Textum is a structured workflow that ensures AI adheres to your requirements. It features 4 phases with validation gates, making the process more controllable. Ideal for operations teams, it weaves ideas into functional code, reducing errors and improving reliability.
git clone https://github.com/snakeying/Textum.gitTextum is a structured workflow that ensures AI adheres to your requirements. It features 4 phases with validation gates, making the process more controllable. Ideal for operations teams, it weaves ideas into functional code, reducing errors and improving reliability.
["Define the project scope and requirements clearly, including constraints, inputs, and expected outputs.","Use the prompt template to generate a structured plan with milestones and validation checkpoints.","Iteratively develop the output while adhering to the plan, ensuring each step aligns with the requirements.","Validate the final output against the initial requirements and refine as needed before deployment.","For best results, use tools like GitHub Projects or Jira to track milestones and validation checkpoints."]
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/snakeying/TextumCopy 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.
Follow the Textum structured workflow to generate [OUTPUT_TYPE] for [PROJECT_NAME]. Phase 1: Define requirements by listing all constraints, inputs, and expected outputs. Phase 2: Generate a structured plan with clear milestones and validation checkpoints. Phase 3: Develop the output iteratively, ensuring each step aligns with the plan. Phase 4: Validate the final output against the requirements and refine as needed. Here are the details: [PROJECT_DETAILS].
For a project named 'E-commerce Chatbot Integration,' the Textum workflow produced the following structured output: **Phase 1: Define Requirements** - Constraints: Must integrate with Shopify API, handle 100+ concurrent users, and comply with GDPR. - Inputs: Product catalog, customer data, and order history. - Expected Output: A Python-based chatbot with REST API endpoints for order tracking and customer support. **Phase 2: Structured Plan** - Milestone 1: API documentation review (Day 1) - Milestone 2: Core chatbot logic development (Days 2-3) - Milestone 3: Integration testing with Shopify sandbox (Day 4) - Milestone 4: Load testing and GDPR compliance audit (Day 5) - Validation Checkpoints: Code review after each milestone, final QA before deployment. **Phase 3: Development Iteration** - Day 1: Reviewed Shopify API docs and set up development environment. - Day 2: Implemented basic chatbot logic with intent recognition using NLTK. - Day 3: Added order tracking functionality via REST API calls. - Day 4: Integrated with Shopify sandbox and tested basic order queries. - Day 5: Conducted load testing with 200 simulated users and audited GDPR compliance. **Phase 4: Validation** - Final output validated against requirements: API integration successful, chatbot handles 150+ concurrent users, and GDPR compliance confirmed. - Minor refinements made to error handling for order queries. - Delivered a production-ready chatbot with 99.8% uptime guarantee. The structured approach reduced development time by 30% and eliminated post-deployment bugs by 40%.
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