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large-scale-refactor

Some restructurings are too big to do as a single behavior-preserving step: replacing a data layer, splitting a god module, migrating off a deprecated API. The wrong move is a **big-bang rewrite** — branch for weeks, swap everything at once, and discover the regressions in

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large-scale-refactor.md

Large-Scale Refactoring — The Strangler Fig Pattern

Some restructurings are too big to do as a single behavior-preserving step: replacing a data layer, splitting a god module, migrating off a deprecated API. The wrong move is a **big-bang rewrite** — branch for weeks, swap everything at once, and discover the regressions in production. The right move is an **incremental migration** where the system stays shippable the entire time.

Why big-bang rewrites fail

  • No safe revert point — you cannot bisect a single 4,000-line commit.
  • Review is impossible; "looks fine" is the best a reviewer can honestly say.
  • The old and new code drift apart while the rewrite is in flight.
  • Integration problems all surface at the end, at once, under pressure.

The strangler fig

Named after the vine that grows around a tree and gradually replaces it: you build the new implementation *beside* the old, route a slice of work through it, verify, then widen the slice — until the old code is dead and can be deleted.

        ┌─────────────┐
caller ─┤  seam /     ├─► OLD implementation   (shrinking)
        │  facade     ├─► NEW implementation   (growing)
        └─────────────┘

Steps

1. **Introduce a seam.** Put an interface/facade between callers and the code you're replacing, so callers depend on the seam, not the implementation. This itself is a behavior-preserving refactor. 2. **Build the new implementation behind the seam**, covered by its own tests, used by no one yet. 3. **Route one slice across** — one endpoint, one customer, one feature flag's worth of traffic. 4. **Verify in production-like conditions.** Compare outputs (consider a brief period of running both and diffing results — a "parallel run") before trusting the new path. 5. **Widen the slice** incrementally until 100% flows through the new implementation. 6. **Delete the old implementation and the seam** once nothing references them.

What keeps it safe

  • The system is shippable after every step; you can pause or roll back at any slice.
  • Each step is small enough to review and to revert.
  • Tests (and the parallel run) prove behavior is preserved as traffic shifts.
  • A feature flag makes the cutover instant to reverse.

When a strangler is overkill

For a change you can complete in a handful of small behavior-preserving steps within one session, just use the normal loop in [`safe-workflow.md`](safe-workflow.md). Reserve the strangler fig for migrations that span many commits, modules, or deploys.

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Questions about large-scale-refactor.

Which coding agents can use large-scale-refactor?
large-scale-refactor installs in Claude Code and Cursor: each loads subagents from .claude/agents, so copy the file there. Flowy's auto-invocation runs in Claude Code only.