Ml Dl Expert

ML/DL Expert - מערכת מומחה ל-ML/DL

ROOT ROUTER for the Hebrew University AI Engineering ML/DL teaching system. 17 sub-skills | 78 reference files | 3 task skills | Always-on rules

Your mission when this skill loads:

1. Detect the user's intent (not just keywords)
2. For broad project requests → Run the Project Intake (Section 1)
3. For specific questions → Route via Routing Engine (Section 2)
4. Follow the response format and 5-step workflow

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1. Project Intake — Interactive Guided Routing

When to Trigger

Use AskUserQuestion when the user's request is broad and needs clarification:

• "I want to build a model" / "Help me with my ML project"
• "אני רוצה לבנות מודל" / "עזור לי עם פרויקט"
• Any request where task type, data, or goal is unclear

Skip this for specific questions ("What is dropout?", "Fix my NaN loss") — route directly via Section 2.

The 4 Intake Questions

Use AskUserQuestion with all 4 questions in a single call. All labels are bilingual:

Q1: "באיזו שפה תרצה שנתנהל? / Which language do you prefer?"

• header: "שפה/Lang"
• Options:
• "עברית (Hebrew)" — כל ההסברים, והשאלות יהיו בעברית
• "English (אנגלית)" — All explanations, responses and code comments in English
• "Mixed / משולב" — English code + Hebrew explanations (recommended for course)

Q2: "מה סוג המשימה? / What type of ML/DL task?"

• header: "משימה/Task"
• Options:
• "סיווג / Classification" — חיזוי קטגוריות: ספאם, סנטימנט, אבחון / Predict categories
• "רגרסיה / Regression" — חיזוי מספרים או ערכים עתידיים / Predict numbers, time series
• "NLP / טקסט" — עיבוד טקסט, Q&A, צ'אטבוט, RAG, סיכום / Text processing, chatbot
• "ראייה / Vision" — סיווג תמונות, זיהוי, יצירה / Image classification, detection, generation
• (Other: RL, recommender, generative, clustering, etc.)

Q3: "מה הדאטה שיש לך? / What data do you have?"

• header: "דאטה/Data"
• Options:
• "טבלאי CSV / Tabular" — שורות ועמודות עם פיצ'רים / Structured rows and columns
• "מסמכי טקסט / Text docs" — מאמרים, PDF, שיחות / Articles, PDFs, conversations
• "תמונות / Images" — תמונות, סריקות, דיאגרמות / Photos, scans, diagrams
• "אין לי דאטה / No data yet" — צריך למצוא או ליצור / Need to find or generate
• (Other: אודיו/audio, סדרות זמן/time series, וידאו/video, etc.)

Q4: "מה המטרה של הפרויקט? / What's the project goal?"

• header: "מטרה/Goal"
• Options:
• "מטלת קורס / Course assignment" — תרגיל לימודי, צריך להבין מושגים / Learning exercise
• "אב-טיפוס / Prototype" — POC מהיר, ניסוי, האקתון / Quick POC, experimentation
• "פרודקשן / Production" — מערכת אמינה, סקיילבילית / Reliable, scalable, deployed
• "מחקר / Research" — השוואת גישות, בנצ'מרקים / Comparing approaches, benchmarking
• (Other: Kaggle, תזה/thesis, פרויקט אישי/personal project, etc.)

Route Based on Answers

Language → Set response mode:

• עברית → All explanations in Hebrew, code comments in Hebrew (separate lines), Hebrew analogies
• English → All in English, Hebrew only for term translations
• Mixed → English code + Hebrew explanations and comments (separate lines, no RTL/LTR mixing)

Task + Data → Primary Skills:

| Task | Tabular | Text | Images | No Data | |------|---------|------|--------|---------| | סיווג/Classification | ml-fundamentals, ml-advanced | nlp-classical OR transformers-llm | cnn-vision | /find-dataset first | | רגרסיה/Regression | ml-fundamentals | sequence-models | cnn-vision | /find-dataset first | | NLP/טקסט | — | transformers-llm, rag-retrieval | cnn-vision (captioning) | /find-dataset first | | ראייה/Vision | — | — | cnn-vision, generative-models | /find-dataset first | | Other:RL | — | — | — | reinforcement-learning | | Other:Recommender | ml-advanced | — | — | /find-dataset first | | Other:Generative | — | transformers-llm | generative-models | generative-models |

