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S4h Analogy Boundary Testing

Organization
human-avatar
s4h-analogy-boundary-testing

Finds where an analogy breaks down before it's relied upon. Analogies fail silently — the damage happens when decisions are made on a mapping that doesn't hold in the relevant dimension. Triggers: 'stress-test this analogy', 'where does this comparison break', 'does this really apply', 'test the metaphor', 'where is the analogy wrong'.

Overview

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

Use it in TypingMind

Enable S4h Analogy Boundary Testing 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 Boundary Testing 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 Boundary Testing 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 Boundary Testing

Analogies are tools, not truths. The danger is not using an analogy — it is using one past its boundary. Analogies fail silently: the flaw is invisible until a decision has been made that depended on the part that didn't hold. This skill finds the boundary before that happens.


Your Process

Step 1: State the Analogy Write it explicitly: "X is like Y." Name the analogy being tested, the domain it comes from, and the claim being made on the basis of it.

Framing check: Confirm the specific analogy before continuing. State what you've identified — the source domain, target domain, and the claim being made — in one sentence, then use AskUserQuestion:

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

Step 2: List Similarities What does the analogy capture correctly? List every genuine parallel — the aspects where the structural correspondence is real. This is not validation; it's establishing what the analogy is good for before finding what it isn't.

Step 3: List Differences Every meaningful divergence between X and Y is a potential failure point. List them systematically: different actors, different dynamics, different constraints, different feedback mechanisms, different scales, different reversibility. Be thorough — incomplete difference-listing is the most common failure mode here.

Step 4: Test Each Difference Against the Decision Before narrowing: Show the complete list of differences from Step 3 to the user first. Use AskUserQuestion:

  • Question: "I've identified [N] differences. Before I filter to those relevant to your decision, are there any you'd flag as especially important, or any I've missed?"
  • Header: "Prioritise"
  • Options:
    • Proceed with your selection — the set looks right
    • Flag one — user will name a specific difference to include
    • Add a missing one — user will describe it

For each decision or conclusion being made on the basis of this analogy: which differences are relevant to that specific decision? A difference that doesn't affect the conclusion is harmless. A difference that does affect it invalidates the reasoning.

Step 5: Does the Conclusion Still Hold? For each relevant difference identified in Step 4: if this difference is real, does the conclusion derived from the analogy still follow? If not, the analogy cannot support that conclusion.

Step 6: State Safe Scope Where can this analogy be validly relied upon? What does it illuminate, and what decisions can it inform? State the boundary clearly.


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
    • Breaking points only — Where the analogy fails, not where it holds
    • 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

Analogy: [X is like Y — domain and claim]

Similarities (what the analogy captures correctly):

[Bulleted list]

Differences (potential failure points):

[Bulleted list — be thorough]

Relevant differences for this decision:

DifferenceRelevant to decision?Effect on conclusion

Conclusion validity:

[Does the analogy support the conclusion? Yes / Partially / No — with reason]

Safe scope — what this analogy validly applies to:

[Bounded statement]


Notes

The most useful output is the safe scope statement — a positive claim about where the analogy is reliable. Discarding an analogy entirely because it has limits wastes the genuine insight it contains.


What's Next

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

  • Question: "Analogy boundaries tested. What's next?"
  • Header: "Next"
  • Options:
    • /s4h-analogy-domain-transfer — Now that boundaries are clear, execute the transfer carefully
    • /s4h-logic-check — Check whether conclusions crossed a boundary they shouldn't have
    • /s4h-constraint-hardness-testing — Are the boundary differences hard constraints or soft?
    • Done — Wrap up and synthesise what we have so far

Frequently asked questions

What does the S4h Analogy Boundary Testing AI skill do?

Finds where an analogy breaks down before it's relied upon. Analogies fail silently — the damage happens when decisions are made on a mapping that doesn't hold in the relevant dimension. Triggers: 'stress-test this analogy', 'where does this comparison break', 'does this really apply', 'test the metaphor', 'where is the analogy wrong'.

Why use S4h Analogy Boundary Testing on TypingMind?

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

Which AI models can use S4h Analogy Boundary Testing?

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 Boundary Testing?

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

Is the S4h Analogy Boundary Testing 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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