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    Refactoring Safety Planner

    by heyhridyansh

    1

    Convert risky structural code changes into staged, testable, and reversible refactoring plans.

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    See it in action

    You say

    I need to extract the 'Billing' logic from our Express monolith into a separate service. We use Postgres and Redis. How do I do this without breaking active payments?

    Your agent does

    I've created a 4-stage refactoring plan. Stage 1: Define the Billing Repository interface. Stage 2: Implement a compatibility bridge for dual-writes. Stage 3: Extract the service with a feature-flagged router. Stage 4: Sunset the monolith path. Safety Score: 88/100 (Safe with safeguards).

    What you get

    Split monolithic services into modules without breaking API contracts.Replace legacy dependencies using safe compatibility bridges.Modernize database access layers while preserving data integrity.Sequence large-scale architectural migrations into reversible stages.

    About this skill

    The problem

    Large-scale refactors often fail because developers underestimate coupling or break undocumented behavior. Manual planning leads to "big bang" changes that are difficult to review and impossible to roll back safely.

    What it does

    • Analyzes current source code and dependency graphs to identify structural risks.
    • Maps stable behavior contracts including APIs, side effects, and data persistence.
    • Generates a staged, multi-step implementation plan using patterns like Branch by Abstraction or the Strangler Fig.
    • Defines explicit verification gates and rollback triggers for every stage of the change.
    • Separates architectural movement from cleanup to ensure behavior remains identical during migration.

    Why this beats prompting it yourself

    Generic LLM prompts often suggest destructive rewrites or ignore operational risks like side-effect duplication. This skill enforces a strict safety rubric that prioritizes reversibility and data integrity over aesthetic code changes.

    Use cases

    • Splitting a bloated monolithic service into modular packages.
    • Replacing a core library or database driver without breaking consumers.
    • Modernizing legacy architecture while maintaining feature parity.
    • Extracting logic into a shared module across multiple repositories.

    Known limitations

    Does not execute code changes or run tests directly. Requires existing source code or architecture documentation as input to produce an accurate plan.

    How to install

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

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    Creator

    heyhridyansh
    heyhridyansh

    24 skills on Agensi

    I create practical, AI-ready skills for Cursor, Claude Code, Codex CLI, Replit, and other agents that support the SKILL.md format. My skills focus on specific business and workflow problems, including ecommerce creative audits, design quality checks, prompt and skill validation, content systems, and process automation. Each skill is built with clear inputs, structured outputs, defined permissions, safeguards, and real-world usability.

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