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    PromptDecomposer: Monolith Request Splitter

    1

    Splits monolithic build requests into isolated, single-file prompts to prevent context loss in multi-file projects.

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    PromptDecomposer: Monolith Request Splitter

    PromptDecomposer: Monolith Request Splitter

    Example session with this skill installed

    I need a Python Telegram bot that handles user orders, sends notifications, and has a simple admin panel for managing the queue. Build the whole thing for me now.

    • Read your context and instructions
    • Compiled the promptdecomposer

    📡 PROMPT-DECOMPOSER ENGAGED: MONOLITHIC REQUEST REJECTED

    A full bot in one turn loses context on later files. The admin handlers and notification logic are likely to be truncated or contain broken imports if generated together. I will split this into 9 isolated prompts, one file at a time.

    Target Directory Tree

    bot/
├── .env                 # Environment secrets
├── config.py            # Settings and env loading
├── db.py                # Database session factory
├── models/
│   ├── user.py          # User schema
│   └── order.py         # Order schema
├── services/
│   ├── order_service.py # Business logic for orders
│   └── notify_service.py# Notification dispatch logic
├── handlers/
│   ├── start.py         # Entry commands
│   ├── order.py         # Order flow logic
│   └── admin.py         # Admin management
└── main.py              # Application entry point

    Prompt Sequence

    1. config.py (No dependencies)
    2. db.py (Depends on config)
    3. models/user.py & models/order.py (Depends on db)
    4. services/ modules
    5. handlers/ modules
    6. main.py (Wiring)

    Prompt 1/9 — config.py

    Copy this block into a new prompt to generate the first module:

    "Act as a clean-room backend engineer. Write bot/config.py that reads environment variables using pydantic-settings. It must expose a Settings class containing BOT_TOKEN, DATABASE_URL, and ADMIN_IDS (a list of integers). The module must expose a single instance named settings. It imports from pydantic_settings. It must NOT import from any other local modules. After generation, confirm the file imports without errors."

    Next steps

    1. Generate bot/config.py using the prompt above.
    2. Verify the file exists and the Settings class initializes correctly.
    3. Reply with "ready" to receive Prompt 2/9 for the database layer.

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

    What you get

    Decompose a "build me an app" request into a verifiable file-by-file roadmap.Enforce strict module boundaries to prevent circular dependencies in LLM output.Ensure complex multi-file projects don't suffer from context drift or truncation.Map out a validated directory tree before a single line of code is written.

    About this skill

    When an LLM tries to generate a multi-file project in a single turn, it loses context by the fourth file. Code quality degrades, imports break, and the final modules are often hallucinated garbage. This skill intercepts monolithic "build me an app" requests and enforces a strict, isolated development workflow. It prevents context drift by forcing the agent to plan a validated directory tree and then emit exactly one prompt per file.

    What it does

    • Monolith interception identifies requests for full applications and halts generation to prevent context loss.
    • Tree validation maps a complete directory structure with single-responsibility boundaries before any code is written.
    • Clean-room prompting generates isolated, one-file-at-a-time prompts that declare strict import boundaries and public interfaces.
    • Dependency sequencing orders the build process from low-level configs and models up to high-level entry points.
    • Verification gates requires the user to confirm each file exists and imports correctly before the next prompt is emitted.

    How it works

    1. Signal detection: The agent identifies a multi-file request and halts to explain the risks of monolithic generation.
    2. Architecture mapping: It proposes a directory tree and requires your confirmation on frameworks, databases, and module boundaries.
    3. Serial emission: It provides a specific "Prompt 1/N" for the first file in the dependency chain.
    4. Verification: You generate the file in a new session or turn and confirm it works; only then does the agent provide the next prompt.

    Frameworks & tools

    Works with any language or framework including Python, TypeScript, Go, Rust, React, and Node.js. It is particularly effective for FastAPI, Express, and Telegram bot frameworks where modularity is critical.

    Why this beats prompting it yourself

    Manual decomposition is tedious and prone to logical gaps where you forget to define an interface for a later module. This skill automates the architectural planning and enforces a "verify-as-you-go" discipline that ensures the final file (like main.py) actually works with the first file (like config.py).

    Use cases

    • Building a multi-module Telegram or Discord bot from scratch.
    • Scaffolding a REST API with separate models, services, and controllers.
    • Developing a microservice where interface boundaries must be strictly enforced.
    • Migrating a single-file script into a maintainable, multi-file project structure.

    Known limitations

    Requires manual intervention to copy-paste prompts and verify files. Not suitable for quick one-file prototypes or small scripts under three files.

    How to install

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

    ~30 seconds
    1. 1

      Download the ZIP

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    2. 2

      Unzip into your skills folder

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    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.

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