analogical-discovery

SkillDev tools

Lets 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.

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.

  1. You MUST load skill abstract-structure to abstract the relational structure.
  2. You MUST load skill map-analogy to map source relations to the target.
  3. You MUST load skill instantiate-transfer to instantiate and test the transferred mechanism. If the analogy should be expanded across a typed combination space, consider explore-dimensional-space. If biological mechanisms are the relevant source domain, consider biomimetic-transfer. If several source structures must be composed, consider conceptual-blending. If the claimed mapping requires a formal preservation audit, audit-structural-equivalence may 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