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Get Started Free →Designs incremental migration strategies, identifies service boundaries, produces dependency maps and migration roadmaps, and generates API facade designs for aging codebases. Use when modernizing legacy systems, implementing strangler fig pattern or branch by abstraction, decomposing monoliths, upgrading frameworks or languages, or reducing technical debt without disrupting business operations.
.claude/skills/jeffallan-legacy-modernizer/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 10% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 20% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 20% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 20% | 0% |
| case-11 | ✗→✓ | ▲ Improved | -10% | 0% |
references/system-assessment.md for code analysis templates.Load detailed guidance based on context:
| Topic | Reference | Load When | |-------|-----------|-----------| | Strangler Fig | references/strangler-fig-pattern.md | Incremental replacement, facade layer, routing | | Refactoring | references/refactoring-patterns.md | Extract service, branch by abstraction, adapters | | Migration | references/migration-strategies.md | Database, UI, API, framework migrations | | Testing | references/legacy-testing.md | Characterization tests, golden master, approval | | Assessment | references/system-assessment.md | Code analysis, dependency mapping, risk evaluation |
python# facade.py — routes requests to legacy or new service based on a feature flag import os from legacy_service import LegacyOrderService from new_service import NewOrderService class OrderServiceFacade: def __init__(self): self._legacy = LegacyOrderService() self._new = NewOrderService() def get_order(self, order_id: str): if os.getenv("USE_NEW_ORDER_SERVICE", "false").lower() == "true": return self._new.fetch(order_id) return self._legacy.get(order_id)
python# feature_flags.py — thin wrapper around an environment or config-based flag store import os def flag_enabled(flag_name: str, default: bool = False) -> bool: """Check whether a migration feature flag is active.""" return os.getenv(flag_name, str(default)).lower() == "true" # Usage if flag_enabled("USE_NEW_PAYMENT_GATEWAY"): result = new_gateway.charge(order) else: result = legacy_gateway.charge(order)
python# test_characterization_orders.py # Captures existing legacy behavior as a golden-master safety net. import pytest from legacy_service import LegacyOrderService service = LegacyOrderService() @pytest.mark.parametrize("order_id,expected_status", [ ("ORD-001", "SHIPPED"), ("ORD-002", "PENDING"), ("ORD-003", "CANCELLED"), ]) def test_order_status_golden_master(order_id, expected_status): """Fail loudly if legacy behavior changes unexpectedly.""" result = service.get(order_id) assert result["status"] == expected_status, ( f"Characterization broken for {order_id}: " f"expected {expected_status}, got {result['status']}" )
When implementing modernization, provide:
Strangler fig pattern, branch by abstraction, characterization testing, incremental migration, feature flags, canary deployments, API versioning, database refactoring, microservices extraction, technical debt reduction, zero-downtime deployment
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 32,000 | 30,075 | -6% | 1 | 1 | 0% | 6,220 | 6,865 | +10% | 0 | 0 | — |
case-02 | fail→pass | 35,974 | 29,467 | -18% | 1 | 1 | 0% | 6,212 | 7,450 | +20% | 0 | 0 | — |
case-03 | fail→pass | 32,364 | 28,567 | -12% | 1 | 1 | 0% | 6,218 | 7,433 | +20% | 0 | 0 | — |
case-04 | pass→pass | 20,423 | 23,260 | +14% | 1 | 1 | 0% | 4,024 | 6,358 | +58% | 0 | 0 | — |
case-05 | pass→pass | 13,276 | 17,337 | +31% | 1 | 1 | 0% | 2,757 | 4,717 | +71% | 0 | 0 | — |
case-06 | pass→pass | 17,376 | 18,646 | +7% | 1 | 1 | 0% | 3,986 | 5,788 | +45% | 0 | 0 | — |
case-07 | pass→pass | 14,987 | 7,341 | -51% | 1 | 1 | 0% | 2,465 | 2,660 | +8% | 0 | 0 | — |
case-08 | fail→pass | 13,617 | 7,980 | -41% | 1 | 1 | 0% | 2,161 | 2,596 | +20% | 0 | 0 | — |
case-09 | pass→pass | 16,269 | 14,642 | -10% | 1 | 1 | 0% | 2,881 | 4,180 | +45% | 0 | 0 | — |
case-10 | pass→pass | 15,629 | 13,860 | -11% | 1 | 1 | 0% | 2,684 | 3,680 | +37% | 0 | 0 | — |
case-11 | fail→pass | 11,994 | 2,155 | -82% | 1 | 1 | 0% | 1,762 | 1,581 | -10% | 0 | 0 | — |
case-12 | fail→pass | 14,675 | 11,301 | -23% | 1 | 1 | 0% | 2,650 | 3,396 | +28% | 0 | 0 | — |
case-13 | fail→pass | 7,474 | 1,823 | -76% | 1 | 1 | 0% | 1,271 | 1,539 | +21% | 0 | 0 | — |
case-14 | fail→pass | 11,745 | 3,662 | -69% | 1 | 1 | 0% | 1,717 | 1,528 | -11% | 0 | 0 | — |
case-15 | fail→pass | 8,783 | 2,533 | -71% | 1 | 1 | 0% | 1,330 | 1,610 | +21% | 0 | 0 | — |
case-16 | fail→fail | 13,829 | 13,243 | -4% | 1 | 1 | 0% | 2,391 | 3,530 | +48% | 0 | 0 | — |
case-17 | fail→pass | 11,950 | 7,155 | -40% | 1 | 1 | 0% | 2,031 | 2,619 | +29% | 0 | 0 | — |
case-18 | fail→pass | 16,718 | 11,956 | -28% | 1 | 1 | 0% | 2,924 | 3,473 | +19% | 0 | 0 | — |
case-19 | pass→pass | 12,576 | 9,392 | -25% | 1 | 1 | 0% | 1,938 | 2,562 | +32% | 0 | 0 | — |
case-20 | fail→pass | 13,622 | 8,389 | -38% | 1 | 1 | 0% | 2,175 | 2,774 | +28% | 0 | 0 | — |
case-21 | pass→pass | 18,197 | 16,715 | -8% | 1 | 1 | 0% | 3,174 | 4,358 | +37% | 0 | 0 | — |
case-22 | fail→fail | 8,679 | 1,984 | -77% | 1 | 1 | 0% | 1,344 | 1,608 | +20% | 0 | 0 | — |
case-23 | pass→pass | 13,069 | 10,608 | -19% | 1 | 1 | 0% | 1,956 | 3,239 | +66% | 0 | 0 | — |
case-24 | pass→pass | 14,128 | 19,365 | +37% | 1 | 1 | 0% | 2,043 | 3,132 | +53% | 0 | 0 | — |
case-25 | fail→pass | 18,562 | 13,722 | -26% | 1 | 1 | 0% | 2,878 | 3,533 | +23% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 25 cases were attempted. The headline lift of +52 percentage points is the difference between those two pass rates over the 25 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.