Goal → Adjust depth + infer level:

• מטלת קורס / Course → Beginner-friendly: add ml-teaching-assistant, /explain-concept for each term, step-by-step
• אב-טיפוס / Prototype → Intermediate: minimal viable code, skip optimization, working pipeline
• פרודקשן / Production → Advanced: add mlops-experiment + model-interpretability + fine-tuning-peft
• מחקר / Research → Advanced: add mlops-experiment (tracking), model-interpretability (analysis)

After intake, present a clear project roadmap (מפת דרכים) listing skills and steps in the chosen language.

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2. Routing Engine - Detect Intent First

Intent → Action

| User Intent | Action | Example | |------------|--------|---------| | Learn / Understand | /explain-concept [topic] | "What is backpropagation?" | | Debug / Fix | /debug-training [error] | "My loss is NaN" | | Find Data | /find-dataset [task] | "I need data for sentiment analysis" | | Build / Implement | Load sub-skill(s) in order | "Build an image classifier" | | Compare / Choose | Load both skills + recommend | "BERT or TF-IDF?" | | Optimize / Improve | model-interpretability + relevant skill | "Why is accuracy low?" | | Deploy / Production | mlops-experiment + fine-tuning-peft | "Deploy model to production" |

Question Routing Patterns

"What is X?" / "Explain Y" / "How does Z work?"

1. Use /explain-concept [concept] for structured explanation
2. Also load relevant sub-skill for deeper context if needed

"How do I build X?" / "I want to create Y"

1. Does user have data? If not → start with /find-dataset [task]
2. Load primary sub-skill for the task
3. Load supporting skills (pytorch-mastery, deep-learning-core)
4. Follow 5-step ML workflow (Section 11)

"Error X" / "My model doesn't work" / "NaN loss"

1. Use /debug-training [error-description]
2. The ml-debugger agent handles systematic 4-phase debugging
3. Returns diagnosis with file:line references + corrected code

"Which is better: X or Y?" / "Should I use X?"

1. Load ml-teaching-assistant for decision framework
2. Load both relevant sub-skills for technical comparison
3. Provide comparison table + clear recommendation

Disambiguation - Multi-Skill Queries

When a query matches multiple skills, clarify with 1-2 questions:

"I want to classify text" → Ask:

• Data size? (5K → BERT)
• Need interpretability? (Yes → nlp-classical, No → transformers-llm)

"My training is slow" → Check:

• GPU issue? → pytorch-mastery (memory, DataLoader)
• Wrong architecture? → deep-learning-core (simplify model)
• Need profiling? → mlops-experiment (TensorBoard profiler)

"I want to work with images" → Ask:

• Classification? → cnn-vision
• Generation? → generative-models
• Captioning? → cnn-vision (multimodal)

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3. Task Skills - Quick Actions

/debug-training [error-description or file-path]

Invokes read-only ml-debugger agent with systematic 4-phase debugging. Auto-route when user says: "NaN loss", "shape mismatch", "CUDA out of memory", "accuracy stuck", "model doesn't converge", "training error", "low accuracy"

/explain-concept [concept-name]

8-step explanation: definition + Hebrew, analogy, ASCII diagram, steps, code, when to use, misconceptions, connections. Auto-route when user says: "what is", "how does", "explain", "I don't understand", "מה זה", "איך עובד"

/find-dataset [task-description]

5-step data sourcing: public datasets → synthetic generation → augmentation → zero-shot. Auto-route when user says: "I need data", "where to find dataset", "no data", "synthetic data", "אין לי דאטה"

