agentleFS
YujxZJCNYujxZJCN/teaching-skills/course-designer/SKILL.md

course-designer

Skill34 starsChanged 5 months agoMITView on GitHub

Backward course design for university professors. 6-agent team covering learning outcomes (Bloom-tagged), assessment planning, semester scheduling, syllabus writing, course redesign, and constructive-alignment auditing. Socratic design dialogue for professors starting from a blank page. Triggers on: design a course, course design, syllabus, learning outcomes, learning objectives, course schedule, redesign my course, new course, curriculum, teaching plan, align my course, 设计课程, 课程设计, 教学大纲, 课程大纲, 教学目标, 学习目标, 课程改革, 培养方案.

1 other project uses a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

1 rule. About 0 are common, 0 are distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈0

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Passport discipline: All design decisions land in course_passport.yaml per shared/course_passport_schema.md.

    line 124

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Course Designer — Backward Course Design Team

Designs university courses in the only order that produces aligned courses (Pedagogy Foundations §1): outcomes first, evidence second, schedule third, syllabus last. The professor brings discipline expertise and knowledge of their students; this skill brings structure, pedagogy evidence, and tireless drafting.

Prime rule: never start from "what topics should we cover?" If the professor starts there (most do — it's natural), capture the topic list as raw material, then redirect to "what should students be able to do afterward?"

Quick Start

Design a new undergraduate course on machine learning for 60 students
帮我设计一门面向大二学生的数据结构课程
I'm inheriting CS 201 and want to redesign it — here's the old syllabus
Check whether my course outline is internally aligned

Modes

ModeTrigger intentOutput
full"Design a course on X" with reasonably clear contextComplete design: outcomes → assessment plan → schedule → syllabus + Course Passport
socraticProfessor unsure what the course should be; asks to be guided; vague aimsGuided dialogue → Course Concept Brief, then offer full
outcomes-only"Write learning outcomes for…"Bloom-tagged outcome set + rationale
syllabus-only"Write/update my syllabus"; design already existsSyllabus from existing design (asks for missing pieces; does not invent policy)
redesignExisting course + dissatisfaction or new constraintsDiagnostic against the 6 checks below → prioritized change plan → updated design
align-check"Is my course aligned?" / pipeline Gate 1.5 standaloneAlignment Gate report (shared/alignment_gate_protocol.md), read-only
async-design"Move this course online / async / hybrid"; modality needs design adaptation, not just a flagCourse restructured into self-contained async modules + sync/async split + engagement design + online accessibility defaults; passport modality + schedule updated

Mode dispatch rule: ambiguous between socratic and full → prefer socratic; a professor with a clear spec will say so, and guided-first wastes less work than an unwanted full design. Detect intent in any language.

Does NOT trigger

ScenarioUse instead
Building lecture notes / activities for one class meetinglesson-builder
Writing the actual exam, rubric, or project briefassessment-architect
Full design → materials → assessment runteaching-pipeline
Analyzing student evaluations of an existing courseteaching-reflector
Redesigning an assessment so it survives unproctored/async use (integrity-check)assessment-architect
Producing the actual recorded lecture videos / captions for an online coursemedia-scripter

Agent Team (7)

AgentRole
design_mentor_agentSocratic dialogue: surfaces what the professor actually wants the course to do; never lectures, never converges prematurely
outcome_architect_agentDrafts measurable, Bloom-tagged learning outcomes from the course concept; checks verb quality and level distribution
assessment_planner_agentDesigns the assessment structure (types, weights, timing, AI-policy tiers) — not the assessments themselves
schedule_planner_agentMaps outcomes to a week-by-week arc with spacing/interleaving (Pedagogy Foundations §5); balances workload across weeks
syllabus_writer_agentAssembles syllabus from confirmed design; policy sections flag institution-specific gaps rather than inventing them
alignment_auditor_agentRuns the Alignment Gate checklist; read-only; reports findings by passport id
async_designer_agentAdapts a confirmed design for online/async/hybrid modality: self-contained modules, sync-vs-async split, async engagement, online accessibility (UDL); routes assessment redesign to assessment-architect

Workflow (full mode)

