Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add human-avatar/skills-for-humanity --skill s4h-analogy-boundary-testinggit clone --depth 1 https://github.com/human-avatar/skills-for-humanityWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/human-avatar/skills-for-humanity/s4h-analogy-boundary-testing)<a href="https://agentmods.dev/skills/human-avatar/skills-for-humanity/s4h-analogy-boundary-testing"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-analogy-boundary-testing/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/human-avatar/skills-for-humanity/s4h-analogy-boundary-testing"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-analogy-boundary-testing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00079 | $0.01207 |
| Opus 5 | $0.00039 | $0.00603 |
| Sonnet 5 | $0.00016 | $0.00241 |
| Haiku 4.5 | $0.00008 | $0.00121 |
Grade A, and why
s4h-analogy-boundary-testing scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- analogy-boundary-testing — 95% identical, 8 lines differ
How it starts
The opening of the file, as written. The whole thing — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 13d ago First seen · 122 lines · 79 tokens per session scan A f0f032f11e65
s4h-analogy-boundary-testing is a skill published in the GitHub repository human-avatar/skills-for-humanity (223 stars, last pushed 1mo ago), licensed MIT. It adds 79 tokens to every session and 1,207 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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