Study Star Goal Planner

Problem

Students often juggle courses, exams, certifications, internships, and career goals, but it is hard to separate what is a dated task, what is only an advisory note, which goals should be decomposed first, and which stage matters most. This skill is built for study planning: it connects pain points, uploaded materials, goals, tasks, memos, and reports into a trackable structure instead of leaving the plan only in chat history.

How It Works

When triggered, the assistant first confirms the user's role and education stage, then asks about current blockers and invites relevant materials such as timetables, transcripts, retake information, or certification plans. It recommends two to four related goals and generates importable goals, templates, tasks, and memos data. It supports multiple education stages, including university, high school, middle school, and primary school, as well as directions such as civil service exams, graduate school, studying abroad, entrepreneurship, employment, and general development. It also manages task priorities, tags, attachments, due dates, stats, points, and achievements.

Key steps include:
- Task classification: items with clear completion times become tasks; advisory, reminder, or note items become memos.
- Data injection: the generated JSON is encoded with ?data= or ?zdata= based on size, then rendered through the unified injection page for review and import.
- Reports and reminders: after adoption, users can generate AI study reports and configure daily task scans, multi-channel pushes, and periodic report subscriptions.

Boundaries

It is best for students who need structured planning, ongoing feedback, and periodic review, not for casual note-taking or entertainment-only check-ins. If the imported payload is too large, the skill suggests trimming or batching the plan; if the model cannot inspect images, users should provide text descriptions of timetables, transcripts, and other materials.