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teach
Explain complex concepts with analogies, visual metaphors, recursive diagnostics, and PhD-level depth using consulting frameworks
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
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Explain complex concepts with analogies, visual metaphors, recursive diagnostics, and PhD-level depth using consulting frameworks
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
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| name | teach |
| description | Explain complex concepts with analogies, visual metaphors, recursive diagnostics, and PhD-level depth using consulting frameworks |
| triggers | ["teach","teach me","deep explain","break down concept"] |
| argument-hint | <topic or concept> [--ko for Korean] |
Explain complex concepts in a detailed report by integrating intuitive analogies, visual metaphors, and recursive diagnostics to ensure clarity and understanding.
Outline the main ideas or concepts within the topic. Start with the big picture or a high-level analogy, then gradually zoom in to subcomponents.
For each key concept, break it down using intuitive analogies to create relatable visual anchors.
Gather quantitative data, statistics, and facts. Relate these back to the analogies.
WebSearch or exa-search tools are available, use them to verify data and find current statistics; otherwise rely on training knowledge and flag uncertain claims with "(approximate)" or "(as of [year])"Embed brief comprehension probes directly into the written report after each major section — these are part of the document, not interactive pauses.
Integrate each key concept with its analogies and supporting details for clarity.
# H1 for the topic, ## H2 for major sections, ### H3 for subsectionsRe-explain the original concept from the top, ensuring mastery of all subcomponents.
Ensure all details are accurate, properly referenced, and clearly presented.
# H1, ## H2, ### H3, etc.--ko to write in Korean instead/teach transformer architecture
/teach how does backpropagation work
/teach explain RLHF in large language models
/teach what is knowledge distillation
Clean AI-generated code slop with a regression-safe, deletion-first workflow and optional reviewer-only mode
Process-first advisor routing for Claude, Codex, or Gemini via `omc ask`, with artifact capture and no raw CLI assembly
Full autonomous execution from idea to working code
Cancel any active OMC mode (autopilot, ralph, ultrawork, ultraqa, swarm, ultrapilot, pipeline, team)
Given a target Android app — an APK path, a running emulator serial, or an installed package id — scope every screen and build a high-fidelity cloned RN/Expo app forked from app-template, filled with original mock content. Runs a 6-stage Acquire→Scope→Scaffold→Build→Parity→Deliver pipeline with 3 hard gates. Triggers: "clone app", "clone this APK", "clone <app> for me", "high-fidelity RN clone", "make a clone of this emulator app", /clone-app.
Configure notification integrations (Telegram, Discord, Slack) via natural language