About this skill

Problem it addresses

High school physics teaching is not just problem solving. Teachers need to address class performance, student gaps, exam topic mapping, and error analysis; parents need help with college applications and career planning; schools need support for new teachers and AI tool adoption. The skill frames teaching and college guidance as a structured workflow: it turns ambiguous requests into actionable plans with scope, steps, metrics, and risk controls.

How it works

It follows three steps: scenario identification, capability matching, and structured output.
- Scenario identification: determines whether the request involves exam teaching, differentiated tutoring, college applications, competitions, academic planning, teaching research, or AI-assisted instruction, and identifies the user role as a teacher, educator, or parent.
- Capability matching: routes single needs to relevant references/... documents and combines modules for complex cases. For example, improving class results may include lesson flow, error attribution, and measurable targets; application guidance may include school databases, student matching, and tiering strategy.
- Output: produces a Markdown report with decision rationale, execution path, delivery criteria, risk controls, and reusable templates such as tables, checklists, or evaluation frameworks.
Boundary: advice is constrained by the real gaokao context and exam syllabus, emphasizes individual differences, and does not replace official admissions policy advice or guarantee outcomes. It needs key inputs such as score rank, preferences, class baseline, or teaching resources before generating a plan.

Screenshots

Use Cases

A physics teacher needs a gaokao-aligned plan for class weak spots and an error-attribution workflow.
A parent needs a tiered college application strategy based on score rank, location preference, and interests.
A teaching leader needs new-teacher training, differentiated tutoring, and student evaluation checklists.
A teacher wants to integrate AI into lesson prep, Q&A, or grading, including tool selection and prompt templates.

Best For

High school physics teachers who want to optimize lesson flow and build a measurable error-attribution system.
Gaokao students' parents who need college application decisions based on score rank, interests, and location preferences.
Teaching leaders who need new-teacher training, differentiated tutoring evaluation, and team research workflows.
School administrators who want clear AI tool selection and usage boundaries for lesson prep, Q&A, and grading.