api-response-mocker
Generate realistic mock API responses with fake data. Use for testing, prototyping, or creating sample data for frontend development.
Best use case
api-response-mocker is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Generate realistic mock API responses with fake data. Use for testing, prototyping, or creating sample data for frontend development.
Teams using api-response-mocker should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/api-response-mocker/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How api-response-mocker Compares
| Feature / Agent | api-response-mocker | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Generate realistic mock API responses with fake data. Use for testing, prototyping, or creating sample data for frontend development.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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
```python
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
```bash
# 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:
```json
{
"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
```json
{
"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
```json
{
"id": "uuid",
"status": {
"_choice": ["pending", "active", "completed"]
},
"priority": {
"_range": [1, 5]
},
"score": {
"_float": {"min": 0.0, "max": 100.0, "decimals": 2}
}
}
```
### Nested Objects
```json
{
"user": {
"id": "uuid",
"profile": {
"bio": "paragraph",
"avatar_url": "image_url",
"social": {
"twitter": "user_name",
"linkedin": "url"
}
}
}
}
```
## API Reference
### APIMocker Class
```python
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
```json
{
"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
```json
{
"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
```json
{
"error": {
"code": {"_choice": ["NOT_FOUND", "UNAUTHORIZED", "BAD_REQUEST"]},
"message": "sentence",
"request_id": "uuid",
"timestamp": "iso8601"
}
}
```
## Dependencies
- faker>=22.0.0Related Skills
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