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Get Started Free →Identify critical dependencies from ablation results, producing a dependency graph and highlighting critical components.
.claude/skills/yogsoth-ai-dependency-identification/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-10 | ✗→✓ | ▲ Improved | -81% | 0% |
| case-20 | ✓→✓ | ＝ Same ✓ | -12% | 0% |
| case-21 | ✓→✓ | ＝ Same ✓ | 17% | 0% |
| case-22 | ✓→✓ | ＝ Same ✓ | 0% | 0% |
| case-02 | ✗→✗ | ＝ Same ✗ | 130% | 0% |
Extract a dependency graph from ablation results, identifying which components are critical and how they relate.
Subagent — spawned via subagent-spawning/spawn-agent skill.
Dependency analysis requires synthesizing ablation data into graph structures and identifying non-obvious transitive dependencies. Benefits from focused analytical context.
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Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use | | --- | --- | | spawn-agent | Spawn a customized CC subagent with full MCP tool access. Used by SOPs that declare execution: subagent. |
<!-- 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-02 | fail→fail | 4,442 | 8,892 | +100% | 1 | 1 | 0% | 655 | 1,505 | +130% | 0 | 0 | — |
case-03 | fail→fail | 20,303 | 24,127 | +19% | 1 | 1 | 0% | 2,920 | 3,412 | +17% | 0 | 0 | — |
case-01 | fail→fail | 8,139 | 21,042 | +159% | 1 | 1 | 0% | 1,094 | 2,567 | +135% | 0 | 0 | — |
case-04 | fail→fail | 10,221 | 8,111 | -21% | 1 | 1 | 0% | 1,494 | 580 | -61% | 0 | 0 | — |
case-05 | fail→fail | 5,597 | 16,664 | +198% | 1 | 1 | 0% | 742 | 2,423 | +227% | 0 | 0 | — |
case-06 | fail→fail | 20,185 | 25,062 | +24% | 1 | 1 | 0% | 3,175 | 3,863 | +22% | 0 | 0 | — |
case-07 | fail→fail | 6,205 | 17,996 | +190% | 1 | 1 | 0% | 930 | 3,078 | +231% | 0 | 0 | — |
case-08 | fail→fail | 9,827 | 14,249 | +45% | 1 | 1 | 0% | 1,368 | 569 | -58% | 0 | 0 | — |
case-09 | fail→fail | 4,778 | 8,060 | +69% | 1 | 1 | 0% | 665 | 1,146 | +72% | 0 | 0 | — |
case-10 | fail→pass | 17,714 | 2,308 | -87% | 1 | 1 | 0% | 2,542 | 490 | -81% | 0 | 0 | — |
case-11 | fail→fail | 6,415 | 4,356 | -32% | 1 | 1 | 0% | 921 | 757 | -18% | 0 | 0 | — |
case-12 | fail→fail | 6,888 | 12,410 | +80% | 1 | 1 | 0% | 884 | 919 | +4% | 0 | 0 | — |
case-13 | fail→fail | 19,936 | 3,773 | -81% | 1 | 1 | 0% | 3,299 | 466 | -86% | 0 | 0 | — |
case-14 | fail→fail | 6,605 | 5,564 | -16% | 1 | 1 | 0% | 1,010 | 687 | -32% | 0 | 0 | — |
case-15 | fail→fail | 15,939 | 10,888 | -32% | 1 | 1 | 0% | 2,167 | 793 | -63% | 0 | 0 | — |
case-16 | fail→fail | 9,909 | 16,685 | +68% | 1 | 1 | 0% | 1,449 | 2,747 | +90% | 0 | 0 | — |
case-17 | fail→fail | 23,445 | 38,031 | +62% | 1 | 1 | 0% | 3,681 | 5,234 | +42% | 0 | 0 | — |
case-18 | fail→fail | 22,506 | 38,461 | +71% | 1 | 1 | 0% | 2,962 | 4,426 | +49% | 0 | 0 | — |
case-19 | fail→fail | 22,990 | 19,702 | -14% | 1 | 1 | 0% | 1,930 | 2,184 | +13% | 0 | 0 | — |
case-20 | pass→pass | 12,852 | 10,337 | -20% | 1 | 1 | 0% | 1,884 | 1,653 | -12% | 0 | 0 | — |
case-21 | pass→pass | 8,570 | 9,211 | +7% | 1 | 1 | 0% | 1,182 | 1,379 | +17% | 0 | 0 | — |
case-22 | pass→pass | 11,979 | 11,114 | -7% | 1 | 1 | 0% | 1,688 | 1,694 | +0% | 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 +5 percentage points is the difference between those two pass rates over the 18 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.