ΠΑΙΔΕΙΑ · Paideia

Your course. Your patterns. Your errors. Your cheatsheet.
A Claude Code plugin that turns your own materials into a permanent, editable, per-course study graph — every artifact shaped by you, not by a generic syllabus.

License Last commit Top language

한국어 README · PAIDEIA-codex — OpenAI Codex CLI edition

Using OpenAI Codex CLI instead of Claude Code? Same tool, same on-disk layout, same license — just ported to Codex so it still runs after Anthropic's 2026-04-21 removal of Claude Code from the Pro tier. Both editions are maintained side by side; pick whichever agentic CLI you already pay for → PAIDEIA-codex.

Generic study tools teach you the average syllabus. Paideia teaches you your syllabus —
from your professor's notes, your HW emphases, your handwriting, your errors. Every artifact is a markdown file you can edit.

What Paideia means

In ancient Greece, Παιδεία was never the deposit of facts into a passive student. It was the lifelong formation of a complete human being — through structured encounter with primary texts, guided practice under a master, and reflective dialogue that folds feedback into deeper revision.

This plugin implements that cycle for the specific, bounded problem of exam preparation in math, physics, and engineering courses:

  ingest ──▶ analyze ──▶ drill ──▶ grade ──▶ weakmap ──▶ cheatsheet
     ▲                                                        │
     └────────────────── feedback loop ───────────────────────┘

Every stage produces a markdown artifact that lives in your course folder forever. Nothing is ephemeral. Nothing is hidden behind an API. Nothing stops working when the next funding winter hits.

What generic study tools can't do

Most study tools can't personalize to your course, your professor, or your mistakes — because the product they sell is a generic curriculum.

Coursera, edX, Khan Academy — fixed curriculum; no idea what your professor actually emphasizes.
Quizlet, Anki, Brainscape — you manually curate every card; nothing derives patterns from your own solution manuals.
Chegg, Course Hero — generic solution manuals; not organized around your course's recurring idioms.
Brilliant, Duolingo Max, Khanmigo — generic exercises; no knowledge of what you got wrong on HW2 last month.
ChatGPT Study Mode, Gemini "Deep Study", NotebookLM — no persistent per-course state. Every new session starts cold, and last week's mistakes don't shape this week's drill unless you re-upload and re-explain.

None of them form understanding around the specific material in front of you. They each give every student the same answer. Paideia does the opposite: every artifact is derived from your folder — lecture notes, textbook chapter, HW, solutions, handwritten attempts — and accumulates permanently in plain markdown you can edit.

Axis	Paideia	Typical edu-SaaS / LLM chat
Solution patterns (`P1..Pk`)	Extracted from your course's own solutions, citing your own files	Generic textbook list, or none
Drill priority	Weighted by your professor's HW emphasis (HW density = exam tier)	Fixed curriculum, or your own guesswork
Cheatsheet	Built from your `errors/log.md` — whatever you actually got wrong	Boilerplate from the syllabus
Per-course state across sessions	Permanent markdown + YAML, grows as you work	Conversation resets; paid tier for history
Editing an artifact you disagree with	Open the `.md` in any editor, save	Read-only UI
Carrying last semester's prep into next semester	Fork the course folder, edit deltas	Start over
Version history of your own understanding	`git log` / `git diff` any artifact	Not surfaced
Where the artifacts live	Your disk, as text	Remote DB, exportable only with paid tier

The plugin uses Claude Code (which is a paid tool) to do the heavy lifting, but everything it produces lives on your disk as plain markdown. If you later switch to a different model runner, or pause your Claude Code subscription, the course-index, patterns, error log, weakmaps, and cheatsheets are all still yours to open, read, edit, and diff. The scaffold is the plugin; the study graph is yours.

