extract-generic-space
SkillDev toolsLets your agent find the shared abstract structure across different ideas when blending concepts.
Available today. Use it from your connected AI after setup.
No other account needed.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the extract-generic-space skill
About this skill
Infer the shared abstract relational structure that is common across conceptual-blend input spaces.
What this skill tells your AI
The instructions your AI receives, as published by yogsoth-ai/de-anthropocentric-research-engine in skills/extract-generic-space/SKILL.md and read by ahel’s review.
Purpose
Infer the shared abstract relational structure common across conceptual-blend input spaces.
Input contract
required: [input_space_set]
optional: [candidate_relations, blend_goal]
constraints: [generic relations must be present in at least two input spaces]
Procedure
- Compare roles and relations across the input spaces.
- Keep only shared structure and mark source-specific attributes.
- Validate the generic space against the blend goal before projection.
If the generic space supports a coherent candidate blend, consider simulate-emergent-properties as the next tactic.
Output contract
produces: [generic_space, shared_relations, excluded_attributes]
delta_fields: [findings, hypothesis_updates, uncertainties]
Quality gates
- Every generic relation has at least two source traces; source-specific structure is not silently generalized.
Failure and counterexamples
Return no generic space when the inputs share only vocabulary or when relation alignment is ambiguous.
Provenance map
conceptual-blending/generic-space: concept (no exact pool entry).
Signals
- GitHub stars
- 501
- Forks
- 41
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Key
extract-generic-space- Source
- github.com/yogsoth-ai/de-anthropocentric-research-engine