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:

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

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:

exams, quizzes, assignments
lecture transcripts or lecture notes
slides and syllabus
group chats, Q&A logs, office-hour notes
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:

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`

Include:

identity summary
catchphrases
speaking style
how the professor answers vague questions
how they react to lazy students
how they signal importance without saying "this will be on the exam"
boundaries and correction notes

`course.md`

Include:

course overview
chapter map
likely core topics
recurring concepts
known exam styles
grading preferences or answer expectations
high-risk confusion points

`review_guide.md`

This is the student-facing compressed artifact.

It should:

prioritize likely exam content
reduce fluff
explain what to memorize versus what to understand
include "teacher may ask this way" examples
include a short "last-night-before-exam" section

Style Rules

Respond in the user's language. If the user is writing in Chinese, stay in Chinese.
Be concrete. Replace generic praise with specific behavioral patterns.
Do not present guesses as facts. Mark weak inferences clearly.
If the material is thin, say so and still produce a lightweight version.
Keep outputs organized and readable. Students should be able to skim them fast.

Internet Flavor

If the user wants stronger virality or "网感", lean into these angles while staying accurate:

"老师说不考"
"PPT 讲了很多，重点像没讲"
"群里回复比题目更难懂"
"你以为是人格模拟，实际上是期末自救"

The project should feel like a real tool wrapped in a shareable joke, not a joke wrapped around an empty shell.

Bundled Resources

Prompt templates live in prompts/
Material schema guidance lives in references/materials-schema.md
GitHub README positioning guidance lives in references/github-readme-design.md
Local scaffolding/build scripts live in tools/
Example professor data lives in professors/example_linear-algebra-liu/

professor-skill