCount": {
    "type": "number",
    "description": "Number of views",
    "nullable": true,
    "example": 15000
}

Boolean field:

"isVerified": {
    "type": "boolean",
    "description": "Whether the account is verified",
    "nullable": true,
    "example": true
}

Array field:

"hashtags": {
    "type": "array",
    "description": "Hashtags associated with the item",
    "items": { "type": "string" },
    "nullable": true,
    "example": ["#example", "#demo"]
}

Nested object field:

"authorInfo": {
    "type": "object",
    "description": "Information about the author",
    "properties": {
        "name": { "type": "string", "nullable": true },
        "url": { "type": "string", "nullable": true }
    },
    "required": [],
    "additionalProperties": true,
    "nullable": true,
    "example": { "name": "Example Author", "url": "https://example.com/author" }
}

Enum field:

"contentType": {
    "type": "string",
    "description": "Type of content",
    "enum": ["article", "video", "image"],
    "nullable": true,
    "example": "article"
}

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

"metadata": {
    "type": ["object", "string"],
    "description": "Structured metadata object, or error string if unavailable",
    "nullable": true,
    "example": { "key": "value" }
}

Anonymized example values

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

Field type	Example approach
IDs	Match platform format and length (e.g., 11 chars for YouTube video IDs)
Usernames	`"exampleuser"`, `"sampleuser123"`
Display names	`"Example Channel"`, `"Sample Author"`
URLs	Use platform's standard URL format with fake IDs
Dates	`"2025-01-15T12:00:00.000Z"` (ISO 8601)
Text content	Generic descriptive text, e.g., `"This is an example description."`

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

{
    "actorKeyValueStoreSchemaVersion": 1,
    "title": "<Descriptive title — what the key-value store contains>",
    "description": "<One sentence describing the stored data>",
    "collections": {
        "<collectionName>": {
            "title": "<Human-readable title>",
            "description": "<What this collection contains>",
            "keyPrefix": "<prefix->"
        }
    }
}

How to identify collections

Group the discovered setValue / set_value calls by key pattern:

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

Each group becomes a collection.

Collection properties

Property	Required	Description
`title`	Yes	Shown in UI tabs
`description`	No	Shown in UI tooltips
`key`	Conditional	Exact key for single-key collections (use `key` OR `keyPrefix`, not both)
`keyPrefix`	Conditional	Prefix for multi-key collections (use `key` OR `keyPrefix`, not both)
`contentTypes`	No	Restrict allowed MIME types (e.g., `["image/jpeg"]`, `["application/json"]`)
`jsonSchema`	No	JSON Schema draft-07 for validating `application/json` content

Examples

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

{
    "actorKeyValueStoreSchemaVersion": 1,
    "title": "Analysis Results",
    "description": "Key-value store containing analysis output",
    "collections": {
        "report": {
            "title": "Report",
            "description": "Final analysis report",
            "key": "REPORT",
            "contentTypes": ["application/json"]
        }
    }
}

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

{
    "actorKeyValueStoreSchemaVersion": 1,
    "title": "Scraped Files",
    "description": "Key-value store containing downloaded files and screenshots",
    "collections": {
        "screenshots": {
            "title": "Screenshots",
            "description": "Page screenshots captured during scraping",
            "keyPrefix": "screenshot-",
            "contentTypes": ["image/png", "image/jpeg"]
        },
        "documents": {
            "title": "Documents",
            "description": "Downloaded document files",
            "keyPrefix": "doc-",
            "contentTypes": ["application/pdf", "text/html"]
        }
    }
}

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:

{
    "actorOutputSchemaVersion": 1,
    "title": "<Descriptive title — what the Actor returns>",
    "description": "<One sentence describing the output data>",
    "properties": {
        "dataset": {
            "type": "string",
            "title": "Results",
            "description": "Dataset containing all scraped data",
            "template": "{{links.apiDefaultDatasetUrl}}/items"
        }
    }
}

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:

"files": {
    "type": "string",
    "title": "Files",
    "description": "Key-value store containing downloaded files",
    "template": "{{links.apiDefaultKeyValueStoreUrl}}/keys"
}

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:

Read the current actor.json

Add or update the storages.dataset reference:

"storages": {
    "dataset": "./dataset_schema.json"
}

If key_value_store_schema.json was generated, add the reference:

"storages": {
    "dataset": "./dataset_schema.json",
    "keyValueStore": "./key_value_store_schema.json"
}

Add or update the output reference:
```
"output": "./output_schema.json"
```
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:

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)

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What's inside

Files included with this skill.

View on GitHub

Generate Actor output schema

Core principles

Phase 1: Discover Actor structure

Phase 2: Generate dataset_schema.json

File structure

Consistency with existing schemas

Hard rules (no exceptions)

Field type patterns

Anonymized example values

Views section

Phase 3: Generate key_value_store_schema.json (if applicable)

File structure

How to identify collections

Collection properties

Examples

Phase 4: Generate output_schema.json

Available template variables

Phase 5: Update actor.json

Phase 6: Review and validate

Phase 7: Summary

No requirements beyond installing this skill were found.

What's inside

More skills

MCP keyword matches

Phase 2: Generate `dataset_schema.json`

Phase 3: Generate `key_value_store_schema.json` (if applicable)

Phase 4: Generate `output_schema.json`

Phase 5: Update `actor.json`