Cross-Domain Translator

SkillAI & models

Lets your agent reframe a problem or workflow from one professional field so experts in another field understand it.

Use Cross-Domain Translator in Claude, ChatGPT or Ahel Desktop

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Then ask your AI: use the Cross-Domain Translator skill

Details

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Add Ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Cross-Domain TranslatorStart free
About this skill

Translate a problem, workflow, or project from one professional domain into another while preserving the real structure. Use when someone needs to connect domain expertise with software, AI, product, research, data, policy, operations, or another field. Rebuild actors, objects, states, events, const

What this skill tells your AI

The instructions your AI receives, as published by fairy123456789/human-edge-agent-skills in skills/cross-domain-translator/SKILL.md and read by Ahel’s review.

Translate the structure of the problem, not the nouns.

Workflow

  1. Describe the source-domain problem in ordinary language.
  2. Extract its structural model using references/domain-map.md:
    • actors;
    • objects and data;
    • states;
    • events and actions;
    • decisions;
    • constraints;
    • evidence;
    • success metrics;
    • failure modes.
  3. Identify which parts are domain-specific and which are generic information-processing problems.
  4. Map generic parts into the target domain.
  5. Mark every analogy that could break because of regulation, physical constraints, human incentives, data quality, or different causal mechanisms.
  6. Propose the smallest useful bridge: data model, API, workflow, Agent, experiment, prototype, taxonomy, or research question.
  7. State what new domain knowledge must be learned before implementation.
  8. If the translation is for a resume or interview, explain the transferable structure without pretending the domains are identical.

Output

Return:

  • source-domain problem;
  • structural map;
  • target-domain translation;
  • valid transfer points;
  • invalid or risky analogies;
  • smallest bridge artifact;
  • missing knowledge.

A good translation lets a domain expert recognize their problem and a technical expert see something they can build.

Signals

GitHub stars
98
Last commit
Sep 2026
Advanced
Item type
skill
Key
cross-domain-translator
Source
github.com/fairy123456789/human-edge-agent-skills