Analogical Discovery

Organization

yogsoth-ai

analogical-discovery

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

Overview

Publisher	yogsoth-ai
Repository	`de-anthropocentric-research-engine`
Skill name	`analogical-discovery`
Stars	505
Forks	43
Bundled files	Instructions only
License	Apache-2.0
Links	Skill source Repository

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by yogsoth-ai on GitHub. Read the source before you install it.

Installation

Install the Analogical Discovery AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/yogsoth-ai/de-anthropocentric-research-engine/tree/main/skills/analogical-discovery
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/yogsoth-ai/de-anthropocentric-research-engine/tree/main/skills/analogical-discovery

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engine.git /tmp/de-anthropocentric-research-engine
mkdir -p .claude/skills
cp -r /tmp/de-anthropocentric-research-engine/skills/analogical-discovery .claude/skills/analogical-discovery

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Analogical Discovery in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Analogical Discovery on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Analogical Discovery is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

analogical-discovery

Purpose

Transfer a validated relational structure from a source domain into a target research problem.

Input contract

yaml
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-structure to abstract the relational structure.
You MUST load skill map-analogy to map source relations to the target.
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

yaml
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.

Frequently asked questions

What does the Analogical Discovery AI skill do?

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

Why use Analogical Discovery on TypingMind?

Because you install it once and use it with any model. Analogical Discovery is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Analogical Discovery in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/yogsoth-ai/de-anthropocentric-research-engine/tree/main/skills/analogical-discovery. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Analogical Discovery?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Analogical Discovery?

As many as you like. As long as a model supports skills, you can use Analogical Discovery with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Analogical Discovery AI skill free?

Yes. It is published on GitHub by yogsoth-ai under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

analogical-discovery

Purpose

Input contract

Execution protocol

Output contract

Thresholds and quality gates

Failure and counterexamples

Provenance map

Preserved source criteria ledger

Context checkpoint / Delta notes

Frequently asked questions

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