add coding agent description files

CLAUDE.md

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+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## Quick Commands
+
+### Python Projects
+- **Install dependencies**: `uv sync` (root) or `uv sync` in a subdirectory with its own `pyproject.toml`
+- **Run linter**: `ruff check .` (includes all files via `extend-include`)
+- **Auto-fix**: `ruff check . --fix`
+- **Format imports**: `ruff format .`
+
+### R Projects
+- **Load project**: RStudio/.Rprofile uses `renv` for isolation
+- **Check style**: `lintr::lint("script.R")`
+- **Format code**: `styler::style_file("script.R")`
+
+### SQL (M2/SQL)
+```bash
+docker compose -f M2/SQL/docker-compose.yml up -d
+make tp1       # Execute TP1.sql
+make tp2       # Execute TP2.sql
+make tp3       # Execute TP3.sql
+make project   # Execute DANJOU_Arthur.sql
+```
+
+## Project Structure
+
+```
+L3/                    # Bachelor's degree (3rd year)
+M1/                    # Master's degree (1st year)
+M2/                    # Master's degree (2nd year)
+└── <Course>/          # e.g., "Deep Learning", "Data Visualisation"
+    ├── TP{n}/         # Practical work (numbered)
+    ├── Project/       # Final project
+    └── data/          # Course-specific data
+```
+
+## Python Conventions
+
+- **Package manager**: `uv` (workspace configured at root)
+- **Linting**: Ruff with strict rules (`select = ["ALL"]`)
+- **Import ordering**: Custom sections in `pyproject.toml`:
+  - `data-science`: numpy, pandas, scipy, matplotlib, seaborn, plotly
+  - `ml`: tensorflow, keras, torch, sklearn, xgboost, catboost, shap
+- **Reproducibility**: Use `np.random.seed(42)` for random seeds
+- **Notebooks**: Jupyter with descriptive markdown cells
+
+## R Conventions
+
+- **Package management**: `renv` (autoloading via `.Rprofile`)
+- **Linting**: `lintr` configured in `.lintr`
+- **Documents**: RMarkdown (`.Rmd`) for reproducible reports
+- **Visualization**: ggplot2, plotly, FactoMineR
+
+## Key Technologies
+
+- **Data Science**: numpy, pandas, scipy, matplotlib, seaborn, plotly, geopandas
+- **Machine Learning**: scikit-learn, xgboost, catboost, tensorflow, keras, shap
+- **LLM/RAG**: langchain, sentence-transformers, faiss-cpu
+- **R**: tidyverse, ggplot2, FactoMineR, caret, glmnet, RShiny
+
+## Notes
+
+- Some subprojects (e.g., `M2/Reinforcement Learning/project/`) have isolated `pyproject.toml` files
+- Large datasets are not versioned—download via notebook code when needed
+- Course materials and documentation are primarily in French