A lightweight markdown-based workflow for collaborating with AI coding assistants using spec-driven development. Enables operations teams to build predictable software with a repeatable AI-guided workflow. Connects to AI coding assistants like Claude and integrates with development tools.
git clone https://github.com/liatrio-labs/spec-driven-workflow.gitA lightweight markdown-based workflow for collaborating with AI coding assistants using spec-driven development. Enables operations teams to build predictable software with a repeatable AI-guided workflow. Connects to AI coding assistants like Claude and integrates with development tools.
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/liatrio-labs/spec-driven-workflowCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Create a spec-driven workflow for [COMPANY] in the [INDUSTRY] sector. The workflow should include the following steps: 1. Define the problem, 2. Identify the data sources, 3. Design the solution, 4. Implement the solution, and 5. Test and validate the solution. Use markdown format and include code snippets where appropriate. The workflow should be compatible with AI coding assistants like Claude and integrate with development tools.
# Spec-Driven Workflow for Retail Inventory Management System ## 1. Define the Problem The retail inventory management system aims to streamline the inventory tracking process for [COMPANY], a mid-sized retail chain in the [INDUSTRY] sector. The current system is manual and error-prone, leading to stockouts and overstock situations. ## 2. Identify the Data Sources - **Point of Sale (POS) Data**: Real-time sales data from all retail locations. - **Supplier Data**: Information about suppliers, lead times, and minimum order quantities. - **Inventory Data**: Current stock levels, reorder points, and safety stock levels. - **Customer Data**: Historical sales data and customer preferences. ## 3. Design the Solution The proposed solution is a web-based inventory management system with the following features: - **Real-time Inventory Tracking**: Automatically updates inventory levels based on sales data. - **Automated Reordering**: Generates purchase orders when stock levels fall below the reorder point. - **Supplier Integration**: Connects with supplier systems to automate the ordering process. - **Reporting and Analytics**: Provides insights into inventory turnover, stockouts, and overstock situations. ## 4. Implement the Solution The system will be developed using the following technologies: - **Frontend**: React.js for the user interface. - **Backend**: Node.js with Express for the server-side logic. - **Database**: PostgreSQL for storing inventory and supplier data. - **Integration**: RESTful APIs for connecting with POS and supplier systems. ## 5. Test and Validate the Solution The system will be tested using the following methods: - **Unit Testing**: Testing individual components of the system. - **Integration Testing**: Testing the interaction between different components. - **User Acceptance Testing (UAT)**: Testing the system with end-users to ensure it meets their needs. The system will be validated by comparing the inventory levels and reordering process with the manual system.
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