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Get Started Free →Define or implement regression proof: test strategy, black-box/integration tests, KPIs, thresholds, audits, or missing tests.
.claude/skills/hashgraph-online-create-test/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | -25% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 6% | 0% |
| case-10 | ✗→✓ | ▲ Improved | -6% | 0% |
| case-18 | ✗→✓ | ▲ Improved | 31% | 0% |
| case-14 | ✓→✓ | = Same ✓ | -3% | 0% |
Start with the user need or business rule that must always hold, not files, coverage, or internal calls. Inspect for facts; ask only for business decisions that cannot be discovered.
Establish:
Never invent a metric or threshold; record the gap.
Prefer the highest reliable boundary:
Mock only beyond the verified boundary. For database or migration work, read integration patterns. When proving a process manager, worker, container entrypoint, or deployed artifact, exercise the real container or OS image, not only a host process, and verify worker replacement, signal handling, and graceful shutdown where those boundaries apply.
For strategy or audit only, return the contract, prioritized scenarios, proof method, and blind spots. Judge tests by failures caught, not assertion or coverage counts.
Follow project layout and the development loop. Add the smallest important proof, observe it fail, make approved source changes, then observe it pass.
Assert outcomes, state, events, metrics, and error contracts rather than calls. Run focused and relevant suites. Report what the evidence proves and what it does not cover.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-14 | pass→pass | 16,184 | 13,316 | -18% | 1 | 1 | 0% | 1,745 | 1,693 | -3% | 0 | 0 | — |
case-07 | fail→fail | 22,700 | 20,590 | -9% | 1 | 1 | 0% | 4,555 | 4,042 | -11% | 0 | 0 | — |
case-01 | fail→pass | 39,320 | 29,321 | -25% | 1 | 1 | 0% | 5,743 | 4,309 | -25% | 0 | 0 | — |
case-02 | fail→fail | 35,567 | 10,902 | -69% | 1 | 1 | 0% | 5,697 | 670 | -88% | 0 | 0 | — |
case-03 | fail→fail | 38,449 | 13,227 | -66% | 1 | 1 | 0% | 7,706 | 580 | -92% | 0 | 0 | — |
case-04 | pass→pass | 24,940 | 30,941 | +24% | 1 | 1 | 0% | 4,291 | 4,759 | +11% | 0 | 0 | — |
case-05 | fail→fail | 22,948 | 16,277 | -29% | 1 | 1 | 0% | 3,507 | 3,453 | -2% | 0 | 0 | — |
case-06 | pass→pass | 44,501 | 38,284 | -14% | 1 | 1 | 0% | 8,243 | 7,199 | -13% | 0 | 0 | — |
case-08 | fail→pass | 20,278 | 19,259 | -5% | 1 | 1 | 0% | 2,493 | 2,638 | +6% | 0 | 0 | — |
case-09 | pass→pass | 20,116 | 12,003 | -40% | 1 | 1 | 0% | 2,379 | 2,228 | -6% | 0 | 0 | — |
case-10 | fail→pass | 18,806 | 15,096 | -20% | 1 | 1 | 0% | 2,172 | 2,050 | -6% | 0 | 0 | — |
case-11 | pass→pass | 16,570 | 29,982 | +81% | 1 | 1 | 0% | 2,662 | 4,397 | +65% | 0 | 0 | — |
case-12 | pass→pass | 9,011 | 8,778 | -3% | 1 | 1 | 0% | 1,614 | 1,890 | +17% | 0 | 0 | — |
case-13 | pass→pass | 19,312 | 20,522 | +6% | 1 | 1 | 0% | 2,432 | 2,907 | +20% | 0 | 0 | — |
case-15 | pass→pass | 16,688 | 16,440 | -1% | 1 | 1 | 0% | 2,837 | 3,284 | +16% | 0 | 0 | — |
case-16 | pass→pass | 19,712 | 19,518 | -1% | 1 | 1 | 0% | 2,350 | 3,591 | +53% | 0 | 0 | — |
case-17 | pass→pass | 17,207 | 7,044 | -59% | 1 | 1 | 0% | 1,815 | 1,479 | -19% | 0 | 0 | — |
case-18 | fail→pass | 13,336 | 14,856 | +11% | 1 | 1 | 0% | 2,322 | 3,031 | +31% | 0 | 0 | — |
case-19 | pass→pass | 17,280 | 14,871 | -14% | 1 | 1 | 0% | 2,522 | 2,954 | +17% | 0 | 0 | — |
case-20 | pass→pass | 18,970 | 20,160 | +6% | 1 | 1 | 0% | 2,406 | 2,815 | +17% | 0 | 0 | — |
case-21 | pass→pass | 12,072 | 8,969 | -26% | 1 | 1 | 0% | 2,220 | 1,916 | -14% | 0 | 0 | — |
case-22 | pass→pass | 19,471 | 22,738 | +17% | 1 | 1 | 0% | 3,234 | 4,461 | +38% | 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 20 counted toward the lift figure. The other 2 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 20 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.