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Get Started Free →Analyze interventions and manipulations on the causal system
.claude/skills/yogsoth-ai-intervention-analysis/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 15% | 0% |
| case-03 | ✗→✓ | ▲ Improved | -5% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 112% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 80% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 27% | 0% |
Identify and analyze interventions — deliberate manipulations of variables in the causal system — and predict their downstream outcomes given the current causal graph. This strategy operationalizes the model by showing what would happen if a practitioner or researcher actually pulled a lever.
CC must distinguish clearly between observational associations and interventional effects. An intervention severs the incoming edges to the manipulated variable (do-calculus style), so the predicted outcome may differ substantially from what correlation alone would suggest. For each intervention analyzed, CC should trace the causal path forward through the graph, note moderating variables that could dampen or amplify the effect, and flag any feedback loops that make the outcome path-dependent. Predicted outcomes should be directional claims (increases, decreases, no expected change) with explicit uncertainty where the graph is incomplete.
| Metric | S | M | L | |--------|---|---|---| | Interventions analyzed | 2 | 5 | 10 | | Intervention pages created | 2 | 5 | 10 | | Predicted outcomes | 3 | 8 | 15 |
| Metric | Target | Current | Status |
|---------------------------|--------|---------|--------|
| Interventions analyzed | S:2 / M:5 / L:10 | 0 | ⬜ |
| Intervention pages created | S:2 / M:5 / L:10 | 0 | ⬜ |
| Predicted outcomes | S:3 / M:8 / L:15 | 0 | ⬜ |<HARD-GATE> Cannot exit until 80% of budget met. Print state ledger before each iteration decision. </HARD-GATE>
<!-- BEGIN available-tables (generated) -->
Optional, no fixed order; the final leaf is always a sop.
| Tactic | When to use | | --- | --- | | counterfactual-reasoning | Tactic for reasoning about what would happen if variables were different — supports causal identification and intervention analysis. | | evidence-weighing | Tactic for assessing the strength and relevance of evidence for causal claims — distinguishes correlation from causation. | | feedback-loop-detection | Tactic for identifying circular causation — detect feedback loops, classify as reinforcing or balancing, document loop structure. |
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use | | --- | --- | | intervention-page-creation | SOP for documenting an intervention — what happens when a causal variable is manipulated. |
<!-- 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 | fail→pass | 40,287 | 76,072 | +89% | 1 | 1 | 0% | 6,076 | 7,005 | +15% | 0 | 0 | — |
case-02 | fail→fail | 53,793 | 47,043 | -13% | 1 | 1 | 0% | 7,119 | 8,956 | +26% | 0 | 0 | — |
case-03 | fail→pass | 95,030 | 42,914 | -55% | 1 | 1 | 0% | 8,285 | 7,872 | -5% | 0 | 0 | — |
case-04 | fail→pass | 28,485 | 55,215 | +94% | 1 | 1 | 0% | 3,997 | 8,472 | +112% | 0 | 0 | — |
case-05 | fail→pass | 41,889 | 48,885 | +17% | 1 | 1 | 0% | 4,942 | 8,908 | +80% | 0 | 0 | — |
case-06 | pass→pass | 24,939 | 38,270 | +53% | 1 | 1 | 0% | 4,943 | 8,092 | +64% | 0 | 0 | — |
case-07 | fail→pass | 42,433 | 50,282 | +18% | 1 | 1 | 0% | 7,028 | 8,902 | +27% | 0 | 0 | — |
case-08 | fail→pass | 26,191 | 36,188 | +38% | 1 | 1 | 0% | 3,946 | 6,491 | +64% | 0 | 0 | — |
case-09 | fail→pass | 38,029 | 65,199 | +71% | 1 | 1 | 0% | 5,471 | 8,894 | +63% | 0 | 0 | — |
case-10 | fail→pass | 22,344 | 47,109 | +111% | 1 | 1 | 0% | 3,507 | 6,535 | +86% | 0 | 0 | — |
case-11 | fail→pass | 42,683 | 31,721 | -26% | 1 | 1 | 0% | 5,492 | 7,248 | +32% | 0 | 0 | — |
case-12 | fail→pass | 28,787 | 46,050 | +60% | 1 | 1 | 0% | 5,761 | 8,895 | +54% | 0 | 0 | — |
case-13 | pass→pass | 20,763 | 33,393 | +61% | 1 | 1 | 0% | 3,859 | 5,715 | +48% | 0 | 0 | — |
case-14 | fail→pass | 27,546 | 46,387 | +68% | 1 | 1 | 0% | 4,920 | 6,950 | +41% | 0 | 0 | — |
case-15 | fail→fail | 30,250 | 39,770 | +31% | 1 | 1 | 0% | 5,642 | 8,066 | +43% | 0 | 0 | — |
case-16 | fail→pass | 26,046 | 21,223 | -19% | 1 | 1 | 0% | 4,383 | 3,507 | -20% | 0 | 0 | — |
case-17 | fail→pass | 37,755 | 41,909 | +11% | 1 | 1 | 0% | 7,067 | 8,881 | +26% | 0 | 0 | — |
case-18 | pass→pass | 32,022 | 38,259 | +19% | 1 | 1 | 0% | 5,094 | 8,076 | +59% | 0 | 0 | — |
case-19 | fail→fail | 11,417 | 27,625 | +142% | 1 | 1 | 0% | 1,763 | 5,783 | +228% | 0 | 0 | — |
case-20 | pass→fail | 8,823 | 46,997 | +433% | 1 | 1 | 0% | 1,445 | 8,894 | +516% | 0 | 0 | — |
case-21 | pass→fail | 15,235 | 55,307 | +263% | 1 | 1 | 0% | 3,082 | 8,893 | +189% | 0 | 0 | — |
case-22 | fail→pass | 40,236 | 40,878 | +2% | 1 | 1 | 0% | 7,265 | 7,750 | +7% | 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 +55 percentage points is the difference between those two pass rates over the 22 comparable cases. 3 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.