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    Coding Agent Prompt Architect

    1

    Converts vague developer requests into precise, scoped, and testable prompts for AI coding agents.

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    Coding Agent Prompt Architect

    Coding Agent Prompt Architect

    Example session with this skill installed

    I want to add a dark mode toggle to my React app. It should save the user preference in local storage so it persists. I'm using Tailwind CSS. Make it look good.

    • Read your context and instructions
    • Compiled the coding agent prompt

    I have refined your request into a scoped, technical prompt optimized for a coding agent. I removed the vague styling requirement and replaced it with specific implementation patterns and persistence logic.

    Refined Prompt

    Implement a dark mode toggle feature using React, Tailwind CSS, and localStorage for persistence.

    Task Details:
    1.

    Inspection: Before implementation, inspect the repository to identify the main layout component, existing Tailwind configuration, and any global state management (e.g., Context API).
    2. Implementation:

    • Create a ThemeContext or use an existing global state to manage the 'dark' vs 'light' state.
    • Apply the .dark class to the <html> or <body> element based on state.
    • Implement a toggle component (Icon or Switch) that triggers the state change.
    • Persist the selected theme in localStorage under the key app-theme.
    • On initial load, check localStorage and the prefers-color-scheme media query to determine the default theme.
    1. Constraints:
      • Use Tailwind's dark: variant for all dark mode styling.
      • Do not modify existing component logic unrelated to theme styling.
      • Ensure no flickering on initial page load (hydration).

    Acceptance Criteria:

    • Toggle successfully switches classes and updates state.
    • Theme persists after a full page refresh.
    • System preference is respected if no local preference is set.
    • No console errors related to localStorage access.

    Suggested Tests:

    • Verify .dark class presence in DOM on toggle.
    • Verify localStorage value updates on change.
    • Mock prefers-color-scheme to verify default behavior.

    Key Improvements:

    • Defined the persistence key and logic.
    • Added system preference detection.
    • Constrained the agent to Tailwind's native dark mode patterns.

    Assumptions:

    • You are using a standard React setup where modifying the root element class is acceptable for Tailwind dark mode.

    Next steps

    • Copy the Refined Prompt into Cursor or Claude Code.
    • Provide the agent access to your tailwind.config.js and App.tsx.
    • Review the generated Toggle component for UI consistency.

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

    About this skill

    The problem

    Vague coding requests lead to hallucinations, unbounded scope creep, and broken builds when using agents like Cursor or Claude Code. Developers often spend more time fixing an agent's mistakes than they would have spent writing the code themselves.

    What it does

    • Transforms loose feature requests into precise, scoped, and testable instructions for coding agents.
    • Detects high-impact weaknesses like undefined success criteria, missing edge cases, and hidden destructive actions.
    • Automatically structures prompts into In-Scope and Out-of-Scope sections to prevent unnecessary refactoring.
    • Generates Given/When/Then acceptance criteria and specific test cases for the agent to verify.
    • Adapts output structure specifically for the target tool, whether it is Cursor, Claude Code, Replit, or Codex CLI.

    Why this beats prompting it yourself

    Writing a prompt that prevents a coding agent from wandering off-track requires a level of discipline that is hard to maintain mid-sprint. This skill enforces a senior-level prompt engineering workflow that separates facts from assumptions and mandates negative constraints, ensuring the agent delivers exactly what you need without breaking existing patterns.

    Use cases

    • Converting a messy bug report into a step-by-step reproduction and fix instruction.
    • Drafting complex API requirement prompts that include server-side auth and validation logic.
    • Creating refactoring tasks that strictly protect public interfaces and data compatibility.
    • Generating comprehensive test-suite requirements for legacy modules.

    Known limitations

    Does not execute code or access your local filesystem directly. It requires the user to provide the initial context or existing prompt for refinement.

    Does not execute code or access your local filesystem directly. Requires the user to provide initial context or existing prompts for refinement.

    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 any payment.

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    What's inside

    Frequently Asked Questions

    This skill acts as a pre-processing layer for your development workflow, converting high-level ideas into structured instructions that prevent coding agents from hallucinating, refactoring unnecessary files, or missing edge cases.

    The skill generates optimized prompts compatible with all major AI coding tools, including Cursor, Claude Code, Replit, and GitHub Copilot, by adapting the structure to fit each tool's specific strengths.

    No complex installation is required; once added to your workspace, you simply provide your rough task description or bug report, and the skill will return a structured prompt ready to be pasted into your coding agent.

    Each purchase includes the core prompt transformation engine, specific templates for different agent tools, and access to any future logic updates to accommodate new coding models.

    While it cannot see your local files, you can paste in relevant snippets or file paths, and the Architect will incorporate them into a 'Context' section to ensure the resulting prompt is highly specific to your codebase.

    New in Prompt & Skill Engineering