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Get Started Free →Applies modular monolith with enforced internal boundaries. Use when teams want service-level autonomy without distributed system overhead.
.claude/skills/athola-architecture-paradigm-modular-monolith/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 31% | 0% |
| case-03 | ✗→✓ | ▲ Improved | -3% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 38% | 0% |
| case-11 | ✗→✓ | ▲ Improved | 27% | 0% |
| case-04 | ✓→✓ | = Same ✓ | -17% | 0% |
Skill not loading Check YAML frontmatter syntax and required fields
Token limits exceeded Use progressive disclosure - move details to modules
Modules not found Verify module paths in SKILL.md are correct
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.
dependency-analyzer: module dependency graph builder for spotting forbidden edgesmodule-boundary-enforcer: fails the build when a module imports across a boundaryrefactoring-planner: ranks modules by extraction-readiness for a future splitstates the conditions under which a module would be extracted into a separate service.
database table access between modules is absent (verified by schema ownership review).
least one such violation test case is documented.
pair with > 50% coupling is flagged as a candidate for boundary review.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 22,050 | 22,662 | +3% | 1 | 1 | 0% | 3,427 | 4,499 | +31% | 0 | 0 | — |
case-02 | fail→fail | 19,368 | 16,182 | -16% | 1 | 1 | 0% | 2,822 | 3,222 | +14% | 0 | 0 | — |
case-03 | fail→pass | 26,106 | 19,194 | -26% | 1 | 1 | 0% | 4,181 | 4,068 | -3% | 0 | 0 | — |
case-04 | pass→pass | 18,612 | 10,319 | -45% | 1 | 1 | 0% | 2,698 | 2,232 | -17% | 0 | 0 | — |
case-05 | pass→pass | 12,408 | 5,678 | -54% | 1 | 1 | 0% | 1,831 | 1,696 | -7% | 0 | 0 | — |
case-06 | pass→pass | 22,377 | 11,330 | -49% | 1 | 1 | 0% | 3,177 | 2,407 | -24% | 0 | 0 | — |
case-07 | pass→pass | 14,619 | 10,948 | -25% | 1 | 1 | 0% | 2,294 | 2,459 | +7% | 0 | 0 | — |
case-08 | pass→pass | 14,588 | 2,875 | -80% | 1 | 1 | 0% | 1,978 | 1,310 | -34% | 0 | 0 | — |
case-09 | fail→pass | 21,254 | 2,934 | -86% | 1 | 1 | 0% | 904 | 1,249 | +38% | 0 | 0 | — |
case-10 | fail→fail | 17,363 | 13,658 | -21% | 1 | 1 | 0% | 2,531 | 2,707 | +7% | 0 | 0 | — |
case-11 | fail→pass | 14,135 | 11,109 | -21% | 1 | 1 | 0% | 1,885 | 2,403 | +27% | 0 | 0 | — |
case-12 | pass→pass | 12,316 | 7,095 | -42% | 1 | 1 | 0% | 1,837 | 1,803 | -2% | 0 | 0 | — |
case-13 | pass→pass | 12,217 | 9,502 | -22% | 1 | 1 | 0% | 1,935 | 2,212 | +14% | 0 | 0 | — |
case-14 | pass→pass | 13,607 | 12,338 | -9% | 1 | 1 | 0% | 2,092 | 2,647 | +27% | 0 | 0 | — |
case-15 | pass→pass | 10,585 | 5,418 | -49% | 1 | 1 | 0% | 1,553 | 1,552 | -0% | 0 | 0 | — |
case-16 | pass→pass | 14,559 | 12,238 | -16% | 1 | 1 | 0% | 2,212 | 2,704 | +22% | 0 | 0 | — |
case-17 | pass→pass | 14,622 | 13,980 | -4% | 1 | 1 | 0% | 2,494 | 3,145 | +26% | 0 | 0 | — |
case-18 | fail→fail | 9,138 | 1,940 | -79% | 1 | 1 | 0% | 1,410 | 1,114 | -21% | 0 | 0 | — |
case-19 | pass→pass | 10,255 | 4,004 | -61% | 1 | 1 | 0% | 1,487 | 1,375 | -8% | 0 | 0 | — |
case-20 | pass→pass | 12,389 | 3,957 | -68% | 1 | 1 | 0% | 1,887 | 1,400 | -26% | 0 | 0 | — |
case-21 | pass→pass | 15,641 | 11,261 | -28% | 1 | 1 | 0% | 2,514 | 2,537 | +1% | 0 | 0 | — |
case-22 | pass→pass | 8,128 | 3,158 | -61% | 1 | 1 | 0% | 1,168 | 1,281 | +10% | 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, and 21 counted toward the lift figure. The other 1 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +18 percentage points is the difference between those two pass rates over the 21 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.