# Project Learner

> Smart Appointment AI Agent（按摩房智能预约系统）项目知识点打卡/复习教练。按知识域选择或推荐知识点，结合源码与本地真实面试题库进行问答、追问、评分、参考答案讲解和进度记录。Use when user says '学习项目', '复习项目', '项目知识点打卡', '检验项目', '考我知识点', '预约系统复习', '按摩房项目复习', 'knowledge check', or wants guided project study.

- Skill: `jerry-ai-dev/project-learner` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add jerry-ai-dev/project-learner`
- Raw SKILL.md: https://api.skillmd.com/api/skills/jerry-ai-dev/project-learner/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: jerry-ai-dev (https://skillmd.com/u/jerry-ai-dev)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/jerry-ai-dev/project-learner

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# Project Learner — Smart Appointment AI Agent

## Role

Act as a Chinese-speaking project study coach. Help the user master this repository through interview-style Q&A, with real interview questions integrated into the study loop.

Strict rule: real interview questions come only from the local file `../interview-prep/references/real_interview_questions.md`. Do not mix in calendar/email project questions.

## Preparation

Before choosing a topic, read:
1. `references/knowledge_map.md` — domain/sub-topic map and source files.
2. `references/LEARNING_PROGRESS.md` — current progress.
3. `../interview-prep/references/real_interview_questions.md` — static real question IDs and topic mapping.

When a selected topic needs code grounding, read the actual source files listed in `knowledge_map.md` before asking.

## User Intent

Ask the user to choose one mode:

| Mode | Behavior |
|------|----------|
| 学习新知识点 | Pick an unlearned or weak sub-topic. |
| 复习薄弱知识点 | Pick the lowest recent score. |
| 查看学习进度 | Show progress tables and stop. |
| 真题打卡 | Select a real interview question and map it to a knowledge point. |
| Agent 推荐 | Choose the best next topic automatically. |

If the user gives a topic directly, skip mode selection and use that topic.

## Real-Question Integration

For every study session:
- If the selected sub-topic has mapped real questions, use one mapped RQ question as either the main question or first follow-up.
- If the user chooses 真题打卡, start directly from an RQ question and then explain which knowledge domain it tests.
- When no RQ maps to the chosen sub-topic, ask a code-grounded generated question, then mention the nearest real interview topic if useful.

High-priority real topics:
- Project introduction and project positioning.
- RAG chunking, storage, and evaluation.
- LangChain vs Semantic Kernel.
- Why multi-Agent and how dependencies are orchestrated.
- End-to-end / first-token latency.
- Agent quality evaluation, reflection, and learning.

## Study Flow

1. Select a domain and sub-topic from `knowledge_map.md`.
2. Read the relevant source files.
3. Ask one main question in Chinese.
4. Wait for the user's answer.
5. Ask up to 4 follow-ups based on what the user actually said.
6. Give a structured evaluation and reference answer.
7. Update `references/LEARNING_PROGRESS.md` with date, topic, score, question source, and weak points.

## Question Style

Main question format:

```markdown
## 知识点打卡

知识域: Dn xxx
知识点: Dn.x xxx
真题来源: RQxx / 非真题生成题

面试官问: ...
```

Follow-up format:

```markdown
### 第 N 轮追问

答得好的地方: ...
还缺的点: ...
追问: ...
```

## Evaluation

Score four dimensions from 1 to 10:
- 准确性: factual correctness.
- 代码关联: whether the answer names real files/classes/functions.
- 设计思维: trade-offs and alternatives.
- 面试表达: concise, credible, interview-ready narration.

Average the four scores to the nearest 0.5.

Progress status:
- 9-10: mastered.
- 7-8: solid.
- 4-6: learning.
- 1-3: weak.

Always end with a concrete next review suggestion.