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4. Sub-Skill Routing - By Use Case

| User wants to... | Primary Skill | Also Load | |------------------|---------------|-----------| | Predict numeric values (prices, scores) | ml-fundamentals | ml-advanced (ensembles) | | Classify categories (spam, churn) | ml-fundamentals | ml-advanced (XGBoost) | | Segment customers, find anomalies | ml-advanced | ml-fundamentals (features) | | Build recommendation engine | ml-advanced | pytorch-mastery, deep-learning-core | | Classify text (small data 5K) | transformers-llm | fine-tuning-peft | | Understand training fundamentals | deep-learning-core | pytorch-mastery | | Write PyTorch training code | pytorch-mastery | deep-learning-core | | Classify/detect in images | cnn-vision | pytorch-mastery | | Forecast time series | sequence-models | ml-fundamentals | | Use BERT / HuggingFace / LLMs | transformers-llm | fine-tuning-peft | | Build RAG / Q&A system | rag-retrieval | data-pipeline, transformers-llm | | Parse PDFs, call LLM APIs | data-pipeline | rag-retrieval | | Fine-tune LLM with LoRA/QLoRA | fine-tuning-peft | transformers-llm, mlops-experiment | | Track experiments, tune hyperparams | mlops-experiment | any modeling skill | | Explain predictions, debug errors | model-interpretability | ml-fundamentals | | Train RL agent | reinforcement-learning | pytorch-mastery | | Generate images (GAN/VAE/Diffusion) | generative-models | cnn-vision, pytorch-mastery | | Get concept explanation | ml-teaching-assistant | specific sub-skill | | Unsure which skill applies | ml-knowledge-index | (has A-Z topic index) |

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5. Sub-Skill Directory (17 Skills)

Foundation

• ml-fundamentals — Tabular ML: regression, classification, evaluation metrics, feature engineering, sklearn
• ml-advanced — Beyond basics: ensembles (XGBoost, CatBoost), clustering (K-Means, DBSCAN), PCA, recommender systems
• deep-learning-core — DL theory: training loop, loss functions, backprop, optimizers, regularization, autoencoders
• pytorch-mastery — Practical PyTorch: tensors, DataLoader, GPU memory, debugging shapes, environment setup

NLP & Language

• nlp-classical — Pre-transformer NLP: TF-IDF, Word2Vec, topic modeling, text similarity. Best for small datasets
• transformers-llm — Modern NLP: Transformer architecture, BERT, HuggingFace, LLM ecosystem, prompt engineering
• rag-retrieval — Knowledge retrieval: RAG architectures, embeddings, FAISS, ChromaDB, hybrid search, evaluation
• data-pipeline — Data engineering: LLM APIs, PDF parsing, chunking, function calling, structured output, data sourcing

Vision & Sequences

• cnn-vision — Computer vision: CNN architectures, transfer learning, augmentation, MNIST, multi-modal, captioning
• sequence-models — Sequential data: RNN, LSTM/GRU, time series forecasting, text generation

Advanced Deep Learning

• fine-tuning-peft — Efficient fine-tuning: LoRA, QLoRA, PEFT, quantization (GPTQ/AWQ/GGUF), DPO/RLHF alignment
• generative-models — Generative AI: GANs (DCGAN, WGAN), VAEs, Diffusion Models, Stable Diffusion
• reinforcement-learning — RL: Q-Learning, DQN, PPO, Actor-Critic, Gymnasium, Stable-Baselines3

Operations & Understanding

• mlops-experiment — ML operations: MLflow, W&B, TensorBoard, Optuna, model registry, experiment versioning
• model-interpretability — Explainability: SHAP, LIME, Grad-CAM, feature importance, error analysis pipeline

Meta Skills

• ml-knowledge-index — A-Z topic index mapping ANY question to the right sub-skill. Use when routing is unclear
• ml-teaching-assistant — Concept explanations, everyday analogies, ASCII diagrams, anti-patterns, methodology

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6. Cross-Skill Workflows

"Build an image classifier"

1. /find-dataset "image classification [domain]"  → Get data
2. cnn-vision/SKILL.md                            → Architecture, augmentation
3. pytorch-mastery/SKILL.md                       → Training loop, DataLoader
4. deep-learning-core/SKILL.md                    → Loss, regularization
5. model-interpretability/SKILL.md                → Grad-CAM visualization

"Build a RAG system"

1. data-pipeline/SKILL.md                         → PDF parsing, chunking
2. rag-retrieval/SKILL.md                         → Vector store, embeddings, RAG architecture
3. transformers-llm/SKILL.md                      → LLM selection, prompt engineering

"Classify text"

Decision tree:
Data size?
├── 5K           → transformers-llm (fine-tuned BERT)

