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/analogical-discovery

Abstract relational structure from source domains, map it to the target, validate depth, and instantiate transferable mechanisms.

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de-anthropocentric-research-engine
499200 skills
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$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill analogical-discovery --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/analogical-discovery

Context preview

The summary Claude sees to decide when to auto-load this skill.

Abstract relational structure from source domains, map it to the target, validate depth, and instantiate transferable mechanisms.

SKILL.md

analogical-discovery.SKILL.md
name: analogical-discovery
description: "Abstract relational structure from source domains, map it to the target, validate depth, and instantiate transferable mechanisms."

analogical-discovery

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 in `scripts/refactory_source.json`.

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.

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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.

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Python
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Apache-2.0
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53m ago
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7mo ago
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Repo: yogsoth-ai/de-anthropocentric-research-engine