userstory to code
by nowrich
Translates user stories and acceptance criteria into architecture-consistent, production-ready code.
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Works with the AI tools you already use
See it in action
You say
Implement this story: As a user, I want to filter my order history by date range so I can find specific past purchases. Criteria: 1. Add date pickers. 2. Filter API call. 3. Show empty state.
Your agent does
I have analyzed your /orders module. I will reuse the DateRangePicker component and the useApi hook.
Plan:
- Update
OrderList.tsxto include filters. - Modify
orderService.tsto acceptstartDateandendDate. - Add a test case in
OrderList.test.tsx.
Proceed?
What you get
About this skill
The problem
LLMs often generate "hallucinated" code that ignores your project's specific architecture, naming conventions, and existing utilities. This results in generic snippets that require heavy manual refactoring to actually fit your codebase.
What it does
- Analyzes your existing repository to extract real naming styles, export patterns, and directory structures.
- Identifies existing abstractions and shared utilities to prevent duplicate helper functions.
- Maps every individual acceptance criterion from a ticket to a specific code path before implementation.
- Enforces a "plan-first" checkpoint, requiring approval of the architectural approach before writing a single line of code.
- Generates production-ready implementation and matching tests that mirror your team's exact coding style.
Frameworks & tools
Compatible with any stack. It uses grep, glob, and filesystem exploration to adapt to your specific language and framework conventions.
Why this beats prompting it yourself
Generic prompts result in generic code. This skill forces a two-stage verification process that ensures the output isn't just functional, but architecturaly consistent. It eliminates the "copy-paste-refactor" cycle by reading your code before it writes its own.
Use cases
- Translating Jira or Linear tickets into feature stubs and logic.
- Implementing new API endpoints that match existing controller patterns.
- Building UI components that reuse your project's specific design tokens and hooks.
- Converting Gherkin acceptance criteria into functional code and tests.
Known limitations
Requires an existing codebase or defined conventions to match style. It is not designed for bug fixes or pure refactoring tasks without new acceptance criteria.
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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