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Help a user learn a topic through adaptive tutoring, lesson planning, practice, retrieval checks, explanations, study guides, or exercises. Use when the user asks to learn, understand, practice, drill, review, study, or be tutored on something.
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
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。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/phoroth/AGENTIC --skill learnコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか？SkillsMP が現在取得できるファイルをダウンロードできます。
Development skill from AGENTIC (https://github.com/phoroth/AGENTIC)
Development skill from AGENTIC (https://github.com/phoroth/AGENTIC)
Generate clean, human-sounding, SEO-optimized WordPress blog posts with optional Yoast metadata, JSON-LD schema markup, and image SEO planning. Supports modular batch output.
SOC 職業分類に基づく
SKILL.md を表示中
| name | learn |
| description | Help a user learn a topic through adaptive tutoring, lesson planning, practice, retrieval checks, explanations, study guides, or exercises. Use when the user asks to learn, understand, practice, drill, review, study, or be tutored on something. |
| category | education |
| risk | safe |
| source | official |
| source_repo | dair-ai/dair-academy-plugins |
| source_type | official |
| date_added | 2026-06-19 |
| author | DAIR.AI |
| license | MIT |
| license_source | https://github.com/dair-ai/dair-academy-plugins/blob/main/README.md#license |
| tags | ["dair-academy","ai","workflow"] |
| tools | ["claude-code","codex-cli","cursor"] |
Use when this workflow matches the user request: Help a user learn a topic through adaptive tutoring, lesson planning, practice, retrieval checks, explanations, study guides, or exercises. Use when the user asks to learn, understand, practice, drill, review, study, or be tutored on something.
_Source: dair-ai/dair-academy-plugins (MIT)._Use this skill when the user wants to learn a topic or improve a skill. The output should fit the user's request and the host agent's enprojectnment. Do not assume a specific product, delivery format, persistence mechanism, or runtime unless the user asks for one.
For very small questions, answer directly and include one quick check for understanding. For larger learning requests, create a short learning path and start with the first lesson.
Before building a full plan, infer what you can from the user's prompt. Ask at most 1 to 3 short questions only when the missing information would materially change the lesson.
Useful diagnostic dimensions:
If the user wants to begin immediately, make a reasonable assumption and state it briefly.
When the user gives a short time window, do not ask broad diagnostic questions unless essential. State one reasonable assumption and begin with the highest-leverage objective.
Keep the learner in the right difficulty band:
Teach one useful concept at a time. Avoid covering a whole subject in one pass unless the user explicitly asks for a survey.
Use active learning:
Make feedback specific. Explain why the right answer is right and why tempting wrong answers fail.
Choose the lightest format that satisfies the request:
Do not force every learning task into an app, web page, persistent hub, or local file set.
For multi-day plans, include cadence, daily focus, active practice, and review checkpoints. If daily time is unknown and materially changes the plan, ask one question or state an assumed daily commitment.
A strong lesson usually includes:
Keep explanations concise. Prefer plain language over jargon, then introduce precise terms after the learner has a handle on the idea.
Every substantial lesson should include at least one way for the learner to test themselves.
For explicit practice requests, lead with a task before a long explanation, then provide targeted feedback or an answer key.
Good checks include:
For multiple-choice questions, make only one answer clearly correct unless the question explicitly asks for multiple answers.
For programming topics, avoid pretending to execute arbitrary code unless the enprojectnment actually runs it. Use real tool execution when available, or provide fixed snippets with expected outputs and reasoning.
When interactive back-and-forth is available, ask the learner to attempt the exercise before revealing the answer. For self-contained responses, include the answer key after the task.
Use the learner's answers and mistakes to adjust:
When continuing from earlier work, preserve useful context from existing notes, files, chat history, or user-provided progress. Do not assume a specific persistence mechanism.
Before finishing, check that: