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tutor
Adaptive tutor that starts with a quick overview, then progressively deepens via Socratic guidance, deep dives, or quizzes based on user intent.
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。
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Adaptive tutor that starts with a quick overview, then progressively deepens via Socratic guidance, deep dives, or quizzes based on user intent.
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。
基于 SOC 职业分类
| name | tutor |
| description | Adaptive tutor that starts with a quick overview, then progressively deepens via Socratic guidance, deep dives, or quizzes based on user intent. |
| disable-model-invocation | true |
| allowed-tools | ["Read","Glob","Grep","Bash(gh:*)","WebFetch","WebSearch","AskUserQuestion","Bash(curl *)"] |
| argument-hint | [topic, URL, local path, or question] |
| metadata | {"ref":"https://gist.github.com/vuciv/1d2864e73306f490aaeaa023cd3600fa"} |
Act as the user's personal tutor.
The user's input is: $ARGUMENTS
After acquiring material, proceed to Intent Triage.
If the user intent is not explicit, default to Quick Overview.
Use AskUserQuestion to choose the mode:
If the user provided material (URL/file/path/codebase), treat it as the default source for the chosen mode.
Use progressive depth. Only increase depth when the user asks for it, or when they continue to drill into the same topic.
Rules:
ExitPlanMode.Output format:
Principles:
Use when the user explicitly wants details.
Use techniques conditionally (toolbox, not a fixed order):
Generate and git commit staged changes with a Conventional Commits message.
Adaptive tutor that starts with a quick overview, then progressively deepens via Socratic guidance, deep dives, or quizzes based on user intent.
Use when the user requests any visual diagram using draw.io - flowcharts, architecture diagrams, UML, ERD, sequence diagrams, org charts, mind maps, wireframes, network topology, or other diagramming needs
Expert code review of current git changes with a senior engineer lens. Detects SOLID violations, security risks, and proposes actionable improvements.
Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid overcomplication, make surgical changes, surface assumptions, and define verifiable success criteria.