cuda-tutor

name: cuda-tutor description: > Interactive quiz tutor for a CUDA StudyVault built by cuda-tutor-setup. Delivers 4-question rounds with concept-level proficiency tracking (🟥/🟨/🟩/🟦/⬜) across the 6 CUDA learning topics: CUDA kernels (threads/blocks/warps, memory hierarchy, TMA, WGMMA, cp.async), CUTLASS + CuTe (Layout/Stride/Tensor, MMA atoms, GEMM pipelines), cuTile (Python-first tile DSL), open-gpu-kernel-modules (RM, GSP firmware, UVM, kernel-open layout), NCCL (collectives, topology, NVLink-SHARP, transports), and NVSHMEM (PGAS, symmetric heap, IBGDA, on-stream API). Use when the user wants to (1) take a diagnostic CUDA assessment, (2) drill weak GPU concepts, (3) study a specific CUDA topic, (4) review the learning dashboard, or says things like "quiz me on CUDA", "test my CUTLASS knowledge", "drill NCCL", "/cuda-tutor", "퀴즈".

CUDA Tutor

Quiz-based tutor that tracks what the user knows and doesn't know at the concept level across the 6 CUDA topics. The goal is to surface blind spots in NVIDIA GPU programming knowledge through zero-hint questions and rephrased drills on missed concepts.

Prerequisite: Paired Skill

This skill requires a pre-built CUDA StudyVault. If none exists in CWD, tell the user:

"No StudyVault found. Run the cuda-tutor-setup skill first to generate one."

The expected vault layout — produced by cuda-tutor-setup Phase CU9 / C9 / D9 — is described under ## File Structure below.

Curriculum Structure (read once, internalize)

The vault is organized around 6 topics with a fixed prerequisite chain. Session-type selection in Phase 2 below depends on this DAG:

            1. CUDA Kernels (foundation)
                    │
        ┌───────────┼───────────────────────┐
        ▼           ▼                       ▼
   2. CUTLASS   3. cuTile          4. Open GPU Kernel Modules
        ↑           ↑
        └─peer──────┘
        │
        └────────────────┐
                         ▼
                  5. NCCL  ↔  6. NVSHMEM

When the user picks "Follow curriculum order", serve the next unmastered topic in this chain (CUDA Kernels first; never NCCL/NVSHMEM before CUDA Kernels is 🟩+).

File Structure

StudyVault/
├── *dashboard*               ← Compact overview: proficiency table + stats
└── concepts/
    ├── cuda-kernels.md       ← Per-topic concept tracker
    ├── cutlass.md
    ├── cutile.md
    ├── open-gpu-kernel-modules.md
    ├── nccl.md
    └── nvshmem.md

• Dashboard: aggregated numbers only. Links to concept files. Stays small forever.
• Concept files: one per topic. Tracks each concept with attempts / correct / last tested / status / error notes. Bounded growth.

Workflow

Phase 0: Detect Language

Detect the user's language from their message → {LANG}. All quiz prompts, explanations, and file content render in {LANG}. Technical CUDA terms (e.g., cp.async, ncclAllReduce, nvshmem_put) stay verbatim in English regardless of {LANG}.

Phase 1: Discover Vault

1. Glob **/StudyVault/ in the project.
2. List section directories — expect numbered topic folders (e.g., 01-CUDA-Kernels/, 02-CUTLASS/, ...).
3. Glob **/StudyVault/*dashboard* for the dashboard.
4. If found, read it. Preserve existing file path regardless of {LANG}.
5. If not found, create from the Dashboard Template below.
6. If no StudyVault exists, tell the user to run cuda-tutor-setup first, then stop.

Phase 2: Ask Session Type

MANDATORY: use AskUserQuestion to let the user pick a session. Read the dashboard proficiency table first, then build context-aware options:

1. Unmeasured areas (⬜) exist → include "Diagnostic" targeting those areas (e.g., "Cover the 2 unmeasured topics: cuTile, NVSHMEM").
2. Weak areas (🟥/🟨) exist → include "Drill weak areas" naming the weakest topic(s) (e.g., "Drill NCCL — currently 🟥 28%").
3. Always include "Choose a topic" so the user can pick any of the 6 topics.
4. All areas 🟩/🟦 → include "Hard-mode review" (hardest difficulty mix).
5. If the StudyVault declares a recommended prerequisite chain (it does: CUDA kernels → CUTLASS/cuTile, CUDA kernels → driver, CUDA kernels → NCCL ↔ NVSHMEM), include "Follow curriculum order" which serves the next unmastered topic in the chain.

Header: "Session". Concise option descriptions that list which topics each option targets. No (Recommended) tag. The user MUST select before proceeding.

Phase 3: Build Questions

1. Read the markdown files inside the target topic folder(s) of the StudyVault.
2. If drilling a weak area: also read concepts/{topic}.md to find 🔴 unresolved concepts — rephrase these in a new context (different API call, different hardware generation, different failure scenario). Never repeat the literal question.
3. For cross-topic drill sessions (e.g., NCCL + NVSHMEM): include at least one question that probes the interaction (e.g., "When does it make sense to layer NVSHMEM under NCCL?").
4. Craft exactly 4 questions following references/quiz-rules.md. Cross-stack requirement: if the session covers CUDA Kernels (matmul subset), CUTLASS, or cuTile, at least 1 of the 4 questions MUST be a cross-stack question from references/cross-stack-rosetta.md (Triton equivalent of a CUDA/CUTLASS/cuTile mechanism). The cross-stack question is attributed to its CUDA-side primary topic for proficiency tracking. When this rule and the cross-topic rule in item 3 both apply, the cross-stack question may double-count as the cross-topic question.

