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-domain-transfergit 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-domain-transfer)<a href="https://agentmods.dev/skills/human-avatar/skills-for-humanity/s4h-analogy-domain-transfer"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-analogy-domain-transfer/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-domain-transfer"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-analogy-domain-transfer.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 | $0.00057 | $0.01039 |
| Opus 5 | $0.00028 | $0.00519 |
| Sonnet 5 | $0.00011 | $0.00208 |
| Haiku 4.5 | $0.00006 | $0.00104 |
Grade A, and why
s4h-analogy-domain-transfer 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 12d 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.
How it starts
The opening of the file, as written. The whole thing — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analogy Domain Transfer
Your field has blind spots that your field created. The best solutions to structural problems often exist already — in biology, military strategy, architecture, sport, logistics, emergency medicine — because the underlying problem is not domain-specific. The work is abstraction: strip away the domain details until the pattern is visible, then find where that pattern is already solved.
Your Process
Step 1: Abstract the Problem to Structural Essence Describe the problem without any domain vocabulary. What is actually happening? What are the actors, their relationships, the failure mode, the goal? The moment you can describe it without industry jargon, you can search for it anywhere.
Framing check: Confirm the specific problem being transferred before continuing. State what you've identified — the actual challenge, its actors, and the failure mode or goal — in one sentence, then use AskUserQuestion:
- Question: "I'm reading this as: [your one-sentence framing of the problem stripped of domain vocabulary]. 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
Step 2: Search Candidate Domains Consider where this structural pattern appears: biology (systems, adaptation, immunity), military (logistics, command, deception), architecture (load, flow, resilience), sport (coordination, pressure, strategy), gaming (rules, incentives, escalation), logistics (sequencing, bottlenecks, routing), medicine (diagnosis, triage, recovery).
Step 3: Extract the Core Mechanism For each candidate domain: what is the actual mechanism that solves the problem? Not the surface story — the operational logic. How does it work, step by step?
Step 4: Map Mechanism Back Translate the mechanism into your problem. What plays what role? What would be the equivalent of each element? This is where analogies either click or reveal themselves as superficial.
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.
- 12d ago First seen · 111 lines · 57 tokens per session scan A f86befbcd7e2
s4h-analogy-domain-transfer is a skill published in the GitHub repository human-avatar/skills-for-humanity (223 stars, last pushed 1mo ago), licensed MIT. It adds 57 tokens to every session and 1,039 once invoked, about $0.0003 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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