dkyazzentwatwa

api-response-mocker

Generate realistic mock API responses with fake data. Use for testing, prototyping, or creating sample data for frontend development.

dkyazzentwatwa 95 19 Updated 8mo ago

Resources

1
GitHub

Install

npx skillscat add dkyazzentwatwa/chatgpt-skills/api-response-mocker

Install via the SkillsCat registry.

About this skill

We need to produce a 2-3 sentence plain-text summary, objective, factual, no marketing language, no superlatives, no calls to action. Must be at most 60 words. No quotes, no bullet points, no headings, no markdown. Just plain text. Summarize: skill generates realistic mock API responses with fake data using Faker. Solves need for test data, prototyping, frontend development. Use when need sample data, testing APIs, prototyping. Let's craft maybe 2 sentences: "The API Response Mocker creates mock API responses with configurable schemas and Faker-generated fake data.

SKILL.md

API Response Mocker

Generate realistic mock API responses with fake data using Faker.

Features

  • Schema-Based Generation: Define response structure
  • Faker Integration: Realistic fake data
  • Nested Objects: Complex nested structures
  • Arrays: Generate lists of objects
  • Relationships: Reference other mock data
  • Multiple Formats: JSON, XML output

Quick Start

from api_mocker import APIMocker

mocker = APIMocker()

# Generate user response
user = mocker.generate({
    "id": "uuid",
    "name": "name",
    "email": "email",
    "created_at": "datetime"
})

# Generate list of users
users = mocker.generate_list({
    "id": "uuid",
    "name": "name",
    "email": "email"
}, count=10)

CLI Usage

# Generate from schema file
python api_mocker.py --schema user_schema.json --output user.json

# Generate list
python api_mocker.py --schema product.json --count 50 --output products.json

# Generate with seed (reproducible)
python api_mocker.py --schema order.json --seed 42 --output order.json

# Preview without saving
python api_mocker.py --schema customer.json --preview

Schema Format

Define fields using Faker provider names:

{
    "id": "uuid",
    "first_name": "first_name",
    "last_name": "last_name",
    "email": "email",
    "phone": "phone_number",
    "company": "company",
    "address": {
        "street": "street_address",
        "city": "city",
        "state": "state",
        "zip": "zipcode",
        "country": "country"
    },
    "created_at": "date_time_this_year",
    "is_active": "boolean"
}

Available Data Types

Personal

  • name, first_name, last_name
  • email, safe_email
  • phone_number
  • ssn

Address

  • address, street_address
  • city, state, state_abbr
  • zipcode, postcode
  • country, country_code
  • latitude, longitude

Internet

  • url, domain_name
  • ipv4, ipv6
  • user_name, password
  • uuid, uuid4
  • mac_address

Business

  • company, company_suffix
  • job, job_title
  • bs, catch_phrase

Financial

  • credit_card_number
  • iban, bban
  • currency_code
  • price (custom: returns float)

Date/Time

  • date, time
  • date_time, date_time_this_year
  • date_of_birth
  • iso8601

Text

  • text, sentence, paragraph
  • word, words
  • slug

Numeric

  • random_int, random_number
  • random_float (use {"type": "float", "min": 0, "max": 100})
  • boolean

Advanced Schemas

Arrays

{
    "id": "uuid",
    "name": "name",
    "tags": {
        "_array": true,
        "_count": 3,
        "_item": "word"
    },
    "orders": {
        "_array": true,
        "_count": 5,
        "_item": {
            "order_id": "uuid",
            "amount": "random_int",
            "date": "date"
        }
    }
}

Custom Values

{
    "id": "uuid",
    "status": {
        "_choice": ["pending", "active", "completed"]
    },
    "priority": {
        "_range": [1, 5]
    },
    "score": {
        "_float": {"min": 0.0, "max": 100.0, "decimals": 2}
    }
}

Nested Objects

{
    "user": {
        "id": "uuid",
        "profile": {
            "bio": "paragraph",
            "avatar_url": "image_url",
            "social": {
                "twitter": "user_name",
                "linkedin": "url"
            }
        }
    }
}

API Reference

APIMocker Class

class APIMocker:
    def __init__(self, locale: str = "en_US", seed: int = None)

    # Generation
    def generate(self, schema: dict) -> dict
    def generate_list(self, schema: dict, count: int = 10) -> list

    # File operations
    def from_schema_file(self, filepath: str) -> dict
    def save(self, data: any, filepath: str, format: str = "json")

    # Utilities
    def set_seed(self, seed: int)
    def get_faker(self) -> Faker

Example Schemas

User Response

{
    "id": "uuid",
    "username": "user_name",
    "email": "email",
    "profile": {
        "first_name": "first_name",
        "last_name": "last_name",
        "avatar": "image_url",
        "bio": "sentence"
    },
    "created_at": "iso8601",
    "last_login": "date_time_this_month"
}

E-commerce Product

{
    "sku": "uuid",
    "name": "catch_phrase",
    "description": "paragraph",
    "price": {"_float": {"min": 9.99, "max": 999.99}},
    "currency": "currency_code",
    "category": {"_choice": ["Electronics", "Clothing", "Home", "Sports"]},
    "in_stock": "boolean",
    "rating": {"_float": {"min": 1, "max": 5, "decimals": 1}},
    "reviews_count": {"_range": [0, 500]}
}

API Error Response

{
    "error": {
        "code": {"_choice": ["NOT_FOUND", "UNAUTHORIZED", "BAD_REQUEST"]},
        "message": "sentence",
        "request_id": "uuid",
        "timestamp": "iso8601"
    }
}

Dependencies

  • faker>=22.0.0