By default, OCR goes through Claude's native vision inside your existing Claude Code session. If you'd rather the handwritten PDFs never leave the machine, ollama pull qwen3-vl:8b is a one-time ~6 GB download that flips every subsequent OCR pass to local Qwen3-VL inference. Either way, everything downstream — patterns, coverage, weakmaps, cheatsheets, the error log — is plain markdown on your disk.

The load-bearing principle: HW density = exam probability

Most "study smart" advice tells you to hunt your blind spots. That is backwards. The professor has already told you where the exam points live — by assigning homework. Sections with heavy HW coverage are 🔥🔥 Exam-primary. Sections with zero HW are ⚪ Low-risk, not "hidden traps". The professor's omission is the strongest possible signal that the topic is off the exam.

Paideia's ranking is explicit about this, and every drill command honors it by default:

Tier	HW count on section	Treatment	Share of mock-exam points
🔥🔥 Exam-primary	3+	Drill hardest	≥70%
🔥 Exam-likely	2	Drill next	~25%
🟡 Exam-possible	1	Warm-pass review	≤5%
⚪ Low-risk	0	Reference only	0

/paideia:quiz all, /paideia:mock, /paideia:hwmap hot all weight output by this tiering. If you insist on drilling a ⚪ section, the plugin complies once and warns you that exam probability is low — your limited time is worth more than an imagined gotcha.

The formation cycle, stage by stage

Stage	What it does	Commands	Produces
Encounter	Read the professor's signal	`/paideia:ingest`	`converted/*/.md` — every lecture, textbook chapter, HW, solution, as clean markdown
Structure	Extract the grammar of the course	`/paideia:analyze`	`course-index/{summary,patterns,coverage}.md` — topic tree, recurring solution patterns (P1..Pk), HW-density exam-tier ranking
Practice	Active recall weighted by what the professor actually tests	`/paideia:quiz`, `/paideia:twin`, `/paideia:blind`, `/paideia:chain`, `/paideia:mock`	`quizzes/`, `twins/`, `chain/`, `mock/` — problems you solve on paper
Reflection	Your hand-written work becomes a grade	`/paideia:grade`	`answers/converted/<name>.md` + `errors/log.md` — OCR via Claude vision (default), Ollama/Qwen3-VL, or Tesseract; then strategy-based grading
Diagnosis	Errors compressed into a priority-ranked weakness report	`/paideia:weakmap`	`weakmap/weakmap_<ts>.md` — append-only history
Distillation	One page, error-driven, printable	`/paideia:cheatsheet`, `/paideia:derive`, `/paideia:pattern`	`cheatsheet/final.md`, `derivations/<slug>.md` — reference only what you actually need

Supporting: /paideia:hwmap surfaces HW-density exam-probability, /paideia:init-course bootstraps a fresh course folder.

Install

Prerequisites

Required

Claude Code CLI
Python 3.9+ (the plugin checks + offers to install its deps)
A Unix-style shell (bash / zsh). The bootstrap + grade commands use heredocs, mkdir -p, mktemp, and subshell backgrounding — native Windows cmd / PowerShell isn't currently supported.
macOS: brew install poppler tesseract tesseract-lang
Linux (Debian/Ubuntu): apt-get install poppler-utils tesseract-ocr tesseract-ocr-kor
Windows: use WSL2, then follow the Linux path inside the WSL shell. Git Bash works too if poppler and tesseract are on PATH.

Optional — only if you want the --ocr=ollama mode (every page image stays on your machine)

ollama + the qwen3-vl:8b model (~6 GB). macOS: brew install ollama. Linux: see the ollama install script. Then ollama pull qwen3-vl:8b.

If you don't install Ollama, Paideia's default OCR engine is Claude's own vision — nothing extra to install, nothing extra to subscribe to beyond Claude Code itself.

Install via Claude Code plugin marketplace

Run each line as a separate command inside Claude Code:

/plugin marketplace add https://github.com/TaewoooPark/PAIDEIA.git

/plugin install paideia@paideia-marketplace

The full https://... URL is deliberate — the owner/repo shorthand makes the CLI try SSH first, which fails if you don't have a GitHub SSH key registered. HTTPS always works.

