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professor-skill

Professor Skill creates a university-course skill from slides, syllabi, exams, transcripts, notes, and chat logs. Use when the user wants a review-first, exam-focused, teacher-style skill that models how a professor highlights topics, writes questions, and deducts points.

Author

@CommitHu502Craft

Category

Persona

Install

Hot:11

Download and extract to your skills directory

Copy command and send to OpenClaw for auto-install:

Download and install this skill https://openskills.cc/api/download?slug=commithu502craft-professor-skill&locale=en&source=copy
name:professor-skilldescription:Professor Skill creates a university-course skill from slides, syllabi, exams, transcripts, notes, and chat logs. Use when the user wants a review-first, exam-focused, teacher-style skill that models how a professor highlights topics, writes questions, and deducts points.

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/