disagreement-mapping
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npx mdskill add yogsoth-ai/de-anthropocentric-research-engine/disagreement-mappingMap the structure of disagreement rather than forcing convergence. Collect diverse judgments, identify natural clusters of opinion, extract the core arguments supporting each cluster, and produce a visual map of the disagreement topology.
SKILL.md
.github/skills/disagreement-mappingView on GitHub ↗
--- name: disagreement-mapping description: Map disagreement structure by collecting judgments, clustering opinions, extracting arguments per cluster, and visualizing fault lines. execution: tactic used-by: structured-consensus --- # Disagreement Mapping Map the structure of disagreement rather than forcing convergence. Collect diverse judgments, identify natural clusters of opinion, extract the core arguments supporting each cluster, and produce a visual map of the disagreement topology. ## Stages 1. **Collect** — Run `judgment-collection` to gather positions and reasoning from all perspectives 2. **Cluster** — Run `cluster-analysis` to identify natural groupings of similar positions 3. **Extract** — Run `argument-extraction` for each cluster to surface core arguments 4. **Visualize** — Run `disagreement-visualization` to produce the disagreement map ## Available SOPs | SOP | Role in Tactic | |-----|---------------| | judgment-collection | Gather positions with reasoning from all perspectives | | cluster-analysis | Identify natural opinion clusters and characterize them | | argument-extraction | Extract and steel-man core arguments for each cluster | | disagreement-visualization | Produce structured map of clusters, arguments, and fault lines | ## Execution Guidance - Collect BOTH positions AND reasoning (not just ratings) - Clustering should be based on reasoning similarity, not just position proximity - Each cluster's arguments should be steel-manned (strongest possible version) - Visualization should show: cluster sizes, key arguments, fault lines between clusters - Identify whether disagreements are empirical, value-based, or definitional ## Minimum Yield - Disagreement clusters (identified clusters with characterization) - Core arguments per cluster (core arguments per cluster, steel-manned) - Visualization (disagreement map showing topology and fault lines)