After install, 14 slash commands become available under the /paideia: namespace.

Per-course bootstrap

Open Claude Code inside the folder you want to use for this course, then run:

/paideia:init-course

This interactively:

Checks Python / poppler / tesseract deps and offers to install missing ones (ollama is only probed when you pick the ollama engine in step 3)
Asks for COURSE_NAME, EXAM_DATE, EXAM_TYPE, USER_WEAK_ZONES
Asks which OCR engine you want as the default: claude (native vision, no install), ollama (local Qwen3-VL, pulls the 6 GB model in the background), or tesseract (lightest, lowest fidelity)
Creates the directory skeleton (materials/, converted/, course-index/, quizzes/, mock/, twins/, chain/, derivations/, cheatsheet/, weakmap/, answers/converted/, errors/)
Writes .course-meta (carries OCR_ENGINE, read by /paideia:grade) and a project-level CLAUDE.md
Wires a project-scoped statusline (.claude/settings.json → scripts/statusline.py) so Claude Code's statusline slot shows paideia · <COURSE> · D-N · <phase> · P<k> ↑ whenever you're inside the course folder
git init so your prep is versioned from the first keystroke

You can always override the OCR engine for a single grade call: /paideia:grade --ocr=claude path/to/answer.pdf.

Course folder layout

After /paideia:init-course, your course folder looks like this:

my-course/
├── .course-meta                     # course name, exam date, OCR engine
├── CLAUDE.md                        # project rules Claude Code reads every turn
├── .gitignore                       # hides answer PDFs, solution keys, OCR scratch
├── .claude/
│   └── settings.json                # wires the PAIDEIA statusline for this folder only
│
├── materials/                       # YOU DROP RAW FILES HERE (PDF or MD)
│   ├── lectures/                    # professor's notes, slide decks
│   ├── textbook/                    # textbook chapters
│   ├── homework/                    # HW problem sets
│   └── solutions/                   # HW solutions / worked examples
│
├── converted/                       # auto-generated markdown — do not edit
│   ├── lectures/                    # output of /paideia:ingest (vision-transcribed LaTeX)
│   ├── textbook/
│   ├── homework/
│   └── solutions/
│
├── course-index/                    # knowledge base — built by /paideia:analyze
│   ├── summary.md                   # topic tree (§1, §1.1, §2, …)
│   ├── patterns.md                  # recurring solution patterns, labeled P1, P2, …
│   └── coverage.md                  # HW ↔ § map with 🔥🔥 / 🔥 / 🟡 / ⚪ exam tiers
│
├── answers/                         # YOU DROP HAND-WRITTEN SCAN PDFs HERE
│   └── converted/                   # /paideia:grade writes OCR'd markdown here
│
├── errors/
│   └── log.md                       # append-only YAML error log (seed for /weakmap + /cheatsheet)
│
├── quizzes/                         # /paideia:quiz — each problem has a hidden _answers.md sibling
├── mock/                            # /paideia:mock — full mock exams (hidden _sol.md siblings)
├── twins/                           # /paideia:twin — same pattern, new surface
├── chain/                           # /paideia:chain — multi-pattern integration problems
├── derivations/                     # /paideia:derive — clean reference derivations
├── cheatsheet/                      # /paideia:cheatsheet — error-driven one-pager (+ optional PDF)
└── weakmap/                         # /paideia:weakmap — timestamped, append-only history

Only two directories are yours to edit by hand:

materials/ — drop source PDFs (or MDs) into the matching subfolder.
answers/ — drop hand-written scan PDFs into the root; the OCR'd markdown shows up under answers/converted/.

Everything else is produced by slash commands and should be treated as regenerable. Delete and rebuild whenever, git log <dir> to see your own progress over time, or point Obsidian at the whole folder as a vault.

