abstract-structure
Remove domain surface details to expose transferable relational/mechanistic structure at a chosen abstraction level.
Define the structured input spaces for conceptual blending: entities, relations, goals, constraints, and salient dynamics in each source.
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill construct-input-spaces --agent claude-codeHow it fires
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Define the structured input spaces for conceptual blending: entities, relations, goals, constraints, and salient dynamics in each source.
name: construct-input-spaces description: "Define the structured input spaces for conceptual blending: entities, relations, goals, constraints, and salient dynamics in each source."
Define the explicit source spaces used by conceptual blending.
required: [source_domains, blend_goal] optional: [entities, relations, constraints, salient_dynamics] constraints: [each source needs entities, relations, goals, and constraints or an explicit unknown]
1. Partition each source domain into entities, relations, goals, constraints, and dynamics. 2. Normalize equivalent roles without erasing source-specific structure. 3. Record the blend goal and the items eligible for projection.
If the input spaces and their correspondences are explicit, consider `extract-generic-space` as the next tactic.
produces: [input_space_set, normalized_roles, projection_candidates] delta_fields: [findings, hypothesis_updates, uncertainties]
Reject an input space that is only a keyword list or that merges incompatible roles before blending.
The complete research orchestration system for AI-native science. What It Does Design Philosophy Architecture (v3.2.2) Quick Start Configuration Roadmap License DARE is not a tool that helps you do research. It is the researcher.
Repo: yogsoth-ai/de-anthropocentric-research-engine
Remove domain surface details to expose transferable relational/mechanistic structure at a chosen abstraction level.
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