A specialized tool that transforms natural language requirements, API documentation, and UI prototypes into structured, machine-executable JSON test suites.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install generate-model-ready-test-cases-cn
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 generate-model-ready-test-cases-cn 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 Model-Ready Automated Test Case Generator?
The Model-Ready Automated Test Case Generator is designed to bridge the gap between human-centric documentation and machine-executable testing. By utilizing Openclaw Skills, this tool avoids generating vague, text-heavy test plans and instead produces high-precision JSON objects that AI models and automation agents can consume directly. It provides a standardized framework for defining steps, locators, request parameters, and assertions, ensuring that every generated case is ready for immediate deployment.
This skill is particularly valuable for teams practicing agentic automation, where the output serves as the source of truth for downstream execution models. By integrating Openclaw Skills into your QA workflow, you ensure that test designs remain stable, portable, and mathematically consistent across different testing environments and platforms.
Model-Ready Automated Test Case Generator Use Cases
- Converting Product Requirement Documents (PRDs) into executable Web UI automation scripts.
- Generating comprehensive API test suites from Swagger or OpenAPI definitions.
- Creating end-to-end (E2E) regression tests from user stories and Figma prototypes.
- Translating bug reports into structured reproduction cases for automated verification.
- Building smoke test suites for new feature acceptance in CI/CD pipelines.
How Model-Ready Automated Test Case Generator Works
- The skill ingests raw input materials such as requirements, API docs, or interface descriptions to extract the minimal fact set.
- It cross-references the internal test-suite-schema and test-design-playbook to determine the optimal coverage strategy.
- Executable actions are mapped to specific model-ready verbs like navigate, click, fill, or request.
- Stable locators (role, label, testid) are prioritized over brittle CSS or XPath selectors to ensure long-term test stability.
- A final JSON suite is generated, including assumptions, environment placeholders, and structured assertions.
- The output is validated against the schema using integrated validation scripts to ensure it is error-free.
Model-Ready Automated Test Case Generator Setup
To get started with this skill, ensure you have the necessary reference files in your project directory. You can validate your environment by running the included validation script:
# Validate a generated test suite JSON file
python scripts/validate_test_suite_json.py <path-to-your-suite.json>
Configure your environment variables and secrets using the {{env.VAR_NAME}} or {{secret.KEY_NAME}} syntax within your input prompts to ensure Openclaw Skills handles sensitive data securely.
Model-Ready Automated Test Case Generator Data Schema & Taxonomy
The skill produces a structured JSON output based on a rigorous schema. Key fields include:
| Field | Description |
|---|---|
suite_id |
A unique, stable identifier for the entire test suite. |
assumptions |
A list of logical inferences made when requirements were incomplete. |
cases |
An array containing individual test cases with IDs, priorities, and tags. |
steps |
The sequence of actions, including action, target, input, and expected. |
placeholders |
Dynamic variables used for environment-specific data. |
Model-Ready Automated Test Case Generator Advanced Features
- Smart Risk Management: Automatically marks high-risk or destructive operations as disabled by default.
- Semantic Locator Prioritization: Prefers accessible roles and labels over structural selectors for resilient UI testing.
- Placeholder Injection: Native support for secrets and environment variables to prevent hardcoding sensitive data.
- Multi-Step E2E Support: Seamlessly combines UI and API steps within a single unified test case.
- Validation Integration: Built-in hooks for Python-based schema verification before delivery.
SKILL.md
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