A reading tip: use Obsidian

Paideia writes everything as plain markdown with LaTeX math ( $...$ , $$...$$). You can read it in any editor, but Obsidian is the natural choice:

Renders $...$ and $$...$$ math via MathJax with zero configuration
Backlinks let you click from quizzes/q_<ts>.md straight into the cited converted/lectures/chN.md §K
The whole course folder is just a vault — point Obsidian at ~/courses/my-course, and everything is a searchable graph
Works entirely offline, free, local. Consistent with Paideia's philosophy: your notes, your disk, your tool

VS Code with a markdown-math extension works too. The terminal — even with a markdown preview — is bad for math; don't fight that.

Full workflow — an example

Phase 0 — once per course (15 minutes)

cp ~/textbooks/ch*.pdf      ~/courses/my-course/materials/textbook/
cp ~/lecture-notes/wk*.pdf  ~/courses/my-course/materials/lectures/
cp ~/hw/hw*.pdf             ~/courses/my-course/materials/homework/
cp ~/hw/hw*_sol.pdf         ~/courses/my-course/materials/solutions/

In Claude Code:

/paideia:ingest                     # every PDF → vision pipeline (parallel agents, LaTeX-faithful)
/paideia:analyze <weak-zone hints>  # build patterns + coverage + summary
/paideia:hwmap hot                  # surface 🔥🔥 exam-primary zones

Phase 1 — diagnostic (40 minutes)

/paideia:quiz all 20                # broad diagnostic, 20 problems
# solve on paper (40 min), scan to answers/diagnostic.pdf
/paideia:grade                      # local qwen3-vl OCR + strategy grade

Phase 2 — targeted drilling (bulk of your prep time)

/paideia:weakmap                    # priority-ranked weakness report
/paideia:blind hw3-p2               # strategy-only drill on a known problem
/paideia:twin hw3-p2                # variant with same pattern, new surface
/paideia:chain 3                    # multi-pattern integration problem
/paideia:quiz weakmap 5             # 5 problems targeting the latest weakmap

Phase 3 — integration (~90 minutes)

/paideia:mock 90                    # full 90-min mock weighted by HW density
# solve on paper, scan, upload to answers/mock_<ts>.pdf
/paideia:grade                      # grade the mock

Phase 4 — compression (60 minutes, night before exam)

/paideia:cheatsheet --pdf           # error-driven one-pager
/paideia:weakmap                    # review weak zones one more time

Phase 5 — cool-down (10 minutes before exam)

/paideia:weakmap                    # top 3 only. Do not learn new things.

Commands (14 total)

Command	Purpose
`/paideia:init-course`	Bootstrap a fresh course folder (dep check, skeleton, metadata prompt, background `ollama pull`)
`/paideia:ingest [--force]`	Every PDF in `materials/` → markdown in `converted/` via the vision pipeline (parallel agents, one per PDF, LaTeX-faithful).
`/paideia:analyze [hints]`	Build `course-index/{summary,patterns,coverage}.md`
`/paideia:hwmap hot\|<§>`	Surface 🔥🔥 Exam-primary sections ranked by HW density
`/paideia:pattern <§\|Pk\|keyword>`	Show pattern cards from course-index
`/paideia:derive <target>`	Clean reference derivation to `derivations/<slug>.md`
`/paideia:quiz <topic\|§\|weakmap> [N]`	N practice problems, answers hidden in sibling `_answers.md`
`/paideia:blind <problem-id>`	Strategy-check drill on a known problem (no re-solve, describe approach)
`/paideia:twin <problem-id>`	Variant of a known problem — same pattern, new surface
`/paideia:chain <N>`	Multi-pattern integration problem combining N patterns
`/paideia:mock <minutes>`	Full mock exam, HW-density weighted
`/paideia:grade [--ocr=<engine>] [path]`	OCR answer PDF via the engine set in `.course-meta` (Claude vision / Ollama / Tesseract), strategy-grade, append `errors/log.md`
`/paideia:weakmap [concept]`	Priority-ranked weakness report saved to `weakmap/weakmap_<ts>.md`
`/paideia:cheatsheet [--pdf]`	Error-driven one-pager

