Install any skill in seconds. Free to start, no credit card required.
Get Started Free →Applies coarse-grained service architecture for deployment independence. Use when independent deployment is needed but shared databases rule out microservices.
.claude/skills/athola-architecture-paradigm-service-based/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | -3% | 0% |
| case-02 | ✗→✓ | ▲ Improved | -23% | 0% |
| case-03 | ✗→✓ | ▲ Improved | -3% | 0% |
| case-08 | ✗→✓ | ▲ Improved | -23% | 0% |
| case-09 | ✗→✓ | ▲ Improved | -18% | 0% |
These vocabulary items name the concrete tools and abstractions that show up when the paradigm is implemented. They are not required dependencies and they are not part of the skill's tools: frontmatter (which is reserved for Claude Code tool restrictions). Use this list to disambiguate during architecture discussions.
api-gateway: single ingress that routes to coarse-grained services and centralizes cross-cutting concernsservice-registry: directory of available services with health status and contractsschema-management: shared schema repo for types crossing service boundariescoordination mechanism (service registry or API gateway) before any service is deployed independently.
for every inter-service interaction point.
owner merges breaking interface changes.
any service reading another service's tables without a view or replication layer is flagged as an architectural violation.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 16,954 | 11,785 | -30% | 1 | 1 | 0% | 2,756 | 2,670 | -3% | 0 | 0 | — |
case-02 | fail→pass | 23,788 | 12,368 | -48% | 1 | 1 | 0% | 3,487 | 2,675 | -23% | 0 | 0 | — |
case-03 | fail→pass | 18,167 | 12,824 | -29% | 1 | 1 | 0% | 2,705 | 2,637 | -3% | 0 | 0 | — |
case-04 | pass→pass | 13,470 | 9,897 | -27% | 1 | 1 | 0% | 2,053 | 2,173 | +6% | 0 | 0 | — |
case-05 | pass→pass | 17,378 | 12,433 | -28% | 1 | 1 | 0% | 2,594 | 2,499 | -4% | 0 | 0 | — |
case-06 | pass→pass | 14,116 | 6,973 | -51% | 1 | 1 | 0% | 2,069 | 1,749 | -15% | 0 | 0 | — |
case-07 | pass→pass | 19,591 | 14,506 | -26% | 1 | 1 | 0% | 2,880 | 2,917 | +1% | 0 | 0 | — |
case-08 | fail→pass | 21,348 | 11,276 | -47% | 1 | 1 | 0% | 3,294 | 2,529 | -23% | 0 | 0 | — |
case-09 | fail→pass | 17,712 | 10,005 | -44% | 1 | 1 | 0% | 2,754 | 2,253 | -18% | 0 | 0 | — |
case-10 | pass→pass | 16,171 | 13,482 | -17% | 1 | 1 | 0% | 2,508 | 2,646 | +6% | 0 | 0 | — |
case-11 | fail→pass | 15,641 | 11,068 | -29% | 1 | 1 | 0% | 2,285 | 2,266 | -1% | 0 | 0 | — |
case-12 | pass→pass | 19,640 | 10,453 | -47% | 1 | 1 | 0% | 3,001 | 2,302 | -23% | 0 | 0 | — |
case-13 | pass→pass | 17,143 | 12,263 | -28% | 1 | 1 | 0% | 2,559 | 2,606 | +2% | 0 | 0 | — |
case-14 | fail→fail | 17,637 | 10,322 | -41% | 1 | 1 | 0% | 2,501 | 2,248 | -10% | 0 | 0 | — |
case-15 | pass→pass | 16,927 | 15,052 | -11% | 1 | 1 | 0% | 2,539 | 2,973 | +17% | 0 | 0 | — |
case-16 | pass→pass | 16,025 | 9,658 | -40% | 1 | 1 | 0% | 2,425 | 2,248 | -7% | 0 | 0 | — |
case-17 | fail→pass | 12,518 | 3,406 | -73% | 1 | 1 | 0% | 1,896 | 1,326 | -30% | 0 | 0 | — |
case-18 | pass→pass | 8,471 | 6,427 | -24% | 1 | 1 | 0% | 1,302 | 1,577 | +21% | 0 | 0 | — |
case-19 | pass→pass | 10,291 | 5,138 | -50% | 1 | 1 | 0% | 1,640 | 1,556 | -5% | 0 | 0 | — |
case-20 | pass→pass | 13,960 | 9,342 | -33% | 1 | 1 | 0% | 2,034 | 2,069 | +2% | 0 | 0 | — |
case-21 | pass→pass | 18,741 | 15,179 | -19% | 1 | 1 | 0% | 2,993 | 3,301 | +10% | 0 | 0 | — |
case-22 | pass→pass | 14,635 | 3,098 | -79% | 1 | 1 | 0% | 2,372 | 1,297 | -45% | 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. 22 cases were attempted. The headline lift of +32 percentage points is the difference between those two pass rates over the 22 comparable cases.
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.