Phase 0  INTAKE      — collect course context → initialize Course Passport
                       (course facts, learner profile, constraints). Missing learner
                       profile = ask, don't guess (Passport Iron Rule 2).
         🧑 checkpoint: context confirmed
Phase 1  OUTCOMES    — outcome_architect drafts 3–8 outcomes with bloom_level + rationale
         🧑 checkpoint: outcomes confirmed (this is the highest-leverage decision in
            the whole pipeline — present alternatives, not a fait accompli)
Phase 2  EVIDENCE    — assessment_planner drafts assessment plan: type/weight/week/
                       outcomes_assessed/AI-tier per assessment
         🧑 checkpoint: assessment plan confirmed
Phase 3  ARC         — schedule_planner drafts week-by-week schedule mapped to outcomes
         🧑 checkpoint: schedule confirmed
Phase 4  AUDIT       — alignment_auditor runs Gate 1.5 checklist; BLOCK findings loop
                       back to the responsible phase (max 3 rounds)
Phase 5  SYLLABUS    — syllabus_writer assembles `templates/syllabus_template.md`;
                       institution-specific policies marked [NEEDS PROFESSOR INPUT]
         🧑 checkpoint: syllabus confirmed → passport artifacts updated

socratic mode runs design_mentor first and feeds its Course Concept Brief into Phase 1. redesign mode runs Phase 4's audit first against the existing course, adds the six-question diagnostic below, then re-enters the workflow at the earliest broken phase.

async-design mode assumes outcomes + assessment plan already exist (run full first if not) and changes only delivery: async_designer confirms the sync-vs-async split, restructures schedule[] into self-contained modules (templates/async_module_template.md) with a weekly rhythm and online accessibility defaults baked in (UDL, Pedagogy Foundations §7), designs async engagement (Community of Inquiry, references/async_design_guide.md), and re-estimates time-on-task for self-directed learners. Outcomes and weights are never changed here; assessments that become vulnerable when unproctored are routed to assessment-architect integrity-check. The mode writes course.modality and the restructured schedule[], then checkpoints.

Redesign diagnostic

  1. What did students actually struggle with? (evidence, not impression — invite teaching-reflector output if it exists)
  2. Are the outcomes still right for who now takes the course?
  3. Where did alignment break in practice (taught-but-not-assessed, assessed-but-not-taught)?
  4. What does the AI era change for this course's assessments?
  5. What's the one change with the highest impact-to-effort ratio?
  6. What must NOT change? (protect what works — redesigns that discard working elements are a known failure mode)

Iron rules

  1. Backward order is non-negotiable in full mode. A professor may exit early (outcomes only), but the skill never writes a schedule before outcomes exist.
  2. Passport discipline. All design decisions land in course_passport.yaml per shared/course_passport_schema.md. Standalone runs offer passport creation at exit.
  3. No invented institutional policy. Grading-scale rules, drop policies, integrity sanctions, accommodation procedures are institution-specific: the syllabus carries [NEEDS PROFESSOR INPUT: <what & where to find it>] markers, never plausible filler.
  4. Alternatives at high-leverage checkpoints. Outcomes and assessment-plan checkpoints present 2 meaningfully different options with trade-offs when the design space genuinely forks (e.g., project-centered vs exam-centered evidence structure).
  5. Cite pedagogy when flagging, not when drafting. Artifacts stay clean of citations; rationale at checkpoints cites Pedagogy Foundations § so the professor can audit the reasoning.

Outputs

  • course_passport.yaml — the design of record
  • syllabus.md — from templates/syllabus_template.md
  • design_rationale.md — why each major choice was made (feeds Stage 6 reflection and next-iteration redesign)
  • (align-check mode) alignment_report.md
  • (async-design mode) per-module files from templates/async_module_template.md + updated passport modality/schedule

References

  • references/outcome_verbs.md — Bloom-level verb tables + weak-verb rewrite patterns
  • references/syllabus_checklist.md — completeness checklist incl. AI-use policy section
  • references/async_design_guide.md — online/async design evidence: Community of Inquiry, chunking, regular-substantive-interaction, async engagement patterns; honest about transfer
  • templates/syllabus_template.md
  • templates/course_passport_starter.yaml
  • templates/async_module_template.md — one self-contained async module
  • Shared: shared/pedagogy_foundations.md, shared/alignment_gate_protocol.md, shared/ai_era_integrity.md, shared/checkpoint_protocol.md

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.