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    Structured Impl Planner

    1

    You get the full implementation plan — constraints, risks, rollback points — before you open an editor.

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    Structured Impl Planner

    Structured Impl Planner

    Example session with this skill installed

    Plan how to add CSV export of invoices to our Laravel admin panel. Accounts can have up to 50k invoices, users must filter by date range, and the export should run in the background and notify the user when ready.

    • Read your context and instructions
    • Compiled the structured impl planner

    Implementation plan for background invoice CSV export: six verifiable steps with rollback points.

    Context to read first

    • The existing invoice list controller and its filter query
    • Queue driver config and how other jobs are dispatched
    • Storage disk used for user downloads and the notification setup

    Steps

    #StepVerify by
    1Extract the date-filter query into a reusable scopeExisting list tests still pass
    2Add exports table (user, status, path, filters)Migrate and rollback locally
    3Create ExportInvoicesJob using lazy() or chunk() to stream rowsJob test with 50k seeded invoices completes
    4Endpoint to request an export, returns 202Feature test asserts the job is queued
    5Notify the user with a signed, expiring download linkNotification test and a manual download
    6UI button and status indicatorManual check in staging

    File-level change map

    app/Models/Invoice.php               scope
    app/Jobs/ExportInvoicesJob.php       new
    app/Http/Controllers/Admin/InvoiceExportController.php
    app/Notifications/ExportReady.php    new
    database/migrations/..._create_exports_table.php
    routes/web.php, invoices/index.blade.php
    

    Rollback points

    • After step 1: refactor only, safe to ship alone
    • After step 4: hide the route behind a flag if the job misbehaves
    • Step 2 migration is additive and can be rolled back

    Next steps

    • Confirm the CSV columns
    • Decide how long export files are kept

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

    About this skill

    Jumping straight to code works until step seven, where the change you didn't anticipate meets the four files you already modified and nothing fits together anymore. This skill forces the planning work that prevents mid-implementation pivots: it maps every affected module before you write a line, surfaces the dependency order that makes changes non-reversible, identifies the test harness you need to build first, and writes a sequenced task list where each step is independently mergeable. You get the full implementation plan — constraints, risks, rollback points — before you open an editor. Use it when the feature touches more than two files, when the spec is incomplete, or when your last three attempts at 'just implementing it' produced a mess you refactored for longer than you built. Give it your codebase context, the feature description, and the constraints that matter; it returns a plan you can execute without rediscovering the architecture mid-flight.

    • Requires user to provide current file context
    • Planning only: does not execute code changes
    • Might overlook implicit architectural constraints in very large repos

    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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    Permissions

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    Listed4 months ago
    Updated12 days ago

    What's inside

    Frequently Asked Questions

    This skill acts as a pre-coding architect that maps out dependencies, identifies required test harnesses, and creates a sequenced, mergeable task list to prevent mid-project refactoring.

    It is designed for any multi-file feature addition, complex refactoring, or when you are working with an unfamiliar codebase where architectural side effects are difficult to predict.

    You will receive the proprietary skill configuration and documentation on how to integrate it with your preferred AI agent or IDE environment.

    This skill is compatible with most major LLMs (like GPT-4 and Claude 3) and can be easily integrated into common AI agent frameworks and developer tools via standard prompt injection or system role configuration.

    Yes, the plan includes a specific 'dependency order' and identifies rollback points, ensuring that each step of your implementation is independently stable and mergeable.