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.
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- qike7777/skill-librarytheir own version, first seen Mar 31, 2026
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- qike7777/skill-library★ 1same name
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In this repository15 other files it gives its agents.
- CLAUDE.mdCLAUDE.md
- accreditation-mapperSkill
- assessment-architectSkill
- bilingual-coursewareSkill
- cohort-analystSkill
- course-publisherSkill
- deck-studioSkill
- lab-forgeSkill
- lesson-builderSkill
- media-scripterSkill
- student-mentorSkill
- submission-auditorSkill
- ta-coordinatorSkill
- teaching-pipelineSkill
- teaching-reflectorSkill
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
- Very specific: line 124
Passport discipline: All design decisions land in course_passport.yaml per shared/course_passport_schema.md.
≈ “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.
Files
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 alignedModes
| Mode | Trigger intent | Output |
full | "Design a course on X" with reasonably clear context | Complete design: outcomes → assessment plan → schedule → syllabus + Course Passport |
socratic | Professor unsure what the course should be; asks to be guided; vague aims | Guided 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 exists | Syllabus from existing design (asks for missing pieces; does not invent policy) |
redesign | Existing course + dissatisfaction or new constraints | Diagnostic against the 6 checks below → prioritized change plan → updated design |
align-check | "Is my course aligned?" / pipeline Gate 1.5 standalone | Alignment Gate report (shared/alignment_gate_protocol.md), read-only |
async-design | "Move this course online / async / hybrid"; modality needs design adaptation, not just a flag | Course 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
| Scenario | Use instead |
| Building lecture notes / activities for one class meeting | lesson-builder |
| Writing the actual exam, rubric, or project brief | assessment-architect |
| Full design → materials → assessment run | teaching-pipeline |
| Analyzing student evaluations of an existing course | teaching-reflector |
Redesigning an assessment so it survives unproctored/async use (integrity-check) | assessment-architect |
| Producing the actual recorded lecture videos / captions for an online course | media-scripter |
Agent Team (7)
| Agent | Role |
design_mentor_agent | Socratic dialogue: surfaces what the professor actually wants the course to do; never lectures, never converges prematurely |
outcome_architect_agent | Drafts measurable, Bloom-tagged learning outcomes from the course concept; checks verb quality and level distribution |
assessment_planner_agent | Designs the assessment structure (types, weights, timing, AI-policy tiers) — not the assessments themselves |
schedule_planner_agent | Maps outcomes to a week-by-week arc with spacing/interleaving (Pedagogy Foundations §5); balances workload across weeks |
syllabus_writer_agent | Assembles syllabus from confirmed design; policy sections flag institution-specific gaps rather than inventing them |
alignment_auditor_agent | Runs the Alignment Gate checklist; read-only; reports findings by passport id |
async_designer_agent | Adapts 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 updatedsocratic 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
- What did students actually struggle with? (evidence, not impression — invite teaching-reflector output if it exists)
- Are the outcomes still right for who now takes the course?
- Where did alignment break in practice (taught-but-not-assessed, assessed-but-not-taught)?
- What does the AI era change for this course's assessments?
- What's the one change with the highest impact-to-effort ratio?
- What must NOT change? (protect what works — redesigns that discard working elements are a known failure mode)
Iron rules
- Backward order is non-negotiable in
fullmode. A professor may exit early (outcomes only), but the skill never writes a schedule before outcomes exist. - Passport discipline. All design decisions land in
course_passport.yamlpershared/course_passport_schema.md. Standalone runs offer passport creation at exit. - 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. - 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).
- 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 recordsyllabus.md— fromtemplates/syllabus_template.mddesign_rationale.md— why each major choice was made (feeds Stage 6 reflection and next-iteration redesign)- (
align-checkmode)alignment_report.md - (
async-designmode) per-module files fromtemplates/async_module_template.md+ updated passportmodality/schedule
References
references/outcome_verbs.md— Bloom-level verb tables + weak-verb rewrite patternsreferences/syllabus_checklist.md— completeness checklist incl. AI-use policy sectionreferences/async_design_guide.md— online/async design evidence: Community of Inquiry, chunking, regular-substantive-interaction, async engagement patterns; honest about transfertemplates/syllabus_template.mdtemplates/course_passport_starter.yamltemplates/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
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