AI-powered university course planning assistant — analyze course catalogs, get smart course recommendations, build conflict-free weekly timetables, and export Excel (local + cloud) and ICS calendar files (cloud standard mode only).
uni-course-scheduler is an AI Skill that helps university students plan their semester:
- Catalog analysis — parses a school's official course catalog (program handbook text) into structured course data: codes, names, credits/points, prerequisites, term availability, and timetable slots.
- Course recommendation — recommends courses matched to the student's goals (e.g. GPA-first, ML specialization), flags enrollment priority, and validates prerequisites.
- Weekly schedule — builds a conflict-free weekly timetable from the real timetable, respecting preferences like "no 8am classes" or "keep 12:00–13:00 free".
- Excel export — generates a 6-sheet (local) or 7-sheet (cloud) Excel workbook, including
Weekly Timetable,Degree Planner, and (cloud)Degree Audit & Risk Assessment. - ICS calendar — exports a downloadable
.icsfile for Apple Calendar / Google Calendar (cloud mode).
Supports international credit systems (US / AU / UK / ECTS / CN / HK / SG) and undergraduate and graduate students.
This Skill ships in two execution modes:
The agent itself researches the official course catalog, verifies it, and produces a 6-sheet Excel + a textual weekly overview.
- Free, single-school, single-student, personal use only.
- No batch pipeline, no standardized decision report, no downloadable
.icscalendar, no deep degree audit.
A fixed production-grade cloud pipeline (catalog → recommend → schedule → decision → ICS → degree-audit) processes the catalog text and returns:
- Batch / multi-school / multi-student execution.
- Standardized 8-dimension Decision Report.
- Degree Audit & Risk Assessment sheet (prerequisite chains, credit progress, overload risks).
- Downloadable
.icscalendar (university term dates, weekly recurrence, correct IANA timezone).
Offline-cloud note: Like most hosted LLM pipelines, the cloud executor has no internet access. The local agent (or the user) must first paste the official catalog text. The pipeline never fabricates data — anything not found in the input is reported honestly as missing.
The cloud command is platform-agnostic; the platform is chosen by your payment method:
| Your payment | Platform | Price | Server | Token env |
|---|---|---|---|---|
| China mainland payment (Alipay/WeChat/UnionPay) | Shengsuanyun | ¥0.5/run | loomloom.shengsuanyun.com |
LOOMLOOM_TOKEN_SHENGSUANYUN |
| International credit card (Visa/MC) | CogFoundry | $0.10/run | loomloom.cogfoundry.ai |
LOOMLOOM_TOKEN_COGFOUNDRY |
On first cloud use, the assistant asks which payment you use and configures the matching server. See references/loomloom-setup.md for full setup.
- An AI assistant that supports custom Skills (e.g. WorkBuddy / Claude Code / Codex / Cursor-style agent).
- For cloud mode: a platform account + balance.
- Download the
uni-course-scheduler.ziprelease. - Drag the ZIP into your AI chat and say: "Please install this planning Skill for me."
- The AI agent installs it and you can start.
# 1. Unzip the package
mkdir -p ~/.workbuddy/skills && cd ~/.workbuddy/skills
unzip /path/to/uni-course-scheduler.zip
# 2. Verify the folder structure
ls ~/.workbuddy/skills/uni-course-scheduler # SKILL.md, scripts/, references/# Install the LoomLoom CLI (buyer flow)
# See references/loomloom-setup.md for full instructions
loomloom login # sign in with your key
loomloom market list # verify connectivity- Tell the agent your school, major, year, and goals (e.g. "GPA first, prefer no 8am classes").
- Provide the official catalog text — let the local agent fetch it, or paste it.
- Choose a mode: Local (free) or Cloud (paid).
- Confirm the fee (cloud) and receive: an Excel workbook (
.xlsx) + a downloadable.icscalendar (cloud).
See references/catalog-collection.md for a reusable prompt and quality gates (≥500 chars, ≥5 courses, official source).
uni-course-scheduler/
├── SKILL.md # Main skill instructions (both modes)
├── README.md # This file (English)
├── README.zh-CN.md # Chinese version of this README
├── LICENSE # Personal-use license
├── agents/
│ └── openai.yaml # OpenAI / compatible agent metadata
├── references/
│ ├── catalog-collection.md # How to collect & verify catalogs
│ ├── excel-output-spec.md # Excel workbook schema
│ ├── cloud-output-format.md # Cloud pipeline output format
│ ├── interaction-flow.md # End-to-end user flow
│ ├── local-validation.md # Local output validation rules
│ └── loomloom-setup.md # Cloud setup (dual-platform)
└── scripts/
├── generate_excel.py # Build the Excel workbook from JSON
├── generate_ics.py # Render .ics from cloud ICS data
├── anti_hallucination_check.py # Anti-hallucination validation
├── validate_schedule.py # Schedule conflict validation
├── local_audit.py # Local output audit
└── check_github_downloads.py # Download stats helper
- No fabrication: data not present in the input is labeled
NOT_FOUND/ missing — never invented. - Honest empty: for garbage/empty input returns an honest empty result with guidance, not a hallucination.
- Validation: local scripts run schedule-conflict checks and an anti-hallucination audit on every output.
Personal Use License — No Commercial Redistribution. Free for personal use; commercial redistribution or re-listing on paid marketplaces requires the author's written permission. See LICENSE (Chinese text prevails).
- Author: ez-hq
- Found a bug or have a catalog request? Open an issue in the repository.