OPTIMETA

Use whenever the user wants to ingest a new course's materials (lecture notes, textbook chapters, HW problems, HW solutions) and build the course-specific knowledge base — patterns.md (recurring solution techniques), coverage.md (HW-to-section map with 🔥 exam tiers + ⚠weak flags), and summary.md (topic tree). Invoked by `/ingest` and `/analyze` slash commands. Designed to be domain-general across

Use when the user wants exam-focused drilling from the course's analyzed material. Generates twin variants of known problems (`/twin`), runs strategy-level blind drills on known problems (`/blind`), creates integration problems chaining multiple patterns (`/chain`), surfaces pattern cards (`/pattern`), and shows coverage/exam-tier maps (`/hwmap`). Reads from `course-index/patterns.md`, `course-ind

Use whenever a hand-written or scanned answer PDF needs transcription to markdown for /grade. Three tiers — Claude native vision (default, no extra install), local Qwen3-VL 8B via ollama (opt-in privacy mode), pytesseract fallback. The engine is selected via `OCR_ENGINE` in `.course-meta` (written by /paideia:init-course) and can be overridden per-call with `/paideia:grade --ocr=<engine>`.

Use whenever the user works with PDF files — reading/extracting text from PDFs (lecture notes, textbook chapters, HW problems, HW solutions, hand-written answers), converting PDFs to markdown for downstream analysis, merging/splitting PDFs, or creating PDFs. For scanned or hand-written PDFs, OCR is required (pytesseract + pdf2image). Based on Anthropic's official PDF skill (github.com/anthropics/s

Use whenever the user uploads a hand-written or scanned answer PDF to be graded against a reference solution. Converts answer PDFs in `answers/*.pdf` to markdown in `answers/converted/*.md` using the pdf skill (OCR as needed), then performs strategy-based grading against `converted/solutions/*.md` or `quizzes/*_answers.md`. Invoked by `/grade`.