Legacy Upgrade

The problem

Rewrites fail when they attempt to replace a monolith in a single release. Developers lack the tooling to safely sequence changes, leading to stalled migrations, untested edge cases, and catastrophic cutovers.

What it does

• Identifies code seams to isolate legacy logic without breaking callers.
• Generates characterization test scaffolding to pin existing behavior, including bugs.
• Plans incremental migrations using the Strangler Fig pattern and vertical slicing.
• Structures data migrations with dual-write, backfill, and reconciliation sequences.
• Drafts rollout plans with kill switches, cohorts, and clear rollback triggers.

Why this beats prompting it yourself

General AI tends to suggest "big bang" rewrites that ignore the reality of production data. These rules enforce a specific, safe sequence that prioritizes shippable increments and verified behavior over risky code replacements.

Use cases

• Extracting a complex pricing module from a monolith into a microservice.
• Modernizing a database schema while keeping the application online.
• Implementing feature flags to safely test new logic against production traffic.
• Scoring and prioritizing technical debt based on cost and change frequency.

Known limitations

Does not execute migrations, flip feature flags in production, or read live traffic. All actions must be performed by the developer.