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Get Started Free →Identify the critical chain — longest path considering resource contention
.claude/skills/yogsoth-ai-critical-chain-identification/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | 2% | 0% |
| case-14 | ✗→✓ | ▲ Improved | -79% | 0% |
| case-21 | ✗→✓ | ▲ Improved | -75% | 0% |
| case-01 | ✓→✓ | ＝ Same ✓ | -5% | 0% |
| case-03 | ✓→✓ | ＝ Same ✓ | 3% | 0% |
Identify the critical chain (longest resource-constrained path) using Goldratt's Critical Chain method. Differs from CPM by resolving resource contention and removing task-level safety margins.
Subagent — spawned via subagent-spawning/spawn-agent skill.
Shared: Used by dependency-constraint strategy and implementation-planning campaign.
<!-- BEGIN available-tables (generated) -->
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-01 | pass→pass | 4,277 | 3,820 | -11% | 1 | 1 | 0% | 993 | 940 | -5% | 0 | 0 | — |
case-02 | fail→pass | 3,396 | 2,782 | -18% | 1 | 1 | 0% | 618 | 629 | +2% | 0 | 0 | — |
case-03 | pass→pass | 3,240 | 2,581 | -20% | 1 | 1 | 0% | 639 | 657 | +3% | 0 | 0 | — |
case-04 | pass→pass | 13,059 | 10,934 | -16% | 1 | 1 | 0% | 2,420 | 2,214 | -9% | 0 | 0 | — |
case-05 | pass→pass | 6,489 | 6,937 | +7% | 1 | 1 | 0% | 999 | 1,204 | +21% | 0 | 0 | — |
case-06 | pass→pass | 8,351 | 6,254 | -25% | 1 | 1 | 0% | 1,355 | 1,064 | -21% | 0 | 0 | — |
case-07 | fail→fail | 9,413 | 2,112 | -78% | 1 | 1 | 0% | 1,538 | 528 | -66% | 0 | 0 | — |
case-08 | pass→pass | 5,794 | 5,918 | +2% | 1 | 1 | 0% | 919 | 1,053 | +15% | 0 | 0 | — |
case-09 | pass→pass | 11,347 | 5,779 | -49% | 1 | 1 | 0% | 1,885 | 1,092 | -42% | 0 | 0 | — |
case-10 | pass→pass | 5,527 | 4,203 | -24% | 1 | 1 | 0% | 931 | 820 | -12% | 0 | 0 | — |
case-11 | pass→pass | 9,240 | 7,622 | -18% | 1 | 1 | 0% | 1,474 | 1,254 | -15% | 0 | 0 | — |
case-12 | pass→pass | 5,813 | 6,113 | +5% | 1 | 1 | 0% | 848 | 1,115 | +31% | 0 | 0 | — |
case-13 | pass→pass | 8,495 | 6,170 | -27% | 1 | 1 | 0% | 1,112 | 1,152 | +4% | 0 | 0 | — |
case-14 | fail→pass | 13,555 | 2,178 | -84% | 1 | 1 | 0% | 2,303 | 484 | -79% | 0 | 0 | — |
case-15 | pass→pass | 14,671 | 7,625 | -48% | 1 | 1 | 0% | 1,542 | 1,339 | -13% | 0 | 0 | — |
case-16 | pass→pass | 5,994 | 5,311 | -11% | 1 | 1 | 0% | 1,044 | 1,043 | -0% | 0 | 0 | — |
case-17 | pass→pass | 4,451 | 4,406 | -1% | 1 | 1 | 0% | 730 | 863 | +18% | 0 | 0 | — |
case-18 | pass→pass | 14,210 | 10,755 | -24% | 1 | 1 | 0% | 2,163 | 1,779 | -18% | 0 | 0 | — |
case-19 | pass→pass | 5,149 | 4,086 | -21% | 1 | 1 | 0% | 870 | 871 | +0% | 0 | 0 | — |
case-20 | pass→pass | 11,989 | 7,137 | -40% | 1 | 1 | 0% | 1,924 | 1,316 | -32% | 0 | 0 | — |
case-21 | fail→pass | 9,967 | 1,904 | -81% | 1 | 1 | 0% | 1,529 | 388 | -75% | 0 | 0 | — |
case-22 | pass→pass | 6,558 | 5,811 | -11% | 1 | 1 | 0% | 1,005 | 1,043 | +4% | 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. The headline lift of +14 percentage points is the difference between those two pass rates over the 22 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.