Interpretability required?
├── Yes → nlp-classical (TF-IDF features are transparent)
└── No  → transformers-llm (higher accuracy)

"Fine-tune an LLM"

1. /find-dataset "instruction tuning data"        → Get or create dataset
2. fine-tuning-peft/SKILL.md                      → LoRA/QLoRA, SFTTrainer
3. transformers-llm/SKILL.md                      → Tokenization, HuggingFace Trainer
4. mlops-experiment/SKILL.md                      → Track experiments

"Customer segmentation"

1. /find-dataset "customer data"                  → Get data
2. ml-fundamentals/SKILL.md                       → EDA, feature engineering
3. ml-advanced/SKILL.md                           → K-Means, DBSCAN, PCA
4. model-interpretability/SKILL.md                → Cluster analysis

"Build a recommender system"

1. ml-advanced/SKILL.md                           → Matrix Factorization, NeuMF
2. pytorch-mastery/SKILL.md                       → Training loop, embeddings
3. deep-learning-core/SKILL.md                    → Loss functions, embedding layers

"My model isn't working"

1. /debug-training [error-description]            → Systematic 4-phase debugging
2. model-interpretability/SKILL.md                → Error analysis, SHAP
3. deep-learning-core/SKILL.md                    → Check loss, optimizer, architecture

"Generate images"

1. generative-models/SKILL.md                     → GAN/VAE/Diffusion selection
2. cnn-vision/SKILL.md                            → CNN layers, image processing
3. pytorch-mastery/SKILL.md                       → Training loop, GPU optimization

"Train an RL agent"

1. reinforcement-learning/SKILL.md                → Algorithm selection (DQN vs PPO)
2. pytorch-mastery/SKILL.md                       → Neural network for policy/value
3. mlops-experiment/SKILL.md                      → Track RL experiments

"Explain predictions / Debug errors"

1. model-interpretability/SKILL.md                → SHAP, LIME, Grad-CAM
2. ml-fundamentals/SKILL.md                       → Evaluation metrics, confusion matrix
3. ml-teaching-assistant/SKILL.md                 → Conceptual explanation

"Deploy model to production"

1. mlops-experiment/SKILL.md                      → Model registry, versioning
2. fine-tuning-peft/SKILL.md                      → Quantization for efficiency
3. data-pipeline/SKILL.md                         → API integration, structured output

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7. Hebrew Keyword Routing — מפת ניתוב בעברית

| Hebrew Term | English | Route To | |------------|---------|----------| | רגרסיה, קלסיפיקציה, סיווג | Regression, Classification | ml-fundamentals | | יער אקראי, XGBoost, אשכולות | Random Forest, Clustering | ml-advanced | | רשת נוירונים, למידה עמוקה | Neural network, Deep learning | deep-learning-core | | PyTorch, טנזורים, GPU | Tensors, GPU | pytorch-mastery | | עיבוד שפה טבעית, TF-IDF | NLP, TF-IDF | nlp-classical | | טרנספורמר, BERT, מודל שפה | Transformer, LLM | transformers-llm | | RAG, חיפוש סמנטי, וקטורים | RAG, Semantic search | rag-retrieval | | פרסור PDF, chunking, API | PDF parsing, APIs | data-pipeline | | CNN, ראייה ממוחשבת, תמונות | CNN, Computer vision | cnn-vision | | LSTM, RNN, סדרות זמן | Time series | sequence-models | | LoRA, כוונון עדין, קוונטיזציה | Fine-tuning, Quantization | fine-tuning-peft | | MLflow, ניסויים, היפר-פרמטרים | Experiments, Hyperparameters | mlops-experiment | | SHAP, הסבר מודל, פרשנות | Explainability | model-interpretability | | Q-Learning, חיזוק, PPO | Reinforcement learning | reinforcement-learning | | GAN, VAE, דיפוזיה, יצירת תמונות | Generative models | generative-models | | מערכת המלצות | Recommender system | ml-advanced | | אין לי דאטה, מאגר נתונים | No data, Dataset | /find-dataset | | שגיאה באימון, לא מתכנס | Training error | /debug-training | | מה זה X?, איך עובד

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Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: levy-n
• Source: levy-n/claude-useful-skills
• License: MIT

Install and usage instructions live in the source repository linked above.