# Q Educator

> Develop course content for university teaching via interview-driven workflow. Use for course planning, lecture prep, assignment design, or student communication.

- Skill: `tyrealq/q-educator` (Agent Skill, multi-file: 14 files)
- Install (CLI): `npx skillmds@latest add tyrealq/q-educator`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tyrealq/q-educator/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- Author: tyrealq (https://skillmd.com/u/tyrealq)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/tyrealq/q-educator

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# Q-Educator

Produce course materials for graduate-level, projects-first courses through an interview-driven process that prioritizes student judgment, transparent reasoning, and domain-specific analogies.

## References

- **references/teaching_philosophy.md** — six governing principles
- **references/interview_protocol.md** — six-question interview sequence
- **references/lecture_template.md** — lecture outline structure and design rules
- **references/demo_template.md** — demo outline structure and design rules
- **references/email_guidelines.md** — follow-up email style rules
- **references/assignment_template.md** — scaffolded assignment prompt structure
- **references/feedback_template.md** — per-group feedback structure and design rules
- **references/key_phrases.md** — philosophy catchphrases for natural use in content
- **references/lecture_example.md** — example lecture outline with domain-specific analogies
- **references/demo_example.md** — example demo outline with pipeline walkthrough
- **references/email_example.md** — example follow-up email in conversational style
- **references/assignment_example.md** — example assignment prompt with full scaffold
- **references/feedback_example.md** — example per-group feedback document

## Core Principles

- Projects-first: students learn by executing analytical workflows, not absorbing lectures
- Judgment over polish: develop scholarly judgment, not polished AI output
- Instructor as arbiter: exemplars and diagnostic feedback, not content transmission
- Repeat-exposure transfer: same analytic logic across projects in different domains
- Transparent reasoning: justify choices, acknowledge tradeoffs, document decisions
- Domain-specific analogies: always from the course's subject area, never generic tech

## Workflow

**Step 1 (Interview):** Conduct six-question interview per references/interview_protocol.md. Only begin content generation after the interview is complete.

**Step 2 (Content Pipeline):** Produce deliverables in this order, pausing for instructor review after each:

| Deliverable | Template | Example |
|-------------|----------|---------|
| Lecture Outline | references/lecture_template.md | references/lecture_example.md |
| Demo Outline | references/demo_template.md | references/demo_example.md |
| Follow-Up Email | references/email_guidelines.md | references/email_example.md |

**Step 3 (Assessment, as needed):**

| Deliverable | Template | Example |
|-------------|----------|---------|
| Assignment Prompt | references/assignment_template.md | references/assignment_example.md |
| Per-Group Feedback | references/feedback_template.md | references/feedback_example.md |

## Scope

**Include:** Lecture outlines, demo outlines, follow-up emails, assignment prompts, per-group feedback for graduate-level projects-first courses.

## Checklist

- [ ] Interview completed before drafting (references/interview_protocol.md)
- [ ] Teaching philosophy principles reflected in content (references/teaching_philosophy.md)
- [ ] Domain-specific analogies used throughout (never generic tech metaphors)
- [ ] Each deliverable reviewed by instructor before proceeding to next
- [ ] Key phrases appear naturally where appropriate (references/key_phrases.md)
- [ ] Deliverable follows its template structure and design rules