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AI Coding Prompt Refinement Pro
by ssingh
Transform vague coding requests into production-ready AI coding prompts with context, constraints, architecture, tests, acceptance criteria, and verification gates.
$19.99
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See it in action
You say
Add a dark mode to my React app. Make it look professional and don't break anything. Use the existing styling system if possible. Make sure it works on mobile too.
Your agent does
PRODUCTION CODING PROMPT Task
Implement a production-ready dark mode system for the existing React application while preserving current functionality, design-system conventions, and responsive behavior.
- Repository Inspection
Before modifying code:
Identify the React framework and version. Locate the existing styling system. Identify global theme variables/tokens. Identify layout and component styling conventions. Determine whether a theme provider, context, CSS variables, Tailwind configuration, or equivalent system already exists. Inspect existing responsive breakpoints. Identify components that use hard-coded colors and may not automatically adapt.
Do not introduce a new styling framework if an existing system can support the requirement.
- Scope
In scope:
Light/dark theme support Theme state management Theme persistence Theme-aware global colors Existing components adapting to the theme Mobile compatibility Theme toggle UI Accessible theme controls
Out of scope:
Rebuilding the component library Replacing the existing CSS architecture Unrelated visual redesign Changing business logic Rewriting components unnecessarily 3. Implementation Requirements Reuse the existing styling architecture whenever practical. Prefer centralized theme tokens over scattered hard-coded colors. Avoid duplicating component styles unnecessarily. Preserve existing light-mode appearance as closely as possible. Ensure dark mode provides sufficient contrast. Persist the user's theme preference using the project's existing storage conventions. Avoid hydration or flash-of-incorrect-theme issues where applicable. Respect existing responsive behavior. Keep the implementation modular and maintainable. 4. Edge Cases
Handle:
First-time users Returning users with saved preferences System-theme preference if supported Missing theme preference Components containing hard-coded background colors Modal/dropdown states Form controls Disabled states Loading states Error states 5. Accessibility
Verify:
Text contrast Interactive element contrast Focus states Keyboard accessibility Theme toggle labeling Screen-reader accessibility
Do not rely exclusively on color to communicate state.
- Acceptance Criteria
The implementation is complete only when:
Light mode continues to work. Dark mode can be enabled and disabled. Theme preference persists correctly. Existing major components adapt correctly. Mobile layouts remain functional. No unrelated files or functionality are changed. Existing tests continue to pass. Appropriate new tests are added where practical. No obvious contrast/accessibility regressions are introduced. 7. Verification
After implementation:
Run the existing test suite. Run the project's lint/type-check commands when available. Verify both themes across primary screens. Check responsive layouts. Check interactive states. Report any unresolved issues instead of claiming success. 8. Final Response
Return:
Implemented
Files changed Main architectural decisions Tests executed Verification results
Potential Issues
Remaining limitations Any assumptions made Any manual verification still required
Do not claim the task is complete if verification failed.
What you get
About this skill
AI Coding Prompt Refinement Pro is a production-focused prompt engineering skill designed for developers and AI coding agents.
It transforms incomplete, ambiguous, or poorly structured coding requests into precise execution prompts that help AI coding agents understand what to build, what not to change, how to implement it, how to verify it, and what constitutes a successful result.
Instead of simply rewriting a prompt, the skill performs a structured refinement process covering:
Requirement extraction Ambiguity detection Missing-context identification Scope definition In-scope / out-of-scope boundaries Repository-aware instructions Architecture and implementation constraints Existing-code preservation Dependency awareness Security considerations Edge-case planning Error-handling requirements Testing strategy Acceptance criteria Verification commands Regression protection Rollback considerations AI-agent failure prevention
The final result is a paste-ready production coding prompt optimized for AI coding environments such as Claude Code, Cursor, Codex CLI, Gemini CLI, GitHub Copilot, VS Code Copilot, and other compatible agents.
Core Promise
Don't give an AI coding agent a vague request. Give it an execution contract.
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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