Under the hood

Ingest pipeline: vision for every PDF

/paideia:ingest routes every PDF in materials/** through the same vision pipeline. pdfplumber was tried first as a fast path for prose-heavy material (textbook, HW) and proved unreliable: even pages that look like plain prose silently word-salad as soon as they mix equations, figures, multi-column layouts, or margin notes. Rather than maintain a per-category heuristic with fallbacks we'd have to retune per course, we route everything uniformly.

Source	Method
`materials/*/.pdf`	Vision pipeline (parallel agents, LaTeX-faithful)
`materials/*/.md`	Copy-through with provenance header

How the pipeline runs: every page is rendered to PNG at dpi=160; every PNG is resized to ≤1800 px on the long edge before any agent starts reading (Claude's many-image requests hard-reject images >2000 px; 16:9 slides at dpi=160 blow past that unless you preempt the resize); then one parallel general-purpose agent is spawned per PDF, each of which reads its own pages sequentially (parallel batches trip the same dimension limit) and transcribes to LaTeX markdown. Output like $$\hat H = -\frac{\hbar^2}{2m}\partial_x^2 + V(x)$$ instead of ℏ ∂ p2 ℏ 2 ∂ 2 p ̂. A real 13-lecture, 208-page quantum mechanics course ran end-to-end with zero [?] markers.

Details in plugins/paideia/skills/pdf/VISION.md.

Hand-writing OCR: three engines, you pick

The user does not type math into chat. They solve on paper, scan to PDF, drop the PDF into answers/, and run /paideia:grade. The plugin converts the scan to markdown via one of three engines, chosen per course (via OCR_ENGINE in .course-meta) and overridable per call (via /paideia:grade --ocr=<engine>):

Engine	Default?	How it runs	When to pick it
`claude`	Yes	`pdftoppm` renders each page → Claude reads each PNG directly → synthesizes markdown in one pass. No extra model, no subprocess, nothing to install.	The out-of-the-box path. Strong on Korean + LaTeX; no model-load stall.
`ollama`	opt-in	`vision_ocr.py --engine=ollama` → local Qwen3-VL 8B with automatic tesseract fallback.	You want the page images to never leave the machine (not even to Anthropic). Requires `ollama pull qwen3-vl:8b` once (~6 GB).
`tesseract`	opt-in	`vision_ocr.py --engine=tesseract` → pytesseract `eng+kor` only.	Fastest and lightest; acceptable for typed scans; poor on hand-writing.

Each engine writes answers/converted/<stem>.md with a  /  header comment so /paideia:grade can caveat low-confidence OCR.

Default choice (claude) is deliberately the path of least friction: anything that already ships with Claude Code is enough. The ollama engine exists for users who want a hard privacy boundary on the page images themselves, and tesseract exists as a reliable floor when nothing else is available.

Strategy-based grading, not line-by-line

OCR noise in hand-written math makes strict algebraic grading useless — a single misread ∫ vs ∑ would cascade. More importantly, pattern recognition is the actual exam bottleneck, not arithmetic. The grader therefore checks three things on each problem:

Pattern — did the student pick the right Pk from course-index/patterns.md?
Variables — did they identify the right substitution / basis / index / contour?
End-form — does their final expression have the right shape (dimensions, asymptotics, structure)?

Patterns extracted from your solutions

/paideia:analyze doesn't ship a generic "calculus moves" list. It reads your course's actual solution manual, extracts recurring solution patterns, and labels them P1, P2, ... with worked instances that cite your own converted/solutions/ files. The patterns are your course's idioms, not a textbook's. For a complex analysis course, P3 might be "closed contour + Jordan's lemma + residue at essential singularity." For a linear systems course, P3 might be "partial fractions + inverse Laplace with complex poles." Every discipline has its own moves; only the course itself reveals them.

