大学选课排课助手
AI 大学选课排课助手：输入学校、专业、年级和你的目标，一次执行产出三份结果——课程目录深度分析、按目标定制的选课推荐（每门附理由与风险提示）、带 🔴必抢/🟠推荐/🟢备选 三色优先级的学期排课方案。支持一次运行批量处理多所学校 / 多名学生（Excel 工作簿，一行一个）。 【重要：数据来源说明】 本 SkillBot 是纯文本处理管道，本身不联网，需要先提供课程目录文本才能产出有价值的内容： · 推荐用法——让任何 AI Agent（WorkBuddy / Codex / Claude Code 等）先联网查好你学校官网的培养方案/课程手册，整理成文本交给你（或直接写入工作簿），再提交运行。Agent 负责「找数据」，本管道负责「批量、稳定、格式统一地处理数据」。 · 手动用法——自己把学校官网培养方案文本复制粘贴到工作簿的 Course Catalog 列（或上传 PDF/TXT 目录文件）。 目录文本越完整，输出越精准；请勿留空空跑——空目录只会得到占位输出，浪费费用。 【输入】 每行一个学生：学校名称、教育体系、专业、年级、规划模式（AI 推荐 / 自己已选课）、学习目标与时间偏好、课程目录文本、学期信息（起止日期与教学周数）。 【输出】 1. 目录分析：学分制度、毕业要求、必修课与先修链拆解 2. 选课推荐：按目标推荐课程组合，附推荐理由与给分/难度提示；查不到的信息明确标注，不编造 3. 排课方案：学期课表 + 抢课三色优先级标记 【进阶：配套免费本地 Skill】 在 GitHub（ez-hq/uni-course-scheduler）可免费安装配套 Skill「uni-course-scheduler」：本地 Agent 自动完成官网目录采集、交互式需求收集，并在云端结果返回后生成 6-sheet Excel 课表与 .ics 日历文件。本地功能完全免费，仅云端执行按次计费。 费用：每次执行 ¥0.5 + 平台模型成本（按 token 实结，通常远低于 ¥0.2）。每次执行前显示预估费用，你确认后才会扣费。
Inputs and final outputs
| Input field | Type | Requirement | Description |
|---|---|---|---|
| University Name | Text | Required | Full name of the university (e.g., University of Melbourne, Tsinghua University) |
| Education System | Options | Required | The credit system framework used by the university's country/regionAllowed values: US Credits, ECTS, Australian Credit Points, UK Credits (CATS), Canadian Credits, Singapore Modular Credits, Hong Kong Credits, Chinese Credits, Other |
| Major / Field of Study | Text | Required | The student's major or field of study |
| Year Level | Options | Required | Current year of studyAllowed values: Year 1, Year 2, Year 3, Year 4, Master |
| Planning Mode | Options | Required | Whether AI should recommend courses or the student has already chosen coursesAllowed values: ai_recommend, user_decided |
| Student Goals & Schedule Preferences | Text | Required | Academic goals (GPA/employment/interest/grad-school/easy) and schedule preferences (no-8am/three-day-concentrated/evenly-distributed) |
| Already Chosen Courses (JSON) | Text | Optional | If planning_mode is user_decided, provide the list of chosen course codes in JSON format. Leave empty for ai_recommend mode. |
| Course Catalog File | Text reference | Optional | Paste the university course catalog / handbook text here. The pipeline has NO web access — this text is its only data source. |
| Semester Information | Text | Required | Semester code, start date, end date, and number of teaching weeks |
| Decision Intelligence Report | Options | Optional | Optional advisor-style decision report: why these courses, why this schedule, trade-offs, risks and adjustment suggestions. Set to 'off' to skip (identical output to the standard pipeline).Allowed values: on, off |
No output details
The creator has not yet provided output content or execution result details for this Skill.
Instructions
- Provide university name, education system, major, year level, planning mode, student goals and schedule preferences. IMPORTANT: this pipeline has NO web access — paste the official course catalog / handbook text into the Course Catalog field (collect it yourself or let an AI agent collect it for you). Empty catalog produces an empty analysis. Include semester info (dates, teaching weeks).
Examples
- Already Chosen Courses (JSON)
- ['BIO201', 'CHEM210', 'MATH202', 'PHYS201']
- Education System
- Australian Credit Points
- Major / Field of Study
- Computer Science
- Planning Mode
- ai_recommend
- Semester Information
- 2026-S1, starts 2026-02-24, ends 2026-06-05, 12 teaching weeks
- Student Goals & Schedule Preferences
- Goals: GPA. Preferences: no 8am, lunch break 12-1pm, evenly distributed across the week.
- University Name
- University of Melbourne
- Year Level
- Year 1
- Already Chosen Courses (JSON)
- ['BIO201', 'CHEM210', 'MATH202', 'PHYS201']
- Education System
- Chinese Credits
- Major / Field of Study
- Biomedical Science
- Planning Mode
- user_decided
- Semester Information
- 2026-Spring, starts 2026-02-17, ends 2026-06-20, 16 teaching weeks
- Student Goals & Schedule Preferences
- Goals: grad-school. Preferences: three-day concentrated.
- University Name
- Tsinghua University
- Year Level
- Year 2
Trust and update history
| Date | Version |
|---|---|
| Aug 11, 2026 | v11 |
| Aug 10, 2026 | v10 |
| Aug 10, 2026 | v9 |
| Aug 7, 2026 | v8 |
| Aug 7, 2026 | v7 |
| Aug 6, 2026 | v5 |
| Aug 5, 2026 | v4 |
| Aug 5, 2026 | v3 |
| Aug 5, 2026 | v2 |
| Aug 4, 2026 | v1 |
| Aug 4, 2026 | v1 |