cross-consistency-filtering
$
npx mdskill add yogsoth-ai/de-anthropocentric-research-engine/cross-consistency-filteringFilters scenario configurations by pairwise consistency and narrative quality
- Reduces parameter combinations to viable scenarios through consistency checks
- Uses consistency-pair-evaluation and scenario-narrative-construction tactics
- Applies filtering rules based on consistency scores and configuration diversity
- Delivers ranked scenarios with narratives for further analysis
SKILL.md
.github/skills/cross-consistency-filteringView on GitHub ↗
--- name: cross-consistency-filtering description: "Orchestrates pairwise consistency evaluation and narrative construction to filter the morphological field" version: 1.0.0 category: experiment-execution type: tactic used-by: scenario-planning orchestrates: - consistency-pair-evaluation - scenario-narrative-construction --- # Tactic: Cross-Consistency Filtering ## Orchestration Pattern 1. **Spawn consistency evaluation** → `consistency-pair-evaluation` - Pass: Zwicky Box (full parameter space), evaluation criteria - Receive: CCA matrix with pairwise consistency scores 2. **Apply filtering rules** - Mark configurations with ANY inconsistent pair as eliminated - Mark configurations with conditionally consistent pairs as flagged - Retain configurations with all-consistent pairs as primary scenarios - If too few survive (< 3): relax to include conditionally consistent configs - If too many survive (> 10): tighten criteria or select representative subset 3. **Rank surviving configurations** - Score by: total consistency score (sum of pairwise ratings) - Score by: diversity (maximize coverage of parameter space) - Score by: relevance (proximity to current trajectory) - Select top N (typically 4-8) for narrative construction 4. **Spawn narrative construction** → `scenario-narrative-construction` (per selected config) - Pass: parameter configuration, consistency context, research approach - Receive: rich scenario narrative 5. **Validate narratives** - Check: Does narrative honor all parameter values in the configuration? - Check: Is the causal logic internally consistent? - Check: Is the scenario distinguishable from others? - If validation fails: re-spawn with specific correction guidance ## Quality Checks - [ ] CCA matrix is complete (all pairs evaluated) - [ ] Filtering produces 3-8 surviving configurations - [ ] Surviving configs span the parameter space (not clustered) - [ ] Each narrative is internally consistent with its configuration - [ ] Narratives are qualitatively distinct from each other
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.