interactive-learner

这段内容是审核时保存的快照。外部来源才是完整且最新的版本。

---
name: interactive-learner
description: "Personal AI tutoring skill that deeply researches any topic, then creates rich, interactive HTML courses with quizzes, simulators, debug challenges, explain-back exercises, real-world missions, and more. Tracks per-concept mastery across sessions with spaced repetition. Use when: (1) the user wants to learn a new topic, (2) the user says 'teach me X' or 'I want to learn X', (3) the user asks for an interactive lesson or course, (4) the user wants to study or review a subject. Works for any topic: technical, conceptual, creative, math, languages."
metadata:
  version: 0.1.0
---

# Interactive Learner

Create deeply researched, engaging, interactive courses on any topic. Lessons open in the browser with a mix of click-based exercises, open-ended challenges, real-world missions, and AI-evaluated responses. Every course is personalized, evidence-based, and a little adventurous.

## Workflow

### New course: Profile → Research → Curriculum → Session → Build → Debrief

#### 1. Profile the student (first time only)

Keep profiling fast and frictionless. The student wants to learn, not fill out forms.

**Rules:**

- **Prefer multiple-choice questions.** They're faster to answer and give you structured data. Use the agent's question tool with concrete options wherever possible.
- **Max 1 open-ended question at a time.** Never dump multiple open questions in one message.
- **Max 3-4 profiling questions total.** Infer the rest from context and conversation.
- **Start teaching quickly.** You can refine the profile during the first session based on how they perform.

**What to gather (in order of priority):**

1. Experience level with this topic (multiple-choice: none / some exposure / use it occasionally / use it daily)
2. Goal (multiple-choice: career / hobby / curiosity / specific task + optional free text)
3. Time per session (multiple-choice: ~10 min / ~20 min / ~30+ min)
4. Background — only if not obvious from context (one open question max, e.g. "What's your day job or main interest?")

Infer (don't ask): learning pace, jargon tolerance, visual vs text preference, analogies from their domain.

See [student-profiling.md](references/student-profiling.md) for the full profiling framework.

Initialize progress:

> All scripts use `uv run`. If `uv` is not available, use `python3` instead.
>
> **Path note:** `.agents/skills/` and `.claude/skills/` are symlinked — both paths reference the same location. Examples below use `.agents/`.

```bash
uv run .agents/skills/interactive-learner/scripts/progress.py init <course> <name>
```

#### 2. Research the topic thoroughly

**This is critical. Do not skip or rush this step.** Before designing any curriculum, become an expert on the subject.

**Deep research protocol:**

1. **Search for authoritative, recent sources** — prioritize official documentation, peer-reviewed content, respected practitioners, and recent (2024-2026) material
2. **Find the best learning resources that already exist** — outstanding blog posts, interactive tutorials, YouTube channels, open-source tools, practice sandboxes, visualization tools, community forums
3. **Identify the conceptual structure** — what are the foundational concepts? What depends on what? What are the common misconceptions? What's the optimal learning order?
4. **Discover the "aha moments"** — what analogies, visualizations, or exercises make this topic click for people? What do the best teachers do differently?
5. **Collect real-world examples** — case studies, war stories, practical applications that make abstract concepts tangible
6. **Find hands-on resources** — playgrounds, sandboxes, tools the student can actually use during the course

**Save research notes** to a file the student can reference later:

```bash
# Write research to a markdown file alongside the course
# Include: key sources, recommended deep-dives, practice resources, community links
```

**Source priorities** (in order):

1. Official docu