conclusion-sensitivity-measurement
$
npx mdskill add yogsoth-ai/de-anthropocentric-research-engine/conclusion-sensitivity-measurementSubagent — spawned via subagent-spawning/spawn-agent.
SKILL.md
.github/skills/conclusion-sensitivity-measurementView on GitHub ↗
--- name: conclusion-sensitivity-measurement description: Quantify how much conclusions change across all assumption negations and produce a sensitivity ranking. execution: subagent prompt: ./prompt.md input: original_conclusion, re_derived_conclusions used-by: assumption-criticality --- ## Execution Subagent — spawned via subagent-spawning/spawn-agent. ## Budget One unit = one complete sensitivity measurement and ranking across all tested assumptions.
More from yogsoth-ai/de-anthropocentric-research-engine
- abductive-hypothesis-generationStrategy: 面对异常的最佳解释推理
- ablation-brainstormRemove components one by one, observe system changes to reveal hidden dependencies and generate ideas from structural gaps.
- ablation-component-mappingMap system architecture to ablatable units for ablation studies
- ablation-designDesign ablation studies to isolate component contributions in ML systems
- ablation-executionRemove components one by one from a system, record the response/impact of each removal.
- abp-vulnerability-classificationClassify assumptions on 2 axes — load-bearing (how much conclusion depends on it) × vulnerable (how likely to be false). Focuses attention on High-Load × High-Vulnerable quadrant.
- abstraction-extractionExtract abstract principles from concrete domain cases. Strips domain-specific details to reveal transferable mechanisms.
- abstraction-ladderPerform bisociation at multiple abstraction levels
- abstraction-ladderingMove between concrete and abstract framings — 3 levels up (Why?) and 3 levels down (How?) to find the most productive research level.
- abstraction-to-designAbstract biological principle to design principle. Bridge from biology to engineering.