S4h Analogy Structure Mapping logo

S4h Analogy Structure Mapping

Organization
human-avatar
s4h-analogy-structure-mapping

Identifies the deep structural correspondence between two situations — genuine isomorphism vs superficial similarity. Triggers: 'does this analogy hold', 'map the structure', 'is this really like that', 'structural similarity', 'test the comparison'.

Overview

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

Use it in TypingMind

Enable S4h Analogy Structure Mapping 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 Structure Mapping 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 Structure Mapping 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 Structure Mapping

Analogies are persuasive but often wrong. The surface similarity that makes a comparison feel apt can obscure structural differences that make it invalid. This skill maps the deep structure of both situations and checks correspondence element by element — distinguishing genuine isomorphism (same structure, different content) from superficial similarity (same surface, different structure).


Your Process

Step 1: State the Two Situations Write Situation A and Situation B clearly. State the analogy as claimed: "A is like B because..."

Framing check: Confirm the specific analogy before continuing. State what you've identified — the two situations being compared and the analogical claim linking them — in one sentence, then use AskUserQuestion:

  • Question: "I'm reading this as: [your one-sentence framing of the two situations and the claimed analogy]. 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 situations or analogy than read; incorporate the correction before proceeding

Step 2: Extract Structure of Each For each situation independently, map: key actors, their relationships, the dynamics (what drives change), the constraints (what limits action), and the goals (what success looks like). Do this for each situation before comparing — comparison before extraction introduces bias.

Step 3: Map Elements A to B For each structural element in A, identify the corresponding element in B. State the mapping explicitly: "In A, X plays the role of Y in B."

Step 4: Classify Each Mapped Pair For each mapped pair: is the correspondence genuine (same structural role, same relationship type, same dynamics) or superficial (same label or surface feature, different structural role)?

Step 5: Find Where the Mapping Breaks Every analogy breaks somewhere — this is not a failure, it is where the analysis becomes useful. Which differences are structurally significant? Which make the analogy unreliable for the specific prediction or decision being made?

Step 6: State Valid Predictions and Invalid Ones Based on where the mapping holds and where it breaks, state what the analogy validly predicts and what it cannot be relied upon to predict.


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
    • Structural differences only — Where the two situations diverge, not where they align
    • 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

Situation A structure: [actors / relationships / dynamics / constraints / goals] Situation B structure: [actors / relationships / dynamics / constraints / goals]

Element mapping:

Element in ACorresponding element in BGenuine / SuperficialReason

Where the mapping breaks:

[Specific structural differences and why they matter]

Valid predictions (analogy holds):

[What can be reliably inferred from B about A]

Invalid predictions (analogy breaks):

[What cannot be inferred — where relying on the analogy would mislead]


Notes

Genuine structural correspondence requires that the relationship between elements matches — not just that individual elements can be paired. Two things can share every node and differ entirely on the edges that connect them.


What's Next

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

  • Question: "Structure mapped. What's next?"
  • Header: "Next"
  • Options:
    • /s4h-logic-check — Validate that the structural mapping holds
    • /s4h-analogy-domain-transfer — Use the structure map to guide a domain transfer
    • /s4h-systems-feedback-mapping — Check if the structure reveals feedback dynamics
    • Done — Wrap up and synthesise what we have so far

Frequently asked questions

What does the S4h Analogy Structure Mapping AI skill do?

Identifies the deep structural correspondence between two situations — genuine isomorphism vs superficial similarity. Triggers: 'does this analogy hold', 'map the structure', 'is this really like that', 'structural similarity', 'test the comparison'.

Why use S4h Analogy Structure Mapping on TypingMind?

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

Which AI models can use S4h Analogy Structure Mapping?

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 Structure Mapping?

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

Is the S4h Analogy Structure Mapping 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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