Claude-skill-registry api-response-mocker

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

install

source · Clone the upstream repo

git clone https://github.com/majiayu000/claude-skill-registry

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/api-response-mocker" ~/.claude/skills/majiayu000-claude-skill-registry-api-response-mocker && rm -rf "$T"

manifest: skills/data/api-response-mocker/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