用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt;运行前请先检查来源。
npx skills add https://github.com/benjam3n/reasoningtool --skill met-metaphor-method命令会保持在同一行。复制前请横向滚动并检查完整内容。
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基于 SOC 职业分类
正在显示 SKILL.md
| name | met - Metaphor Method |
| description | A systematic method for using metaphors to: |
| output | {"format":"prose"} |
A systematic method for using metaphors to:
Metaphors are not just decorative language. They reveal structural similarities between domains. By mapping from one domain to another, we can discover relationships that transfer.
What are you trying to understand better? This is the LITERAL domain you want insight into.
Example: "Philosophical conclusions"
Output: target_domain
What is the target domain LIKE? Find a different domain that has structural similarities.
Good source metaphors:
Example: "Conclusions are like movie endings"
Output: source_metaphor
In the source domain, what relationships exist? List them without yet checking if they apply to target.
Example (movie endings):
Output: source_relationships
For each source relationship, what's the equivalent in target? Don't force it - some may not translate.
Example translations:
Output: translated_relationships
For each translated relationship, ask:
Keep relationships that pass testing. Discard or modify those that don't.
Output: tested_relationships
For relationships that survive testing, what's the abstract structure? This is the pattern that applies to BOTH domains.
Example: "Terminal points (endings/conclusions) are evaluated relative to their trajectory (plot/argument) and driving forces (motivations/goals)"
This abstract structure is the real insight.
Output: abstract_structure
Take the abstract structure and find OTHER metaphors that instantiate it. This tests the structure's generality and may reveal new aspects.
Example: Journey metaphor also has terminal point (destination), trajectory (route), and driving forces (reasons for travel).
What new relationships does the journey metaphor reveal?
Output: iterated_insights