S4h Analogy Domain Transfer logo

S4h Analogy Domain Transfer

Organization
human-avatar
s4h-analogy-domain-transfer

Imports solutions from unrelated domains by finding structural similarities between your problem and solved problems elsewhere. Triggers: 'cross-domain analogy', 'what solves this elsewhere', 'find a parallel problem', 'look outside this field', 'borrow a solution'.

Overview

Publisherhuman-avatar
Repositoryskills-for-humanity
Skill names4h-analogy-domain-transfer
Stars
234
Forks
24
Bundled files
Instructions only
LicenseMIT
Links
  • 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 human-avatar on GitHub. Read the source before you install it.

Installation

Install the S4h Analogy Domain Transfer AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

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.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/human-avatar/skills-for-humanity/tree/main/skills/s4h-analogy-domain-transfer
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

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/human-avatar/skills-for-humanity.git /tmp/skills-for-humanity
mkdir -p .claude/skills
cp -r /tmp/skills-for-humanity/skills/s4h-analogy-domain-transfer .claude/skills/s4h-analogy-domain-transfer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable S4h Analogy Domain Transfer 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 S4h Analogy Domain Transfer 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 S4h Analogy Domain Transfer 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.

Analogy Domain Transfer

Your field has blind spots that your field created. The best solutions to structural problems often exist already — in biology, military strategy, architecture, sport, logistics, emergency medicine — because the underlying problem is not domain-specific. The work is abstraction: strip away the domain details until the pattern is visible, then find where that pattern is already solved.


Your Process

Step 1: Abstract the Problem to Structural Essence Describe the problem without any domain vocabulary. What is actually happening? What are the actors, their relationships, the failure mode, the goal? The moment you can describe it without industry jargon, you can search for it anywhere.

Framing check: Confirm the specific problem being transferred before continuing. State what you've identified — the actual challenge, its actors, and the failure mode or goal — in one sentence, then use AskUserQuestion:

  • Question: "I'm reading this as: [your one-sentence framing of the problem stripped of domain vocabulary]. Is that right?"
  • Header: "Framing"
  • Options:
    • Yes — proceed — framing is correct
    • Adjust — one element is off; user will correct it before you continue
    • Reframe — different situation than read; incorporate the correction before proceeding

Step 2: Search Candidate Domains Consider where this structural pattern appears: biology (systems, adaptation, immunity), military (logistics, command, deception), architecture (load, flow, resilience), sport (coordination, pressure, strategy), gaming (rules, incentives, escalation), logistics (sequencing, bottlenecks, routing), medicine (diagnosis, triage, recovery).

Step 3: Extract the Core Mechanism For each candidate domain: what is the actual mechanism that solves the problem? Not the surface story — the operational logic. How does it work, step by step?

Step 4: Map Mechanism Back Translate the mechanism into your problem. What plays what role? What would be the equivalent of each element? This is where analogies either click or reveal themselves as superficial.

Step 5: Test Where the Analogy Holds and Breaks Every analogy breaks somewhere. Where does the mechanism still work when mapped? Where does it stop working? The break points tell you where to be careful and where the insight ends.


Human Check-in

Before proceeding, use the AskUserQuestion tool. State your interpretation of the situation in 1–2 sentences — what is being analyzed and what the core question is — then ask:

  • Question: "My read: [your 1–2 sentence interpretation]. How do you want to proceed?"
  • Header: "Scope"
  • Options:
    • Full analysis — Complete all steps, reasoning shown throughout
    • Key findings only — Bottom-line output, skip step-by-step detail
    • One domain transfer — The single most structurally similar domain, fully developed
    • Reframe — The read is off; correct it and the analysis will follow the corrected framing

Proceed based on their selection. If the user reframes, incorporate the correction before running any analysis.

Output Format

Problem (abstracted — no domain vocabulary):

[Structural description]

Candidate domains and mechanisms:

DomainAnalogous problemCore mechanismStrength of match

Best mapping:

  • Domain element → Your problem element (for each key pair)

Where the analogy holds:

[Specific aspects]

Where the analogy breaks:

[Specific aspects — don't skip this]

Insight to import:

[The actionable mechanism translated back into your problem]


Notes

A weak abstraction in Step 1 produces weak domain matches. If the domains found feel obvious or already-familiar, the abstraction still contains domain assumptions — strip further.


What's Next

After delivering this output, use AskUserQuestion to offer the next move:

  • Question: "Domain transfer complete. What's next?"
  • Header: "Next"
  • Options:
    • /s4h-logic-check — Validate that the transfer holds and conclusions follow
    • /s4h-analogy-boundary-testing — Test where the analogy breaks down
    • /s4h-creativity-alternatives — Use the transferred ideas to generate new alternatives
    • Done — Wrap up and synthesise what we have so far

Frequently asked questions

What does the S4h Analogy Domain Transfer AI skill do?

Imports solutions from unrelated domains by finding structural similarities between your problem and solved problems elsewhere. Triggers: 'cross-domain analogy', 'what solves this elsewhere', 'find a parallel problem', 'look outside this field', 'borrow a solution'.

Why use S4h Analogy Domain Transfer on TypingMind?

Because you install it once and use it with any model. S4h Analogy Domain Transfer 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 S4h Analogy Domain Transfer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/human-avatar/skills-for-humanity/tree/main/skills/s4h-analogy-domain-transfer. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use S4h Analogy Domain Transfer?

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 S4h Analogy Domain Transfer?

As many as you like. As long as a model supports skills, you can use S4h Analogy Domain Transfer with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the S4h Analogy Domain Transfer AI skill free?

Yes. It is published on GitHub by human-avatar under the MIT 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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