analogical-discovery
SkillDev toolsLets your agent solve a research problem by borrowing proven structures from another field and testing the analogy holds.
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 analogical-discovery skill
About this skill
Abstract relational structure from source domains, map it to the target, validate depth, and instantiate transferable mechanisms.
What this skill tells your AI
The instructions your AI receives, as published by yogsoth-ai/de-anthropocentric-research-engine in skills/analogical-discovery/SKILL.md and read by ahel’s review.
Purpose
Transfer a validated relational structure from a source domain into a target research problem.
Input contract
required: [target_problem, source_domain]
optional: [candidate_sources, transfer_constraints]
constraints: [source and target roles must be explicit]
Execution protocol
Do not perform called SOP operations inline; each loaded SOP owns its contract and thresholds.
- You MUST load skill
abstract-structureto abstract the relational structure. - You MUST load skill
map-analogyto map source relations to the target. - You MUST load skill
instantiate-transferto instantiate and test the transferred mechanism. If the analogy should be expanded across a typed combination space, considerexplore-dimensional-space. If biological mechanisms are the relevant source domain, considerbiomimetic-transfer. If several source structures must be composed, considerconceptual-blending. If the claimed mapping requires a formal preservation audit,audit-structural-equivalencemay be the better next tactic. Deviation: skip source search only when a supplied source is structurally specified; never skip mapping or transfer validation.
Output contract
produces: [abstract_structure, structural_mapping, transfer_candidate]
delta_fields: [findings, hypothesis_updates, uncertainties, decisions, open_questions]
Thresholds and quality gates
- B: every transfer records source/target correspondences, unmapped relations, and a depth check; surface similarity alone is insufficient.
Failure and counterexamples
Reject transfers whose causal/relational roles do not map, whose target constraints are violated, or whose claimed mechanism is only lexical resemblance.
Provenance map
creative-ideation/cross-domain-discovery,analogical-transfer,design-by-analogy,functional-analogy,analogy-extraction,bridge-validation: resolved where exact v3 node exists; campaign/strategy labels remain concept provenance.- Status: all six exact names resolved against the v3 source graph.
Preserved source criteria ledger
- Preserve deep structural correspondence and transfer viability; do not collapse to keyword similarity.
Context checkpoint / Delta notes
Append source relations, mapping gaps, transfer assumptions, and validation findings.
Signals
- GitHub stars
- 501
- Forks
- 41
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Key
analogical-discovery- Source
- github.com/yogsoth-ai/de-anthropocentric-research-engine