multi-model-convergence
$
npx mdskill add yogsoth-ai/de-anthropocentric-research-engine/multi-model-convergenceTests robustness by validating conclusions across multiple models and assumptions
- Identifies fragile conclusions that depend on specific assumptions
- Uses subagents for assumption enumeration, model generation, and convergence assessment
- Compares outputs from alternative models to detect divergence
- Flags fragile results and confirms robust findings through convergence
SKILL.md
.github/skills/multi-model-convergenceView on GitHub ↗
--- name: multi-model-convergence description: Wimsatt-style multi-method cross-validation — enumerate assumptions, generate alternative models, compare results, flag divergences. execution: tactic used-by: robustness-testing --- # Multi-Model Convergence Test robustness by checking if conclusions survive across different modeling choices. ## Operations - assumption-enumeration → alternative-model-generation → convergence-assessment → fragility-flagging ## Available SOPs **Subagent:** assumption-enumeration, alternative-model-generation, convergence-assessment, fragility-flagging **Import:** paper-research ## Execution Guidance For each key assumption, generate at least one alternative model. Run all models, compare outputs. Results that converge are robust; results that diverge are fragile. ## Minimum Yield ``` <HARD-GATE> - assumptions enumerated: >= 5 - alternative models generated: >= 3 - convergence assessments: >= 1 - fragility flags: assessed </HARD-GATE> ```