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-perspective-shiftinggit 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-perspective-shifting)<a href="https://agentmods.dev/skills/human-avatar/skills-for-humanity/s4h-analogy-perspective-shifting"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-analogy-perspective-shifting/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-perspective-shifting"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-analogy-perspective-shifting.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 | $0.00064 | $0.01040 |
| Opus 5 | $0.00032 | $0.00520 |
| Sonnet 5 | $0.00013 | $0.00208 |
| Haiku 4.5 | $0.00006 | $0.00104 |
Grade A, and why
s4h-analogy-perspective-shifting 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 — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analogy Perspective Shifting
Domain expertise creates assumption blindness. The more you know about a field, the more invisible its foundational assumptions become — they stop looking like choices and start looking like facts. Bringing in genuine outsider perspectives breaks this. Not hypothetically — by actually applying the diagnostic instincts and tools of people who have never heard of your problem's usual framing.
Your Process
Step 1: State the Problem Describe the problem as you currently understand it. This is the insider framing — it will contain the assumptions you're trying to surface.
Framing check: Confirm the specific problem before continuing. State what you've identified — the actual situation being examined and its domain — in one sentence, then use AskUserQuestion:
- Question: "I'm reading this as: [your one-sentence framing of the problem and its domain]. 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: Choose 2-3 Genuinely Different Fields Select fields with fundamentally different training, tools, and instincts. For software problems: film production, archaeology, emergency medicine, urban planning. For organisational problems: ecology, military logistics, theatre direction, structural engineering. Avoid fields that are adjacent — choose fields that would produce different first questions.
Step 3: Build Each Expert's Toolkit For each field: what are their core diagnostic tools and instincts? What do they always check first? What patterns are they trained to spot? What would their first question be when encountering an unknown problem?
Step 4: Apply Each Lens Apply each expert's toolkit to your problem. What do they immediately notice that insiders overlook? What would they try first that you haven't? What would strike them as obviously wrong or unnecessarily complex? Don't moderate the outsider view — let it be naive.
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 · 108 lines · 64 tokens per session scan A 925a552bfb3a
s4h-analogy-perspective-shifting is a skill published in the GitHub repository human-avatar/skills-for-humanity (223 stars, last pushed 1mo ago), licensed MIT. It adds 64 tokens to every session and 1,040 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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