AI Provider Key Hygiene Kit: Find, Rotate and Cap API Keys
Find leaked AI API keys in code, git history and dotfiles, rotate them in the right order, and cap spend with budgeted proxy keys. Node 20+, zero dependencies.
₹1,275
Stop watching Cursor collapse mid‑refactor. Start finishing multi‑file changes safely, consistently, and in minutes.
Cursor₹1,615
one-time payment
Stop watching Cursor collapse mid‑refactor. Start finishing multi‑file changes safely, consistently, and in minutes.
Stop watching Cursor collapse mid‑refactor. Start finishing multi‑file changes safely, consistently, and in minutes.
You know the pain: you ask Cursor to refactor a subsystem and it freezes, rewrites files inconsistently, or silently deletes code. You try again, splitting tasks manually, babysitting the model, and hoping it doesn’t break something critical. Hours disappear. Nothing feels reliable. And you still don’t trust the output.
This workflow fixes that. The Cursor Large-Refactor Workflow Engine is a single, production-ready YAML workflow (cursor-workflow-large-refactor.yml) that orchestrates a safe, chunked, validated pipeline for multi-file refactors up to hundreds of kilolines. It adds preflight analysis, scoped chunking, diff verification, rollback protection, and semantic integrity checks—so Cursor stops failing and starts acting like a dependable refactor tool.
What’s Included:
- A full large-refactor orchestration engine with chunking, boundaries, and automated checkpoints.
- Preflight impact scanner that safely maps affected files before changes begin.
- Diff-verification layer that blocks hallucinated or destructive edits.
- Security regression detector for missing auth checks, removed sanitizers, and weakened validations.
- Path whitelist/blacklist system preventing accidental edits to infra, CI, or sensitive modules.
- Stability-tuned prompt templates refined across 50+ real production projects.
- Deterministic reasoning constraints that pin model behavior for repeatable results.
- Automatic rollback logic that restores clean states when outputs go off track.
This workflow comes from real refactors shipped in large monorepos (50k–300k LOC) where naive Cursor prompts failed almost half the time. Every guard-rail was built after a real failure mode and tested against messy, interdependent codebases—not toy examples.
Who This Is For:
- Developers refactoring large TypeScript, Python, or full-stack monorepos who keep hitting model limits.
- Engineers maintaining legacy codebases that need safe, incremental modernization.
- Power users who rely on Cursor daily and want consistent, production-safe results without manual splitting.
Who This Is NOT For:
- Beginners looking for a simple “rewrite my file” prompt.
- Anyone doing tiny single-file edits that don’t need workflow safeguards.
Guarantee: If this workflow doesn’t save you hours of refactor time within your first week, email me for a full refund.
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Category
AI IDE configurations