A synthetic test data generator that creates relational JSON, CSV, or SQL database mock data and seeding fixtures for developers and AI agents.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install mockhero
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install mockhero using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is MockHero?
MockHero is a synthetic test data generation engine designed specifically for modern developer workflows and autonomous agents. By integrating with environments through MCP (Model Context Protocol) or REST APIs, it allows users to effortlessly generate high-fidelity, relational data fixtures. Unlike standard faker scripts or simple random generation, MockHero resolves complex relationships between tables. You can produce nested, multi-table databases with accurate foreign keys (e.g., users referencing orders, and orders referencing items) spanning 156 field types across 22 locales in JSON, CSV, or SQL INSERT formats.
As part of the Openclaw Skills ecosystem, MockHero helps AI coding assistants and developers instantly seed databases or build QA datasets without leaving their coding environments. It supports a frictionless free-tier preview, automatic schema detection from raw SQL or JSON structures, and an on-demand, loginless Polar payment flow for metered usage at scale.
MockHero Use Cases
- Seeding development databases (PostgreSQL, MySQL, SQLite) with complex relational data containing functional foreign keys.
- Generating bulk locale-aware mock records for localized QA testing, such as German phone numbers, IBANs, or specialized medical codes.
- Producing large-volume datasets for load-testing applications where token-heavy LLM generations would be cost-prohibitive.
- Equipping AI agents utilizing Openclaw Skills with the ability to dynamically design schemas, convert existing tables, and retrieve mock fixtures on demand.
How MockHero Works
- Schema Discovery & Detection: The user or agent inputs raw SQL queries, sample JSON files, or uses the detect_schema tool to construct a relational mapping structure.
- Free Preview Execution: Small-scale generations (up to 100 records per request or 500 records daily) are run instantly using the free preview endpoints without any keys or signups.
- Usage Estimation & Pricing: For larger datasets or prompt-driven requests, the agent uses estimate_agent_usage to analyze complexity and provide unit costs.
- Seamless Authentication: If required, a frictionless Polar checkout is triggered to securely process metered billing, returning a secure API key once.
- High-Fidelity Generation: MockHero compiles the fields, resolves foreign key dependencies, and outputs relational datasets in requested formats (JSON, CSV, or SQL inserts) ready to be written to disk.
MockHero Setup
To connect this tool within your Openclaw Skills workflow, you can choose between a remote streamable endpoint or a local server setup.
Remote MCP (Preferred, No Install)
Configure your client to communicate directly via HTTP POST with the following streamable endpoint:
https://mockhero.dev/mcp/agent
Local stdio MCP Server
Install the package locally using Node Package Manager:
npx @mockherodev/mcp-server
Environment Variables
For authenticated or metered access, set the following env variables in your configuration file:
export MOCKHERO_API_KEY="mh_your_api_key_here"
export MOCKHERO_API_BASE="https://mockhero.dev/api"
MockHero Data Schema & Taxonomy
MockHero manages synthetic templates and schemas through a clean data structure. Below is an overview of the tools available through this integration:
| Tool Name | Key Parameters | Return Data / Purpose |
|---|---|---|
detect_schema |
sql or sample_json |
Conversions into a mockable table schema |
estimate_agent_usage |
tables, template, prompt |
Calculated records & estimated USD cost |
generate_test_data |
tables or prompt, format, locale |
Relational mock datasets in CSV, JSON, or SQL |
generate_from_template |
template, scale, locale, format |
Multi-table generation from boilerplate presets |
create_agent_checkout |
email |
Checkout URL and unique claim token |
Relational Reference Schema Example
Relationships between tables are defined using the ref field type as shown below:
{
"name": "orders",
"count": 50,
"fields": [
{ "name": "id", "type": "uuid" },
{ "name": "user_id", "type": "ref", "params": { "table": "users", "field": "id" } }
]
}
MockHero Advanced Features
- Relational Mapping Engine: Employs
reffields to intelligently map foreign key structures across multiple simulated tables automatically. - Natural Language Prompt-to-Data: Generates complex datasets matching customized constraints based on arbitrary plain-English prompts (requires an active API key).
- Locale-Aware Localization: Features 22 distinct geographical locales to customize generated names, phone number structures, physical addresses, and financial codes (e.g., IBANs).
- Flexible Database Dialects: Emits fully compliant SQL standard inserts formatted dynamically for PostgreSQL, MySQL, or SQLite engines.
- Zero-Auth Polar Checkout: Enables autonomous agents to securely request and confirm checkout payments on behalf of the developer, bridging agent actions with standard financial layers across other Openclaw Skills.
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