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S4h Analogy

Organization
human-avatar
s4h-analogy

Entry point for the analogy toolkit. Routes to the right analogy skill based on your situation. Use when you say 'analogy', 'is this like that', 'find a comparison', 'import a solution from elsewhere', 'fresh eyes on this', 'where does this metaphor break', or want analogical reasoning applied without knowing which specific tool fits.

Overview

Publisherhuman-avatar
Repositoryskills-for-humanity
Skill names4h-analogy
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 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
  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 .claude/skills/s4h-analogy
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable S4h Analogy 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 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 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

Applies analogical reasoning to any problem. Diagnoses what kind of analogy work is needed and applies the right tool.

Which tool fits

You need to...Tool
Find where an existing analogy or metaphor breaks downboundary-testing
Import solutions from a completely different domaindomain-transfer
Approach a problem through a different field's lensperspective-shifting
Test the structural correspondence between two situationsstructure-mapping

Routing Decision

  • Already using an analogy and want to stress-test it → boundary-testing
  • Stuck on a problem, want to find who's solved something like it elsewhere → domain-transfer
  • Need fresh eyes from an expert in a different field → perspective-shifting
  • Want to know if two situations are genuinely structurally similar → structure-mapping
  • Unclear → structure-mapping first (establishing the analogy rigorously), then boundary-testing (finding where it fails)

Framing check: Confirm the specific analogy situation before routing. State what you've identified — the analogy or problem being examined and the goal (stress-test, import solutions, shift perspective, or validate structure) — in one sentence, then use AskUserQuestion:

  • Question: "I'm reading this as: [your one-sentence framing of the analogy situation and goal]. 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

Confirm Direction

After diagnosing which tool fits, use the AskUserQuestion tool to confirm direction. Construct the question dynamically to include your diagnosis:

  • Question: "My read: [diagnosed tool] — [one sentence on why it fits]. How would you like to proceed?"
  • Header: "Direction"
  • Options:
    • Yes, run that tool — Execute the diagnosed skill immediately using the context already provided
    • Show all options — List every skill in this category with one-line descriptions
    • Quick version — Run a lighter-weight alternative if one exists for this situation
    • Re-diagnose — Revisit the situation description for a different read

Proceed based on their selection.


Boundary Testing

Finds where an analogy breaks down before it's relied upon.

Name the analogy explicitly: A is like B because [shared properties]. Now systematically test each dimension of the comparison. For each shared property: does it actually hold? What differences exist? Find the dimension where the analogy fails in a way that matters for the decision at hand. Analogies fail silently — the damage happens when decisions are made on a mapping that doesn't hold in the relevant dimension.

Output: Analogy map (what holds, what doesn't), the specific dimension of failure, and what decisions should not rely on this analogy.


Domain Transfer

Imports solutions from unrelated domains by finding structural similarities.

Name the core structure of the problem: what kind of challenge is this underneath the surface details? Search for domains that have solved structurally similar problems — often in completely unrelated fields (biology for network design, military strategy for resource constraints, jazz for improvisational systems). For each candidate domain: what was the solution? How does it map back?

Output: Core problem structure, candidate domains with solved analogues, and specific solutions mapped back to the original problem.


Perspective Shifting

Approaches a problem through a completely different field's lens.

Name the home domain — the expertise lens being applied by default. Now select 2-3 radically different fields: what would a [biologist / game designer / urban planner / etc.] see when looking at this? Apply each lens genuinely, not superficially. The goal is to surface assumptions invisible from inside the home domain.

Output: Per-lens analysis — what each field notices, what assumptions it challenges, what solutions it suggests that the home domain wouldn't.


Structure Mapping

Identifies the deep structural correspondence between two situations.

List the elements of situation A. List the elements of situation B. For each element in A: does a corresponding element exist in B? Are the relationships between elements preserved? Test whether the mapping is genuine isomorphism or just surface similarity. Surface similarity fails on deeper structure; genuine isomorphism predicts behavior accurately.

Output: Element-by-element correspondence table, relationship preservation check, verdict on whether the analogy is structurally valid, and what the mapping predicts.

Frequently asked questions

What does the S4h Analogy AI skill do?

Entry point for the analogy toolkit. Routes to the right analogy skill based on your situation. Use when you say 'analogy', 'is this like that', 'find a comparison', 'import a solution from elsewhere', 'fresh eyes on this', 'where does this metaphor break', or want analogical reasoning applied without knowing which specific tool fits.

Why use S4h Analogy on TypingMind?

Because you install it once and use it with any model. S4h Analogy 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 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. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use S4h Analogy?

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?

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

Is the S4h Analogy 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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