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From skills-for-humanity
Applies outsider perspectives from unrelated fields to surface hidden assumptions in problem framing. Useful when stuck on a problem or suspecting domain blindness.
How this skill is triggered — by the user, by Claude, or both
Slash command
/skills-for-humanity:s4h-analogy-perspective-shiftingThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Domain expertise creates assumption blindness. The more you know about a field, the more
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.
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:
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.
Step 5: Find Cross-Field Patterns What do multiple outsiders notice independently? When different fields converge on the same observation, that observation has a strong claim to being real — it's visible from multiple angles, not an artefact of one framing.
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:
Proceed based on their selection. If the user reframes, incorporate the correction before running any analysis.
Problem (insider framing):
[Current description]
Field perspectives:
| Field | Core instincts / tools | What they notice | What they'd try first |
|---|---|---|---|
Cross-field patterns (what multiple outsiders see):
[Observations that appear across more than one field]
Most useful foreign insight:
[The single observation or approach from outside that would most change how you work the problem — and why it's been invisible from inside]
The exercise fails if the "outside" perspectives are just your own reasoning relabelled. Each field's observations should surprise you. If they don't, you haven't actually left your frame — you've just dressed it in different vocabulary.
After delivering this output, use AskUserQuestion to offer the next move:
/s4h-communication-audience-modeling — Model the audience through each shifted perspective/s4h-creativity-alternatives — Generate alternatives from each perspective/s4h-emotional-motivation-mapping — Map motivations visible from each perspectivenpx claudepluginhub human-avatar/skills-for-humanityImports solutions from unrelated domains by finding structural similarities between your problem and solved problems elsewhere. Useful for creative problem-solving and cross-domain innovation.
Applies cross-domain analogies, first-principles deconstruction, and divergent thinking to overcome creative bottlenecks in problem-solving.
Facilitates structured brainstorming for problem exploration, design decisions, approach comparisons, and trade-off evaluations using 8 LLM bias-counteracting methods with parallel subagent deep dives.