CRITICAL: read references/quiz-rules.md before crafting ANY question. Zero hints allowed.

Phase 4: Present Quiz

Use AskUserQuestion:

• 4 questions per round, 4 options each, single-select.
• Header: "Q1. <≤12-char tag>" (examples: Q1. WarpSched, Q2. CuTeLO, Q3. ncclAlgo, Q4. nvshmemAPI).
• Descriptions: neutral, no hints. Distractors must be plausible CUDA concepts (not absurd).

Phase 5: Grade & Explain

1. Show a results table: question / correct answer / user answer / ✅ or ❌.
2. Wrong answers: concise 1–3 line explanation that names the underlying concept and links the relevant StudyVault note via [[wiki-link]].
3. Map each question to its topic for the file-update phase.

Phase 6: Update Files

1. Update concept file (concepts/{topic}.md)

For each question answered:

• New concept → add row to the concept table. If wrong, also add an error-note entry.
• Existing 🔴 concept answered correctly → increment Attempts and Correct, flip status to 🟢, keep the error note as learning history.
• Existing 🟢 concept answered wrong again → increment Attempts, flip status back to 🔴, update the error note.

Concept table format:

| Concept | Attempts | Correct | Last Tested | Status |
|---------|----------|---------|-------------|--------|
| TMA cp.async.bulk vs cp.async | 2 | 1 | 2026-05-15 | 🔴 |

Error-note format (only for wrong answers):

### Error Notes

**TMA cp.async.bulk vs cp.async**
- Confusion: user picked cp.async for 2D tiles
- Key point: cp.async.bulk (TMA) handles 1D-5D tensor copies via descriptor; cp.async is per-thread 4/8/16-byte

2. Update dashboard

• Recalculate per-topic stats from the concept files (sum Attempts and Correct across each topic).
• Update proficiency badges:
  • 🟥 Weak 0–39%
  • 🟨 Fair 40–69%
  • 🟩 Good 70–89%
  • 🟦 Mastered 90–100%
  • ⬜ Unmeasured (no data)
• Update aggregate stats: total questions, cumulative rate, unresolved/resolved counts, weakest/strongest topic.

Dashboard stays compact — no per-session logs, no per-question records.

Dashboard Template

Create when no dashboard exists. Filename localized to {LANG}. Example in English:

# CUDA Learning Dashboard

> Concept-level metacognition tracker for the 6-topic CUDA learning path. See linked files for details.

---

## Proficiency by Topic

| Topic | Correct | Wrong | Rate | Level | Details |
|-------|---------|-------|------|-------|---------|
| 1. CUDA Kernels | 0 | 0 | - | ⬜ Unmeasured | [[concepts/cuda-kernels]] |
| 2. CUTLASS | 0 | 0 | - | ⬜ Unmeasured | [[concepts/cutlass]] |
| 3. cuTile | 0 | 0 | - | ⬜ Unmeasured | [[concepts/cutile]] |
| 4. Open GPU Kernel Modules | 0 | 0 | - | ⬜ Unmeasured | [[concepts/open-gpu-kernel-modules]] |
| 5. NCCL | 0 | 0 | - | ⬜ Unmeasured | [[concepts/nccl]] |
| 6. NVSHMEM | 0 | 0 | - | ⬜ Unmeasured | [[concepts/nvshmem]] |
| **Total** | **0** | **0** | **-** | ⬜ Unmeasured | |

> 🟥 Weak (0-39%) · 🟨 Fair (40-69%) · 🟩 Good (70-89%) · 🟦 Mastered (90-100%) · ⬜ Unmeasured

---

## Stats

- **Total Questions**: 0
- **Cumulative Rate**: -
- **Unresolved Concepts**: 0
- **Resolved Concepts**: 0
- **Weakest Topic**: -
- **Strongest Topic**: -

---

## Curriculum Order

Recommended progression (do not unlock the next tier until the prior is 🟩+):

1. CUDA Kernels
2. CUTLASS  ·  cuTile  ·  Open GPU Kernel Modules  (parallel tier — all build on CUDA Kernels)
3. NCCL  ↔  NVSHMEM  (final tier — multi-GPU communication)

Concept File Template

Create per topic when its first question is asked. Example for concepts/cuda-kernels.md:

# CUDA Kernels — Concept Tracker

| Concept | Attempts | Correct | Last Tested | Status |
|---------|----------|---------|-------------|--------|

### Error Notes

(added as concepts are missed)

Important Reminders

• ALWAYS read references/quiz-rules.md before creating questions.
• NEVER include hints in option labels or descriptions.
• NEVER tag any option with "(Recommended)".
• Randomize the correct answer's position across Q1–Q4.
• Wrong-answer explanations MUST link to the relevant [[concept note]] in the StudyVault.
• After grading, ALWAYS update both the concept file AND the dashboard.
• Keep technical CUDA identifiers verbatim (ncclAllReduce, cp.async.bulk, wgmma.mma_async, nvshmem_quiet) even when prose is in another language.
• For cross-topic questions, attribute the concept to the topic that owns the primary mechanism being tested.
• For seed question banks per topic, see references/cuda-question-bank-seeds.md.
• For exact proficiency-tracking formulas and edge cases, see references/proficiency-tracking.md.