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Get Started Free →Systematic failure mode cataloging — generate boundary inputs, observe failures, cluster by mechanism, identify triggers, estimate frequency.
.claude/skills/yogsoth-ai-deep-insight-failure-mode-cataloging/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 35% | 0% |
| case-03 | ✗→✓ | ▲ Improved | -2% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 121% | 0% |
| case-11 | ✗→✓ | ▲ Improved | 42% | 0% |
| case-14 | ✗→✓ | ▲ Improved | 44% | 0% |
Build a comprehensive failure taxonomy.
Subagent: edge-case-generation, failure-clustering, scaling-regime-detection Import: web-search
Generate edge cases systematically (boundary values, adversarial inputs, distribution shifts, scale extremes), observe which cause failures, cluster failures by mechanism, identify common triggers.
<HARD-GATE>
- edge cases generated: >= 20
- failure clusters identified: >= 3
- triggers per cluster: >= 1
</HARD-GATE><!-- BEGIN available-tables (generated) -->
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use | | --- | --- | | deep-insight-web-search | Quick web scanning for landscape understanding. Import of web-browsing/web-search skill. Snippets only — no conclusions from snippets alone. | | edge-case-generation | Systematically generate boundary inputs — boundary values, adversarial constructions, distribution shifts, rare combinations, scale extremes. | | failure-clustering | Group observed failures by mechanism (not symptom), identify common triggers per cluster, estimate frequency and severity. | | scaling-regime-detection | Detect regime changes in scaling behavior — breakpoints where behavior qualitatively shifts, mechanisms behind transitions. |
<!-- 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-07 | pass→pass | 26,135 | 46,933 | +80% | 1 | 1 | 0% | 4,106 | 6,511 | +59% | 0 | 0 | — |
case-08 | pass→pass | 21,874 | 118,042 | +440% | 1 | 1 | 0% | 3,248 | 4,960 | +53% | 0 | 0 | — |
case-09 | pass→pass | 28,267 | 34,113 | +21% | 1 | 1 | 0% | 4,227 | 5,610 | +33% | 0 | 0 | — |
case-20 | pass→pass | 16,677 | 37,096 | +122% | 1 | 1 | 0% | 2,473 | 6,508 | +163% | 0 | 0 | — |
case-01 | fail→pass | 46,364 | 26,903 | -42% | 1 | 1 | 0% | 3,343 | 4,522 | +35% | 0 | 0 | — |
case-02 | pass→pass | 27,977 | 40,072 | +43% | 1 | 1 | 0% | 4,311 | 6,523 | +51% | 0 | 0 | — |
case-03 | fail→pass | 36,115 | 32,873 | -9% | 1 | 1 | 0% | 5,685 | 5,596 | -2% | 0 | 0 | — |
case-04 | pass→pass | 19,754 | 38,309 | +94% | 1 | 1 | 0% | 3,062 | 6,522 | +113% | 0 | 0 | — |
case-05 | pass→fail | 23,742 | 40,845 | +72% | 1 | 1 | 0% | 3,402 | 6,522 | +92% | 0 | 0 | — |
case-06 | pass→pass | 30,354 | 53,962 | +78% | 1 | 1 | 0% | 4,887 | 6,516 | +33% | 0 | 0 | — |
case-10 | fail→pass | 19,926 | 41,329 | +107% | 1 | 1 | 0% | 2,944 | 6,517 | +121% | 0 | 0 | — |
case-11 | fail→pass | 23,700 | 33,504 | +41% | 1 | 1 | 0% | 3,562 | 5,056 | +42% | 0 | 0 | — |
case-12 | pass→pass | 27,296 | 33,874 | +24% | 1 | 1 | 0% | 4,154 | 5,410 | +30% | 0 | 0 | — |
case-13 | pass→pass | 24,714 | 41,376 | +67% | 1 | 1 | 0% | 3,775 | 6,509 | +72% | 0 | 0 | — |
case-14 | fail→pass | 20,056 | 34,560 | +72% | 1 | 1 | 0% | 2,996 | 4,304 | +44% | 0 | 0 | — |
case-21 | fail→pass | 21,672 | 37,175 | +72% | 1 | 1 | 0% | 3,367 | 6,067 | +80% | 0 | 0 | — |
case-15 | fail→pass | 39,400 | 39,521 | +0% | 1 | 1 | 0% | 6,173 | 6,502 | +5% | 0 | 0 | — |
case-16 | pass→fail | 10,803 | 35,048 | +224% | 1 | 1 | 0% | 2,173 | 6,497 | +199% | 0 | 0 | — |
case-17 | pass→fail | 18,559 | 37,817 | +104% | 1 | 1 | 0% | 2,985 | 6,508 | +118% | 0 | 0 | — |
case-18 | pass→fail | 17,572 | 37,251 | +112% | 1 | 1 | 0% | 3,279 | 6,489 | +98% | 0 | 0 | — |
case-19 | fail→fail | 24,598 | 37,508 | +52% | 1 | 1 | 0% | 3,774 | 6,510 | +72% | 0 | 0 | — |
case-22 | fail→fail | 15,739 | 25,662 | +63% | 1 | 1 | 0% | 2,645 | 842 | -68% | 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 +14 percentage points is the difference between those two pass rates over the 21 comparable cases. 5 cases got worse with the skill loaded, and they are 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.