More screenshots

    Works with the AI tools you already use

    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIMuseMuseOpenClawOpenClaw+21 more

    AI Coding Prompt Refinement Pro

    2

    Transform vague coding requests into production-ready AI coding prompts with context, constraints, architecture, tests, acceptance criteria, and verification gates.

    $14.99

    Secure checkout via Stripe

    30-day refund guarantee

    Converts to your local currency at checkout

    Security scanned
    AI Coding Prompt Refinement Pro

    AI Coding Prompt Refinement Pro

    Example session with this skill installed

    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.

    • Read your context and instructions
    • Compiled the ai coding prompt
    • Generated the document

    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.

    1. 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.

    1. 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.

    1. 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.

    ai-coding-prompt-refinement-pro.pdf

    PDF · document

    Generated

    Example file from a real run - the skill writes it into your workspace.

    Connects securely to your tools. The creator never sees your data.

    What you get

    Turn vague feature requests into precise coding-agent promptsConvert Jira tickets into implementation-ready AI coding instructionsRefine prompts before sending them to Cursor, Claude Code, Codex, or other coding agentsDetect missing requirements before implementation beginsAdd explicit scope and out-of-scope boundaries to coding tasksGenerate acceptance criteria for AI-generated implementationsCreate test and verification requirementsPrevent AI agents from making unnecessary architectural changesProtect existing functionality during feature implementationConvert bug descriptions into reproducible debugging promptsTurn refactoring requests into controlled, incremental execution plansGenerate repository-aware coding prompts from project contextAdd edge cases and failure handling to incomplete development requestsImprove AI coding prompts for production environmentsCreate prompts for complex multi-file changesReduce AI-generated overengineering and unrelated code changes

    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

    Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.

    ~30 seconds
    1. 1

      Download the ZIP

      Free skills download straight away. Paid skills unlock right after purchase.

    2. 2

      Unzip into your skills folder

      Every agent reads skills from one folder on your machine. Drop the unzipped folder in there.

    3. 3

      Ask your agent to use it

      Restart the agent if it was already running. It picks the skill up automatically - no config needed.

    Skills folder by agent

    Click the path to copy it. Create the folder if it does not exist yet.

    Reviews

    No reviews yet

    Be one of the first to try it. Every listed skill passes our trust checks below.

    Security scanned

    Passed our 8-point scan before listing

    Fresh listing

    Recently published to Agensi

    30-day refund

    Not a fit? Get your money back

    Trust & safety

    Security scanned

    Verified clean 1 month ago

    • Passed all security checks, Safe to install

    Listed1 month ago
    Updated1 month ago

    What's inside

    Frequently Asked Questions