Teaching Skills for Claude Code

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A comprehensive suite of Claude Code skills for university professors, covering the full
teaching lifecycle: design → build → assess → deliver → reflect → improve.

15 skills · 84 modes · 71-agent ensemble · 2 quality gates · 1 Course Passport

AI is your teaching assistant, not your replacement. This suite won't teach for
you. It handles the structure and the grunt work — drafting outcomes, building
blueprints before exams, auditing alignment, formatting syllabi, coding evaluation
comments — so you can focus on what actually requires you: knowing your discipline,
knowing your students, and deciding what matters. Every consequential decision passes
through a checkpoint where you decide.

Architecturally inspired by Academic Research Skills
(Cheng-I Wu) — the research-side sibling of this suite. If you also write papers, the
two compose: teaching-reflector's SoTL mode hands off to ARS's deep-research and
academic-paper skills.

Install

Plugin (recommended):

/plugin marketplace add YujxZJCN/teaching-skills
/plugin install teaching-skills

Manual: clone this repo and symlink the skill directories into
~/.claude/skills/ (or the project's .claude/skills/).

Verify: run /ts-status in a directory with a course_passport.yaml, or just say
"Design a new course on X for 60 second-year students."

The skills

Extension skills — production, operations, and compliance layers; each works
standalone and the pipeline dispatches them on demand:

The pipeline

Stage 0 CONTEXT  → Course Passport initialized                 🧑
Stage 1 DESIGN   → course-designer                             🧑
Gate 1.5 ALIGNMENT — constructive-alignment audit              ✓ machine + ack
Stage 2 BUILD    → lesson-builder (just-in-time by default)    🧑
Stage 3 ASSESS   → assessment-architect + integrity audit      🧑
Gate 3.5 QUALITY — AI-integrity, transparency, UDL, workload   ✓ machine + ack
Stage 4 DELIVER  → weekly loop: materials · mentoring · submission audits · midcourse feedback
Stage 5 REFLECT  → teaching-reflector eval-analysis            🧑
Stage 6 IMPROVE  → iteration record → next term re-enters Stage 1

🧑 = professor checkpoint (you decide) · ✓ = deterministic gate, then your acknowledgment

Two ideas hold this together:

• The Course Passport (shared/course_passport_schema.md) — a YAML file that is the
  single source of truth for the course. Outcomes, assessment plan, schedule, policies,
  gate findings, artifact ledger, iteration history. State lives in the passport, not
  the conversation: any fresh session resumes from it. Every skill also works standalone
  without one.
• Constructive alignment as a machine check — the Alignment Gate verifies the
  outcome–teaching–assessment triangle is closed before anything is built on it; the
  Quality Gate verifies the built artifacts are transparent (TILT), accessible (UDL),
  integrity-coherent (AI-era), and humanly workloaded — before students see them.

Showcase: what the output looks like

Browse the worked example → — a complete artifact set for a
demonstration course (CS 304 Introduction to Machine Learning, 90 students): filled
Course Passport, syllabus, alignment gate report with a dismissed-warning trail, a
lesson plan + activity sheet, an exam blueprint with sample items and a worked key,
and the AI-integrity audit that redesigned the course project. Synthetic and clearly
labeled as such — but every file obeys the suite's own rules, so you can judge the
output quality before installing.

Quick start

# Full lifecycle
"I'm teaching a new course on environmental economics next fall — run the full pipeline"

# Blank page
"Guide me — I know my field but I've never designed a course"          → socratic

# Single artifacts
/ts-outcomes  /ts-syllabus  /ts-lesson  /ts-exam  /ts-rubric  /ts-letter  /ts-evals

# Mid-entry
"I already have a syllabus, build me week 3's materials"
"Semester's over, here are my student evaluations"                      → eval-analysis

# Status
/ts-status    "What's next for my course?"

See QUICKSTART.md for a worked first session and
MODE_REGISTRY.md for all 84 modes.

Design principles

1. The professor is the pilot. Checkpoints at every stage; "key decisions made for
   you" are always surfaced, never buried; "just proceed" is respected
   (shared/checkpoint_protocol.md).
2. Evidence-based defaults, professor's final call. Recommendations cite
   shared/pedagogy_foundations.md (backward design, constructive alignment, active
   learning, retrieval practice, TILT, UDL, cognitive load, feedback research). When you
   overrule a principle, the decision is logged and never re-litigated.
3. No invented context. Empty learner profile → the skill asks. Institutional
   policies → [NEEDS PROFESSOR INPUT] markers, never plausible filler. Uncertain
   domain claims in generated materials → [VERIFY] markers.
4. Person-affecting outputs are evidence-bound. Feedback, letters, and intervention
   plans use only material you provided — no invented anecdotes, ever — and always end
   with a verify-before-send reminder. Student data never enters the Course Passport.
5. Integrity by design, not detection. The AI-era integrity model
   (shared/ai_era_integrity.md) uses per-assignment policy tiers and structural
   resilience patterns. Nothing in this suite relies on AI-detection tools.
6. The contracts are machine-checked. The Course Passport has a JSON Schema
   (shared/course_passport.schema.json) and validators: scripts/check_passport.py
   (cross-reference invariants — id mirrors, weight sums) and
   scripts/check_alignment_gate.py (Gate 1.5 executed deterministically, not
   interpreted by a model). CI runs them plus a mutation-test suite on every push.
7. Honest evidence reading. Student evaluations are treated as biased,
   small-sample evidence of student experience — analyzed thematically, triangulated,
   and caveated — not as a teaching-quality score.

Repository layout

course-designer/        SKILL.md + agents/ + references/ + templates/
lesson-builder/         〃
assessment-architect/   〃
student-mentor/         〃
submission-auditor/     〃
teaching-reflector/     〃
teaching-pipeline/      SKILL.md + agents/ + references/
deck-studio/            extension skills, same layout
lab-forge/              〃
media-scripter/         〃
course-publisher/       〃
ta-coordinator/         〃
accreditation-mapper/   〃
bilingual-courseware/   〃
cohort-analyst/         〃
shared/                 Course Passport schema (prose + JSON Schema) · pedagogy
                        foundations · gate protocols · AI-era integrity · checkpoints
scripts/                check_passport.py · check_alignment_gate.py · build_dashboard.py · registry lint
tests/                  validator suite (golden fixture + mutation tests)
commands/               /ts-* slash commands
docs/                   ARCHITECTURE.md
.claude-plugin/         plugin + marketplace manifests
skills/                 symlinks for plugin auto-discovery

Languages

English and Simplified Chinese trigger keywords ship by default; intent-based modes
(socratic design, mid-entry routing) work in any language. Add your language's keywords
to a skill's description frontmatter to improve trigger matching.

License

MIT. The architecture (skills/agents/gates/passport pattern) is inspired by
Academic Research Skills
(CC BY-NC 4.0); no content from that project is reproduced here.