Append-only history

weakmap/ never overwrites. Every /paideia:weakmap invocation produces weakmap/weakmap_<ISO-timestamp>.md. You can git log weakmap/ and see exactly which weaknesses collapsed first, which ones persisted, which new ones emerged after the diagnostic mock. This is "git diff your own understanding over time" in practice.

Statusline: where you are in the cycle, at a glance

/paideia:init-course writes a project-scoped .claude/settings.json that points Claude Code's statusline slot at scripts/statusline.py. The statusline shows:

paideia · <COURSE_NAME> · D-<days-to-exam> · <phase> · P<top-miss> ↑

<phase> is derived from artifacts on disk (not a calendar), so it only advances when you actually produce the artifact:
- setup — course-index/patterns.md doesn't exist yet → run /paideia:ingest + /paideia:analyze
- diag — patterns exist, no quizzes yet → run /paideia:quiz all 20 for a broad diagnostic
- drill — quizzes exist, no mock yet → cycle /paideia:blind · /paideia:twin · /paideia:quiz weakmap
- mock — a mock exists, no cheatsheet yet → compress with /paideia:cheatsheet --pdf
- cram — cheatsheet/final.{md,pdf} exists → taper, re-read the weakmap, stop learning new things
- cool — D-0 overrides everything (exam is today)
<top-miss> is the most frequent pattern: tag from the latest weakmap/weakmap_*.md (falls back to errors/log.md). The ↑ is a reminder that this is the pattern to drill next.
Color is a random neon shade, picked deterministically from sha1(session_id) — it stays stable within one session but rotates across sessions. Pure decoration; no information is color-encoded.
Silent fallback — if the CWD has no .course-meta, the script outputs nothing, and Claude Code falls back to its default statusline. Safe to leave wired.

To disable it for one course, delete or edit that course's .claude/settings.json. To disable globally, edit ~/.claude/settings.json instead. To customize the format or colors, edit plugins/paideia/scripts/statusline.py — everything is in one ~180-line file.

What ships

PAIDEIA/
├── .claude-plugin/marketplace.json      # marketplace manifest
├── LICENSE                              # MIT
├── README.md                            # this file
├── README.ko.md                         # Korean mirror
└── plugins/paideia/
    ├── .claude-plugin/plugin.json       # plugin manifest (name, version, author)
    ├── README.md                        # quick-reference card
    ├── skills/
    │   ├── pdf/
    │   │   ├── SKILL.md                 # routing decision tree + reference extractors
    │   │   └── VISION.md                # default ingest pipeline — parallel vision agents per PDF
    │   ├── vision-ocr/SKILL.md          # Claude vision (default) + Ollama Qwen3-VL + tesseract
    │   ├── course-builder/SKILL.md      # ingest + analyze pipeline
    │   ├── exam-drill/
    │   │   ├── SKILL.md                 # drill primitives (twin, blind, chain, mock)
    │   │   └── twin-recipe.md           # invariance rules for variant generation
    │   └── answer-processing/SKILL.md   # strategy-grade hand-written OCR output
    ├── commands/                        # 14 slash commands
    │   ├── init-course.md  ingest.md    analyze.md   hwmap.md
    │   ├── pattern.md      derive.md    quiz.md      blind.md
    │   ├── twin.md         chain.md     mock.md      grade.md
    │   └── weakmap.md      cheatsheet.md
    └── scripts/
        ├── vision_ocr.py                # opt-in: ollama qwen3-vl driver + tesseract forcing, for --ocr=ollama|tesseract
        └── statusline.py                # emits `paideia · <COURSE> · D-N · <phase> · P<k> ↑` for Claude Code's statusline slot

