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    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.

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    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

    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.

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

    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.

    Find more skills like this on the Claude Skills Marketplace, the Codex Skills Marketplace and the Gemini Skills Marketplace, or browse Claude Prompt Generator and Optimizer Skills.

    The skill improves coding instructions and execution specifications but does not guarantee that an AI coding agent will produce correct or production-safe code. Repository context, framework versions, undocumented business rules, external services, and hidden requirements may not be available to the skill. Generated prompts should be validated against the actual codebase and tested after implementation. The skill does not independently verify production behavior unless the compatible agent provides the necessary tools and evidence.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before buying a skill.

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    Permissions

    Read Files

    Read access allows the skill to inspect relevant project files, repository structure, configuration files, documentation, and existing implementation patterns when the user provides them. Write access, shell execution, browser access, network access, and environment variables are not required because the skill's primary output is a refined production coding prompt rather than direct code modification.

    Listed3 months ago

    What's inside

    Frequently Asked Questions

    It transforms vague, incomplete, or poorly structured coding requests into precise production-oriented prompts that an AI coding agent can execute more reliably.

    No. The workflow goes beyond rewriting. It identifies ambiguity, defines scope, adds constraints, considers repository context, specifies edge cases, creates acceptance criteria, and adds testing and verification requirements.

    Yes. When relevant project files are available, the skill can use repository structure, configuration, documentation, and existing implementation patterns to make the refined prompt more context-aware.

    Its primary purpose is to produce a production-ready coding prompt. The compatible coding agent can then use that prompt to implement the requested change.

    It can explicitly define scope, out-of-scope areas, preservation requirements, and verification criteria to reduce unnecessary changes. It cannot guarantee that every downstream coding agent will perfectly follow those instructions.

    Yes. It can convert vague bug reports into structured debugging prompts containing reproduction requirements, investigation steps, constraints, expected behavior, regression tests, and acceptance criteria.

    Yes. It can add boundaries, preservation requirements, incremental implementation steps, regression protection, and verification requirements to reduce risky broad rewrites.

    The refined prompts are designed to be useful with compatible coding agents such as Claude Code, Cursor, Codex CLI, Gemini CLI, GitHub Copilot, and similar environments.

    No. It improves the specification and execution instructions. Actual code quality depends on the codebase, available context, the downstream AI agent, implementation decisions, and verification.

    New in Testing & Debugging