---
id: gh-professor-skill
name: "professor-skill"
url: https://skills.yangsir.net/skill/gh-professor-skill
author: CommitHu502Craft
domain: education
tags: ["education", "exam-prep", "professor", "study-aid", "course-creation"]
install_count: 100
rating: 4.00 (20 reviews)
github: https://github.com/CommitHu502Craft/professor-skill
---

# professor-skill

> “教授技能”能根据课件、大纲、考试、笔记和聊天记录等材料，创建大学课程技能。它以复习和考试为导向，模拟教授的教学风格，包括重点讲解、出题和评分，帮助用户高效备考。

**Stats**: 100 installs · 4.0/5 (20 reviews)

## Before / After 对比

### 个性化教授技能创建

**Before**:

面对海量课程资料，复习无从下手，考试重点模糊，感觉老师风格难以捉摸。

**After**:

使用教授技能，根据真实材料创建专属AI教授，精准提炼考点，模拟老师出题和评分，高效备考。

## Readme

# Professor Skill

Use this skill when the user wants to build a `大学老师.skill` / `Professor Skill` from real course materials.

The output must stay useful first and funny second:

- Useful enough to help with review, Q&A, and exam prep
- Distinct enough to feel like this specific professor
- Meme-friendly enough that the result is screenshot-worthy

## Core Model

Always separate the professor into two engines:

1. `Course Brain`
   Extract the actual course structure:
   - key topics
   - repeated concepts
   - likely exam scope
   - common question types
   - grading preferences
   - typical mistakes

2. `Teacher Persona`
   Extract the professor's delivery style:
   - catchphrases
   - explanation rhythm
   - patience level
   - response habits
   - classroom humor or sarcasm
   - how they emphasize or downplay topics

The final output should merge both:
`Teacher Persona decides tone. Course Brain decides substance.`

## Workflow

### Step 1: Collect minimum intake

Ask only for the smallest set of details needed to start:

- professor name
- school or department if available
- course name
- what materials the user has
- one-line impression of the professor

If the user already provided files or context, do not repeat questions.

If there is no professor workspace yet, initialize one first:

```text
python ${CLAUDE_SKILL_DIR}/tools/professor_writer.py --name "<teacher>" --course "<course>" --school "<school>" --department "<department>"
```

This creates:

- `meta.json`
- `persona.md`
- `course.md`
- `review_guide.md`
- `materials/` source folders
- `materials_manifest.md`
- `source_brief.md`
- `workflow.md`

### Step 2: Sort material by signal strength

Rank sources before extracting:

1. exams, quizzes, assignments
2. lecture transcripts or lecture notes
3. slides and syllabus
4. group chats, Q&A logs, office-hour notes
5. professor bio, homepage, publication summaries

Use higher-signal sources to determine exam and review content.
Use lower-signal sources to sharpen persona and identity.

When source files have been placed into `materials/`, always run the single-command build pipeline:

```text
python ${CLAUDE_SKILL_DIR}/tools/build_professor_outputs.py "<professor-dir>"
```

This pipeline must:

- extract parseable text from `pdf`, `pptx`, `docx`, and text files into `exports/extracted/`
- refresh `materials_manifest.md`
- refresh `source_brief.md`
- generate `persona.md`, `course.md`, and `review_guide.md`
- validate the workspace before claiming it is ready

Read `materials_manifest.md`, `source_brief.md`, and the highest-signal extracted files first.

If `${CLAUDE_SKILL_DIR}` is unavailable in the runtime, resolve tool paths relative to the skill root directory rather than the caller's working directory.

### Step 3: Build three artifacts

Always generate these three files or sections:

- `persona.md`
- `course.md`
- `review_guide.md`

If the user explicitly wants it, also generate:

- mock exam
- likely key points
- oral-style explanation notes
- teacher-style chat replies

When updating existing artifacts:

- preserve strong evidence already reflected in the files
- replace `[fill me]` placeholders with concrete content
- keep unsupported claims marked as low-confidence inference

### Step 3.5: Refuse fake confidence

If `validate_professor.py` warns that there are no exams, no transcripts, or no indexed sources, you should still help, but explicitly lower confidence and explain which parts are inferred.

### Step 4: Keep the humor disciplined

Humor should come from recognition, not random jokes.

Prefer these patterns:

- "这题上课讲过" energy
- vague but familiar teacher phrasing
- passive-aggressive reminders
- overlong slides, underspecified key points
- exam warnings that feel suspiciously real

Avoid:

- insulting the professor
- fabricated misconduct
- fake official notices
- humor that reduces usefulness

## Legal And Content Guardrails

- Treat imported materials as potentially sensitive by default.
- Do not encourage users to upload or redistribute content they do not have the right to use.
- Do not present generated text as an official notice, grading rule, or statement from the real professor.
- Do not fabricate private facts, misconduct claims, or internal school policies.
- If the user appears to be using private chats, recordings, unpublished materials, or other potentially restricted content without permission, warn briefly and continue only with clearly lawful, minimal assistance.
- When uncertainty exists, prefer summarization, study guidance, and low-confidence caveats over imitation that could be mistaken for the real person.

## Output Requirements

### `persona.md`


---
*Source: https://skills.yangsir.net/skill/gh-professor-skill*
*Markdown mirror: https://skills.yangsir.net/api/skill/gh-professor-skill/markdown*