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Agent skill

S4h Analogy Boundary Testing

by human-avatar in human-avatar/skills-for-humanity

Finds where an analogy breaks down before it's relied upon. An agent skill from human-avatar/skills-for-humanity.

MITAuto-check passedTesting & QA

Install S4h Analogy Boundary Testing

skills CLI
$ npx skills add human-avatar/skills-for-humanity --skill s4h-analogy-boundary-testing -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install human-avatar/skills-for-humanity s4h-analogy-boundary-testing --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/human-avatar/skills-for-humanity.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/s4h-analogy-boundary-testing .claude/skills/s4h-analogy-boundary-testing && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
s4h-analogy-boundary-testing
GitHub stars
231
Token cost
~1.4k tokens
SKILL.md length
687 words
Files
1
Skills in repo
11
Repo updated
First seen
Licence
MIT

At a glance

Finds where an analogy breaks down before it's relied upon. An agent skill from human-avatar/skills-for-humanity.

  • Tasks that involve Load testing
  • SKILL.md covers Your Process, Human Check-in, Output Format and Notes, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

S4h Analogy Boundary Testing is an agent skill from human-avatar/skills-for-humanity. 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'.

Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Testing & QA, covering Load testing. The repository describes itself as: Structured reasoning methodologies from history's most rigorous thinkers, packaged as Claude Code skills. The licence is MIT.

When your agent uses it

  • Tasks that involve Load testing

Example prompts

  • “t hold in the relevant dimension. Triggers:”
  • “where does this comparison break”
  • “does this really apply”
  • “/s4h-analogy-boundary-testing”

What it can do on your machine

Read from SKILL.md and the folder at commit a30df8b. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

S4h Analogy Boundary Testing loads about 1.4k tokens when it runs. Until then it costs about 92 tokens; SKILL.md has 687 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~92
When it runs · the whole SKILL.md, loaded when a task matches
~1.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from human-avatar/skills-for-humanity at commit a30df8b, republished under its MIT licence (© human-avatar). 687 words, ~1,351 tokens.

Download SKILL.mdSave it as .claude/skills/s4h-analogy-boundary-testing/SKILL.md (or your agent's skills folder).
name
s4h-analogy-boundary-testing
description
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'.

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.

Show full SKILL.md (258 more words)Show less

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

© human-avatar, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/s4h-analogy-boundary-testing of human-avatar/skills-for-humanity.

Open the folder on GitHubat commit a30df8b

Compare with similar skills

S4h Analogy Boundary Testing next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

S4h Analogy Boundary Testing compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
S4h Analogy Boundary Testing this skillhuman-avatar/skills-for-humanity231—~1.4kAutomated safety check: PassMIT
Writing Livekit Scenarioslivekit-examples/agent-starter-python2641 repos~2.5kAutomated safety check: PassMIT
Go Testingcxuu/golang-skills1731 repos~1.3kAutomated safety check: PassApache-2.0
Goalcraftgrp06/goalcraft102—~3.8kAutomated safety check: PassMIT
Thinking Partnermattnowdev/thinking-partner206—~4.4kAutomated safety check: PassMIT
Visionkunchenguid/vision331—~2.9kAutomated safety check: PassMIT

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Categories

Questions about S4h Analogy Boundary Testing

What does S4h Analogy Boundary Testing do?

Finds where an analogy breaks down before it's relied upon. An agent skill from human-avatar/skills-for-humanity. S4h Analogy Boundary Testing is an agent skill from human-avatar/skills-for-humanity. Finds where an analogy breaks down before it's relied upon.

When should I use S4h Analogy Boundary Testing?

S4h Analogy Boundary Testing fits situations like: tasks that involve Load testing.

How do I install S4h Analogy Boundary Testing in Claude Code?

Run `npx skills add human-avatar/skills-for-humanity --skill s4h-analogy-boundary-testing -a claude-code`. Or copy the skill folder (skills/s4h-analogy-boundary-testing in human-avatar/skills-for-humanity) into .claude/skills/s4h-analogy-boundary-testing in your project. Claude Code loads it when a task matches its description.

How do I install S4h Analogy Boundary Testing in Codex?

Run `npx skills add human-avatar/skills-for-humanity --skill s4h-analogy-boundary-testing -a codex`. Or copy the skill folder (skills/s4h-analogy-boundary-testing in human-avatar/skills-for-humanity) into .agents/skills/s4h-analogy-boundary-testing in your project. Codex loads it when a task matches its description.

Can I use S4h Analogy Boundary Testing in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add human-avatar/skills-for-humanity --skill s4h-analogy-boundary-testing -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/s4h-analogy-boundary-testing, .gemini/skills/s4h-analogy-boundary-testing, .github/skills/s4h-analogy-boundary-testing and .opencode/skills/s4h-analogy-boundary-testing in your project.

What does S4h Analogy Boundary Testing need to run?

SKILL.md names no scripts, command-line tools or credentials: S4h Analogy Boundary Testing is instructions for the agent only.

Does S4h Analogy Boundary Testing access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is S4h Analogy Boundary Testing safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does S4h Analogy Boundary Testing use?

S4h Analogy Boundary Testing is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does S4h Analogy Boundary Testing use?

About 1.4k tokens (SKILL.md is roughly 5.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to S4h Analogy Boundary Testing?

Skills that share tags, products or a category with S4h Analogy Boundary Testing: Writing Livekit Scenarios (livekit-examples/agent-starter-python, 264 stars), Go Testing (cxuu/golang-skills, 173 stars), Goalcraft (grp06/goalcraft, 102 stars) and Thinking Partner (mattnowdev/thinking-partner, 206 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains S4h Analogy Boundary Testing?

human-avatar (a GitHub organization) maintains it in human-avatar/skills-for-humanity, which has 231 GitHub stars. The repository holds 11 skills in this directory. The repository was last updated on July 15, 2026.

Source: human-avatar/skills-for-humanity on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.