Design convictions

The terminal is bad for math. Claude produces markdown files; you read them (ideally in Obsidian).
Typing solutions is slow and error-prone. You solve on paper, scan, and the plugin OCRs locally.
OCR noise is inevitable. So grading is strategy-based (pattern / variables / end-form), not line-by-line algebra. This is what the actual exam grader is evaluating anyway.
Patterns must be extracted from your course's solutions — not from a generic list. Every discipline has its own idioms; only the course itself reveals them.
Your errors are the most valuable study signal — more than the textbook, more than the lectures. The cheatsheet is generated from errors/log.md, not from the syllabus.
HW density tells you the exam. Your time is finite; spend it where the points are.
Everything is yours to edit. Patterns, weakmaps, cheatsheets, the error log — all plain markdown/YAML in your own git history. Disagree with P3? Rewrite it, and the next drill uses your edit. Fork a course folder from last semester into a new one and edit deltas. The plugin is a scaffold; the study graph is yours.

FAQ

Does this work for non-math courses?
It's built around problem-pattern extraction, so it shines in quantitative disciplines: math, physics, EE, CS-theory, ML-theory, statistics, engineering. For history or literature it would still ingest and produce summaries, but the drill commands assume problems have solution patterns.

Korean and English mixed materials?
Yes. Ingestion and OCR are configured for eng+kor. Patterns and grading responses honor the language mix of your source materials.

How is this different from just asking ChatGPT / Claude / Gemini to help me study?
Per-course persistence. An LLM chat has no memory of the pattern you missed on HW2 two weeks ago, no ranking of which sections your professor actually emphasizes, no notion of "your typical error type." Paideia writes all of that to markdown files on your disk. A /paideia:weakmap today is informed by every /paideia:grade since the course began, because errors/log.md is append-only. A generic chat session, however smart, is a blank slate every time you open it.

Can I edit the patterns / cheatsheet / weakmap if I disagree?
Yes. That's the whole point of keeping them as plain markdown. If P3 feels wrong, open course-index/patterns.md and rewrite it — subsequent drills will use your edit. If the cheatsheet emphasizes the wrong thing, trim it. The plugin is a scaffold; the study graph is yours to shape.

Do I need Ollama / Qwen3-VL to use this?
No. The default OCR engine is Claude's native vision — it uses the Claude Code session you're already in and needs no extra install. Ollama + qwen3-vl:8b is an opt-in path for users who want the page images to stay on their machine entirely (not even visible to Anthropic's servers during a grade call). tesseract is a third option for minimal-install setups or typed scans.

What if my machine can't run qwen3-vl:8b even though I picked Ollama?
The vision_ocr.py driver automatically falls back to tesseract eng+kor on any Ollama failure. You can also just set OCR_ENGINE: claude in .course-meta (or pass --ocr=claude) and skip Ollama entirely.

Can I reuse the plugin across multiple courses?
Yes — each course lives in its own folder with its own .course-meta, course-index/, errors/log.md, and weakmap/. Nothing is shared or polluted across courses. Open Claude Code inside whichever course folder you're working on.

Can I trust an LLM to grade my work?
Grading is strategy-based (pattern match, not algebra), the grader cites the pattern from course-index/patterns.md, and every grade writes a YAML entry you can audit in errors/log.md. If a grade is wrong, fix the YAML entry — the next /paideia:weakmap reflects the correction.

Is my data private?
Your PDFs, markdown, errors, and weakmaps all live in your local course folder — nothing is uploaded to any third-party service. The only network traffic the plugin itself generates depends on the OCR engine you pick: with claude (default), page images flow through your existing Claude Code session (i.e., whatever path your normal Claude Code conversation already takes — nothing new); with ollama, nothing leaves the machine after the one-time model download; with tesseract, nothing leaves the machine ever.

Connect

License

MIT. Use freely. Fork and modify for your own courses — the point of the plugin is that the study graph it builds is yours to shape, not a fixed product you have to live with.

Generic curricula teach the average student. Παιδεία — formation, one student at a time.