{"name":"course-study-kit","description":"Turn a course syllabus into a study kit in one call: a parsed outline, a sequenced reading/study plan, flashcards, and a bullet summary. Built for course prep, study kit, syllabus to plan, and learning workflow use cases where a student or agent needs a syllabus breakdown without doing four separate lookups. This generates a study outline and aids from the syllabus text provided; it makes no credentialing, credit, or accreditation claims. Composite: one call runs reading-plan-generate + paper-to-flashcards + summarize-text in parallel, after syllabus-parse extracts the course name. Sends syllabus_text plus optional hours and level; returns outline, plan, flashcards, and summary. Use it as a syllabus breakdown tool, study plan generator, or course prep assistant.","price":"$0.05","method":"POST","input_schema":{"properties":{"syllabus_text":{"type":"string","description":"Raw course syllabus text (pasted from a PDF, LMS page, or course website). 40-25000 chars."},"hours":{"type":"number","description":"Optional total study time budget in hours for the reading plan. Positive number, max 1000."},"level":{"type":"string","description":"Optional learner level for the reading plan: 'beginner', 'intermediate', or 'advanced'."}},"required":["syllabus_text"]},"input_example":{"syllabus_text":"Intro to Machine Learning\nWeek 1: Linear regression and gradient descent. Reading: chapter 2. Assignment: problem set 1 due Friday.\nWeek 2: Classification and logistic regression. Reading: chapter 3.","hours":20,"level":"beginner"},"output_example":{"note":"No credentialing claims: this generates a study outline and aids from the syllabus text provided, not a certification of completion, credits, or accreditation.","outline":{"course":"Intro to Machine Learning","weeks":[{"week":1,"topics":["Linear regression","gradient descent"],"readings":["chapter 2"],"assignments":["problem set 1"],"due_dates":["Friday"]}]},"plan":{"stages":[{"title":"Foundations of linear models","goal":"Understand linear regression and gradient descent","resources_types":["intro textbook chapter","hands-on tutorial"],"est_hours":6}]},"flashcards":{"flashcards":[{"question":"What optimization method is used to fit linear regression in week 1?","answer":"Gradient descent","difficulty":"easy"}]},"summary":{"text":"- Week 1 covers linear regression and gradient descent with a problem set due Friday.\n- Week 2 moves to classification via logistic regression."},"composed_of":["syllabus-parse","reading-plan-generate","paper-to-flashcards","summarize-text"],"components":[{"name":"syllabus-parse","ok":true,"ms":2800},{"name":"reading-plan-generate","ok":true,"ms":3100}],"degraded":false},"x402_payment_required":true,"x402_facilitator":"https://api.cdp.coinbase.com/platform/v2/x402"}