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Get Started Free →Tactic for verifying ontology consistency — detect contradictions, cycles, orphans, and type violations.
.claude/skills/yogsoth-ai-knowledge-structuring-consistency-checking/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-10 | ✗→✓ | ▲ Improved | -67% | 0% |
| case-17 | ✗→✓ | ▲ Improved | -41% | 0% |
| case-18 | ✗→✓ | ▲ Improved | -10% | 0% |
| case-04 | ✓→✓ | ＝ Same ✓ | -45% | 0% |
| case-05 | ✓→✓ | ＝ Same ✓ | 20% | 0% |
Verify the ontology is internally consistent. No contradictions, no cycles in hierarchies, no orphaned concepts, no type violations.
<HARD-GATE> ≥1 inconsistency identified and resolved per invocation. If vault_lint returns zero issues and graph inspection finds no problems, report clean state. </HARD-GATE>
<!-- BEGIN available-tables (generated) -->
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use | | --- | --- | | gap-detection | SOP for finding structural gaps in the ontology — missing concepts, thin branches, disconnected clusters. | | merge-candidates | SOP for identifying near-duplicate concepts that should be merged. |
<!-- END available-tables (generated) -->
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-04 | pass→pass | 15,580 | 21,487 | +38% | 1 | 1 | 0% | 1,882 | 1,030 | -45% | 0 | 0 | — |
case-01 | fail→fail | 48,260 | 102,188 | +112% | 1 | 1 | 0% | 893 | 1,029 | +15% | 0 | 0 | — |
case-02 | fail→fail | 30,677 | 46,105 | +50% | 1 | 1 | 0% | 1,557 | 531 | -66% | 0 | 0 | — |
case-03 | fail→fail | 19,089 | 50,244 | +163% | 1 | 1 | 0% | 2,275 | 566 | -75% | 0 | 0 | — |
case-05 | pass→pass | 13,912 | 17,994 | +29% | 1 | 1 | 0% | 1,889 | 2,270 | +20% | 0 | 0 | — |
case-06 | pass→pass | 26,505 | 12,799 | -52% | 1 | 1 | 0% | 1,738 | 1,608 | -7% | 0 | 0 | — |
case-07 | pass→pass | 15,773 | 13,469 | -15% | 1 | 1 | 0% | 1,908 | 1,680 | -12% | 0 | 0 | — |
case-08 | pass→pass | 16,388 | 11,587 | -29% | 1 | 1 | 0% | 2,477 | 818 | -67% | 0 | 0 | — |
case-09 | pass→pass | 14,425 | 7,967 | -45% | 1 | 1 | 0% | 1,581 | 866 | -45% | 0 | 0 | — |
case-10 | fail→pass | 19,399 | 8,488 | -56% | 1 | 1 | 0% | 2,255 | 751 | -67% | 0 | 0 | — |
case-11 | pass→pass | 40,727 | 11,622 | -71% | 1 | 1 | 0% | 1,547 | 1,072 | -31% | 0 | 0 | — |
case-12 | pass→pass | 31,063 | 20,394 | -34% | 1 | 1 | 0% | 4,726 | 1,900 | -60% | 0 | 0 | — |
case-13 | fail→fail | 20,081 | 13,345 | -34% | 1 | 1 | 0% | 2,144 | 809 | -62% | 0 | 0 | — |
case-14 | pass→pass | 14,333 | 23,258 | +62% | 1 | 1 | 0% | 1,583 | 1,882 | +19% | 0 | 0 | — |
case-15 | pass→pass | 16,422 | 5,139 | -69% | 1 | 1 | 0% | 1,971 | 941 | -52% | 0 | 0 | — |
case-16 | pass→pass | 8,495 | 2,693 | -68% | 1 | 1 | 0% | 1,291 | 735 | -43% | 0 | 0 | — |
case-17 | fail→pass | 18,172 | 9,295 | -49% | 1 | 1 | 0% | 1,882 | 1,107 | -41% | 0 | 0 | — |
case-18 | fail→pass | 12,698 | 7,833 | -38% | 1 | 1 | 0% | 1,973 | 1,779 | -10% | 0 | 0 | — |
case-19 | pass→pass | 10,406 | 16,568 | +59% | 1 | 1 | 0% | 1,589 | 1,353 | -15% | 0 | 0 | — |
case-20 | pass→pass | 12,854 | 18,576 | +45% | 1 | 1 | 0% | 2,284 | 3,239 | +42% | 0 | 0 | — |
case-21 | pass→pass | 33,783 | 17,347 | -49% | 1 | 1 | 0% | 3,219 | 3,658 | +14% | 0 | 0 | — |
case-22 | pass→pass | 10,727 | 11,147 | +4% | 1 | 1 | 0% | 1,699 | 1,897 | +12% | 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 18 counted toward the lift figure. The other 4 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 +14 percentage points is the difference between those two pass rates over the 18 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.