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Get Started Free →Campaign for building causal models — identify variables, map mechanisms, collect evidence, analyze interventions, validate models. Produces causal graphs in the wiki vault.
.claude/skills/yogsoth-ai-causal-modeling/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-10 | ✗→✓ | ▲ Improved | 32% | 0% |
| case-11 | ✗→✓ | ▲ Improved | 4% | 0% |
| case-12 | ✗→✓ | ▲ Improved | -15% | 0% |
| case-13 | ✗→✓ | ▲ Improved | -32% | 0% |
| case-15 | ✗→✓ | ▲ Improved | 37% | 0% |
Build causal models for research domains. Identifies variables, maps causal mechanisms, collects supporting evidence, analyzes potential interventions, and validates the resulting causal graph.
| Level | Count | Skills | |-------|-------|--------| | Strategy | 5 | variable-identification, mechanism-mapping, evidence-collection, intervention-analysis, model-validation | | Tactic | 3 | counterfactual-reasoning, evidence-weighing, feedback-loop-detection | | SOP | 10 | variable-page-creation, mechanism-edge-creation, evidence-linking, contradiction-flagging, confidence-scoring, intervention-page-creation, loop-documentation, model-gap-detection, causal-chain-query, validation-report |
| Metric | Small | Medium | Large | |--------|-------|--------|-------| | Variables identified | 8 | 20 | 40 | | Causal edges created | 15 | 40 | 80 | | Evidence pages linked | 10 | 30 | 60 | | Interventions analyzed | 2 | 5 | 10 | | Feedback loops documented | 1 | 3 | 6 |
vault_search — find existing variables and mechanismsvault_add_edge — create causal edges (derived_from, supported_by, contradicts)vault_query_graph — trace causal chainsvault_graph_stats — assess model coveragevault_lint — validate structural integrity<HARD-GATE>
context-init at campaign startcontext-checkpoint after each strategy completesknowledge-compilation after each strategy</HARD-GATE>
<!-- BEGIN available-tables (generated) -->
Optional, no fixed order; the final leaf is always a sop.
| Strategy | When to use | | --- | --- | | evidence-collection | Gather evidence for causal claims | | intervention-analysis | Analyze interventions and manipulations on the causal system | | knowledge-structuring-variable-identification | Identify key variables in the causal system | | mechanism-mapping | Map causal mechanisms between variables | | model-validation | Validate causal model consistency |
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. | | knowledge-compilation | Tactic for compiling research findings into vault pages — orchestrates page creation, updates, edge linking, and index maintenance. Minimum yield ≥3 page operations per invocation. |
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use | | --- | --- | | context-checkpoint | Append research process and results to the current Phase's context file. Covers both process and results with genuine substance. Use this skill at plan-designated checkpoint points — typically after each strategy completes or at key decision nodes within a research Phase. | | context-init | Create a new context file for a research Phase. Called once at Phase start to initialize the file that subsequent context-checkpoint calls will append to. Use this skill whenever a new research Phase begins and a fresh context file is needed. |
<!-- 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-06 | pass→fail | 39,486 | 39,709 | +1% | 1 | 1 | 0% | 6,182 | 7,210 | +17% | 0 | 0 | — |
case-01 | fail→fail | 38,932 | 39,982 | +3% | 1 | 1 | 0% | 6,219 | 7,247 | +17% | 0 | 0 | — |
case-02 | fail→fail | 38,762 | 6,301 | -84% | 1 | 1 | 0% | 6,222 | 1,398 | -78% | 0 | 0 | — |
case-03 | fail→fail | 38,108 | 36,948 | -3% | 1 | 1 | 0% | 6,203 | 7,231 | +17% | 0 | 0 | — |
case-04 | pass→pass | 14,506 | 17,763 | +22% | 1 | 1 | 0% | 2,802 | 4,451 | +59% | 0 | 0 | — |
case-05 | pass→pass | 18,189 | 31,231 | +72% | 1 | 1 | 0% | 3,370 | 6,528 | +94% | 0 | 0 | — |
case-07 | pass→pass | 15,866 | 23,383 | +47% | 1 | 1 | 0% | 2,417 | 4,895 | +103% | 0 | 0 | — |
case-08 | pass→pass | 15,376 | 19,689 | +28% | 1 | 1 | 0% | 2,491 | 4,253 | +71% | 0 | 0 | — |
case-09 | pass→fail | 16,752 | 6,955 | -58% | 1 | 1 | 0% | 2,486 | 1,331 | -46% | 0 | 0 | — |
case-10 | fail→pass | 17,606 | 16,146 | -8% | 1 | 1 | 0% | 2,758 | 3,627 | +32% | 0 | 0 | — |
case-11 | fail→pass | 14,691 | 9,407 | -36% | 1 | 1 | 0% | 2,434 | 2,538 | +4% | 0 | 0 | — |
case-12 | fail→pass | 17,383 | 6,884 | -60% | 1 | 1 | 0% | 2,552 | 2,181 | -15% | 0 | 0 | — |
case-13 | fail→pass | 20,977 | 7,570 | -64% | 1 | 1 | 0% | 3,179 | 2,171 | -32% | 0 | 0 | — |
case-14 | fail→fail | 8,198 | 6,973 | -15% | 1 | 1 | 0% | 1,193 | 2,007 | +68% | 0 | 0 | — |
case-15 | fail→pass | 15,951 | 14,771 | -7% | 1 | 1 | 0% | 2,532 | 3,470 | +37% | 0 | 0 | — |
case-16 | fail→pass | 17,179 | 8,514 | -50% | 1 | 1 | 0% | 2,490 | 2,476 | -1% | 0 | 0 | — |
case-17 | pass→pass | 9,828 | 13,366 | +36% | 1 | 1 | 0% | 1,738 | 3,283 | +89% | 0 | 0 | — |
case-18 | pass→pass | 4,598 | 11,652 | +153% | 1 | 1 | 0% | 771 | 2,921 | +279% | 0 | 0 | — |
case-19 | pass→pass | 11,489 | 9,294 | -19% | 1 | 1 | 0% | 1,777 | 2,413 | +36% | 0 | 0 | — |
case-20 | pass→pass | 19,478 | 23,042 | +18% | 1 | 1 | 0% | 2,820 | 4,607 | +63% | 0 | 0 | — |
case-21 | fail→pass | 15,036 | 16,108 | +7% | 1 | 1 | 0% | 2,306 | 3,729 | +62% | 0 | 0 | — |
case-22 | fail→pass | 11,601 | 1,614 | -86% | 1 | 1 | 0% | 1,668 | 1,287 | -23% | 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 20 counted toward the lift figure. The other 2 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 +27 percentage points is the difference between those two pass rates over the 20 comparable cases. 2 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.