apify-generate-output-schema

Name: apify-generate-output-schema
Author: apify

6.5kinstalls247forks

How to install apify-generate-output-schema?

apify/agent-skills/apify-generate-output-schema

$npx -y skills add apify/agent-skills --skill apify-generate-output-schema

Installs into the current project.

Summary

Generate output schemas (dataset_schema.json, output_schema.json, key_value_store_schema.json) for an Apify Actor by analyzing its source code. Use when creating or updating Actor output schemas.

Files · 1

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SKILL.md

1	# Generate Actor output schema
2
3	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`.
4
5	## Core principles
6
7	- Analyze code first: Read the Actor's source to understand what data it actually pushes to the dataset — never guess
8	- Every field is nullable: APIs and websites are unpredictable — always set `"nullable": true`
9	- Anonymize examples: Never use real user IDs, usernames, or personal data in examples
10	- Verify against code: If TypeScript types exist, cross-check the schema against both the type definition AND the code that produces the values
11	- 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
12	- Don't reinvent the wheel: Reuse existing type definitions, interfaces, and utilities from the codebase instead of creating duplicate definitions
13
14	---
15
16	## Phase 1: Discover Actor structure
17
18	Goal: Locate the Actor and understand its output
19
20	Initial request: $ARGUMENTS
21
22	Actions:
23	1. Create todo list with all phases
24	2. Find the `.actor/` directory containing `actor.json`
25	3. Read `actor.json` to understand the Actor's configuration
26	4. Check if `dataset_schema.json`, `output_schema.json`, and `key_value_store_schema.json` already exist
27	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
28	6. Find all places where data is pushed to the dataset:
29	- JavaScript/TypeScript: Search for `Actor.pushData(`, `dataset.pushData(`, `Dataset.pushData(`
30	- Python: Search for `Actor.push_data(`, `dataset.push_data(`, `Dataset.push_data(`
31	7. Find all places where data is stored in the key-value store:
32	- JavaScript/TypeScript: Search for `Actor.setValue(`, `keyValueStore.setValue(`, `KeyValueStore.setValue(`
33	- Python: Search for `Actor.set_value(`, `key_value_store.set_value(`, `KeyValueStore.set_value(`
34	8. Find output type definitions — reuse them directly instead of recreating from scratch:
35	- 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
36	- Python: Look for TypedDict, dataclass, or Pydantic model definitions. Use the existing field names, types, and docstrings as the source of truth
37	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
38	10. If inline `storages.dataset` or `storages.keyValueStore` config exists in `actor.json`, note it for migration
39
40	Present findings to user: list all discovered dataset output fields, key-value store keys, their types, and where they come from.
41
42	---
43
44	## Phase 2: Generate `dataset_schema.json`
45
46	Goal: Create a complete dataset schema with field definitions and display views
47
48	### File structure
49
50	```json
51	{
52	"actorSpecification": 1,
53	"fields": {
54	"$schema": "http://json-schema.org/draft-07/schema#",
55	"type": "object",
56	"properties": {
57	// ALL output fields here — every field the Actor can produce,
58	// not just the ones shown in the overview view
59	},
60	"required": [],
61	"additionalProperties": true
62	},
63	"views": {
64	"overview": {
65	"title": "Overview",
66	"description": "Most important fields at a glance",
67	"transformation": {
68	"fields": [
69	// 8-12 most important field names
70	]
71	},
72	"display": {
73	"component": "table",
74	"properties": {
75	// Display config for each overview field
76	}
77	}
78	}
79	}
80	}
81	```
82
83	### Consistency with existing schemas
84
85	If existing output schemas were found in the repository during Phase 1 (step 5), follow their conventions:
86	- Match the description writing style (sentence case vs. lowercase, period vs. no period, etc.)
87	- Match the field naming convention (camelCase vs. snake_case) — this must also match the actual keys produced by the Actor cod

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apify-generate-output-schema

6.5kinstalls247forks

View on GitHub

How to install apify-generate-output-schema?

apify/agent-skills/apify-generate-output-schema

$npx -y skills add apify/agent-skills --skill apify-generate-output-schema

Installs into the current project.

Summary

Generate output schemas (dataset_schema.json, output_schema.json, key_value_store_schema.json) for an Apify Actor by analyzing its source code. Use when creating or updating Actor output schemas.

Files · 1

View on GitHub

SKILL.md

1	# Generate Actor output schema
2
3	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`.
4
5	## Core principles
6
7	- Analyze code first: Read the Actor's source to understand what data it actually pushes to the dataset — never guess
8	- Every field is nullable: APIs and websites are unpredictable — always set `"nullable": true`
9	- Anonymize examples: Never use real user IDs, usernames, or personal data in examples
10	- Verify against code: If TypeScript types exist, cross-check the schema against both the type definition AND the code that produces the values
11	- 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
12	- Don't reinvent the wheel: Reuse existing type definitions, interfaces, and utilities from the codebase instead of creating duplicate definitions
13
14	---
15
16	## Phase 1: Discover Actor structure
17
18	Goal: Locate the Actor and understand its output
19
20	Initial request: $ARGUMENTS
21
22	Actions:
23	1. Create todo list with all phases
24	2. Find the `.actor/` directory containing `actor.json`
25	3. Read `actor.json` to understand the Actor's configuration
26	4. Check if `dataset_schema.json`, `output_schema.json`, and `key_value_store_schema.json` already exist
27	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
28	6. Find all places where data is pushed to the dataset:
29	- JavaScript/TypeScript: Search for `Actor.pushData(`, `dataset.pushData(`, `Dataset.pushData(`
30	- Python: Search for `Actor.push_data(`, `dataset.push_data(`, `Dataset.push_data(`
31	7. Find all places where data is stored in the key-value store:
32	- JavaScript/TypeScript: Search for `Actor.setValue(`, `keyValueStore.setValue(`, `KeyValueStore.setValue(`
33	- Python: Search for `Actor.set_value(`, `key_value_store.set_value(`, `KeyValueStore.set_value(`
34	8. Find output type definitions — reuse them directly instead of recreating from scratch:
35	- 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
36	- Python: Look for TypedDict, dataclass, or Pydantic model definitions. Use the existing field names, types, and docstrings as the source of truth
37	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
38	10. If inline `storages.dataset` or `storages.keyValueStore` config exists in `actor.json`, note it for migration
39
40	Present findings to user: list all discovered dataset output fields, key-value store keys, their types, and where they come from.
41
42	---
43
44	## Phase 2: Generate `dataset_schema.json`
45
46	Goal: Create a complete dataset schema with field definitions and display views
47
48	### File structure
49
50	```json
51	{
52	"actorSpecification": 1,
53	"fields": {
54	"$schema": "http://json-schema.org/draft-07/schema#",
55	"type": "object",
56	"properties": {
57	// ALL output fields here — every field the Actor can produce,
58	// not just the ones shown in the overview view
59	},
60	"required": [],
61	"additionalProperties": true
62	},
63	"views": {
64	"overview": {
65	"title": "Overview",
66	"description": "Most important fields at a glance",
67	"transformation": {
68	"fields": [
69	// 8-12 most important field names
70	]
71	},
72	"display": {
73	"component": "table",
74	"properties": {
75	// Display config for each overview field
76	}
77	}
78	}
79	}
80	}
81	```
82
83	### Consistency with existing schemas
84
85	If existing output schemas were found in the repository during Phase 1 (step 5), follow their conventions:
86	- Match the description writing style (sentence case vs. lowercase, period vs. no period, etc.)
87	- Match the field naming convention (camelCase vs. snake_case) — this must also match the actual keys produced by the Actor cod

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Type