whole codebase refactor manager

    by nowrich

    1

    Orchestrate and verify large-scale, cross-module codebase refactors with automated planning and testing.

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    Works with the AI tools you already use

    CClaude CodeCCursorCCodex CLIGGitHub CopilotGGemini CLIVVS CodeWWindsurfMManus+14 more

    See it in action

    You say

    Run a codebase-wide refactor to migrate all 'UserAccount' references to 'MemberProfile' in the /src directory. Set risk_tolerance to medium.

    Your agent does

    Plan confirmed: 42 files identified. Execution: 4 agents spawned. Results: 42 files modified, 385 instances replaced. Verification: Jest suite passed (112/112). Report: All references in /src/components and /src/hooks updated. No orphans found.

    What you get

    Migrate legacy API patterns to new standards across all service layers.Safely prune deprecated features and remove dead code paths globally.Standardize error handling logic across distributed module boundaries.Automate large-scale directory restructuring and import path updates.

    About this skill

    The problem

    Large-scale refactoring across multiple modules often leads to broken imports, missing files, and inconsistent patterns when handled manually. Developers struggle to maintain focus and verification standards when migrating conventions or deleting features across dozens of files.

    What it does

    • Scans codebase architecture and dependencies to map out cross-module relationships.
    • Generates a comprehensive, file-by-file change plan with associated risk assessments.
    • Executes changes in parallel by splitting tasks across multiple agent sections based on layers.
    • Runs local test suites and application startup checks to verify the integrity of the refactor.
    • Provides a detailed plan-versus-actual report highlighting every modification and regression.

    Frameworks & tools

    Compatible with any Git-based repository. Automatically detects and adapts to React, Vue, and various backend service frameworks.

    Why this beats prompting it yourself

    Standard LLM prompts often lose context or hallucinate when dealing with wide-reaching architectural changes. This skill enforces a strict multi-step workflow of analysis, planning, and verification that prevents the "partial refactor" trap common in manual sessions.

    Use cases

    • Adding unique test IDs across all UI components for automated testing.
    • Safely deleting deprecated features and their associated dependencies across services.
    • Migrating naming conventions or API patterns throughout a large codebase.
    • Reorganizing directory structures and updating all affected relative imports.

    How to install

    Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.

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    Creator

    nowrich
    nowrich

    23 skills on Agensi

    Masterclass Creator Skill

    Frequently Asked Questions

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