feat(skills): add environment-management skill

Conda + uv hybrid environment setup for ML projects.

.opencode/skills/environment-management/SKILL.md

@@ -0,0 +1,224 @@
+---
+name: environment-management
+description: Use when setting up Python environment for ML/DL paper replication using Conda + uv
+---
+
+# Environment Management (Conda + uv)
+
+## Overview
+
+Hybrid approach using Conda for system-level dependencies and uv for project isolation.
+
+**Announce at start:** "I'm using the environment-management skill for Conda + uv setup."
+
+## Architecture
+
+```
+┌─────────────────────────────────────────┐
+│           Conda (System Base)           │
+│  - Python interpreter                    │
+│  - CUDA toolkit                          │
+│  - System-level C++ libraries            │
+└─────────────────────────────────────────┘
+                    │
+                    │ provides Python
+                    ▼
+┌─────────────────────────────────────────┐
+│        uv (Project Isolation)           │
+│  - Per-project .venv                     │
+│  - Fast dependency resolution            │
+│  - Reproducible installs                 │
+└─────────────────────────────────────────┘
+```
+
+## Setup Commands
+
+### Step 1: Conda Base Environment
+
+Check if base exists:
+```bash
+conda env list | grep ai_base
+```
+
+Create if needed:
+```bash
+# Linux/Mac
+conda create -n ai_base python=3.10 cuda-toolkit=11.8 -y
+
+# Windows (CUDA from NVIDIA, not conda)
+conda create -n ai_base python=3.10 -y
+```
+
+### Step 2: Project Environment
+
+```bash
+cd workspace/{paper_name}
+
+# Get Conda Python path
+# Linux/Mac:
+PYTHON_PATH=$(conda run -n ai_base which python)
+
+# Windows:
+# PYTHON_PATH=$(conda run -n ai_base python -c "import sys; print(sys.executable)")
+
+# Create uv venv
+uv venv --python $PYTHON_PATH
+```
+
+### Step 3: Activate and Install
+
+```bash
+# Linux/Mac
+source .venv/bin/activate
+
+# Windows
+.venv\Scripts\activate
+
+# Install dependencies
+uv pip install -e ".[dev]"
+```
+
+## pyproject.toml Template
+
+```toml
+[project]
+name = "{paper_name}"
+version = "0.1.0"
+description = "Replication of {paper_title}"
+requires-python = ">=3.10"
+
+dependencies = [
+    # Core ML
+    "torch>=2.0.0",
+    "numpy>=1.24.0",
+    
+    # Visualization
+    "matplotlib>=3.7.0",
+    "seaborn>=0.12.0",
+    
+    # Utilities
+    "tqdm>=4.65.0",
+    "pyyaml>=6.0",
+]
+
+[project.optional-dependencies]
+dev = [
+    "pytest>=7.0.0",
+    "pytest-cov>=4.0.0",
+    "black>=23.0.0",
+    "ruff>=0.0.260",
+]
+
+# Add based on paper requirements
+vision = [
+    "torchvision>=0.15.0",
+    "pillow>=9.5.0",
+]
+
+nlp = [
+    "transformers>=4.30.0",
+    "tokenizers>=0.13.0",
+    "datasets>=2.12.0",
+]
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[tool.pytest.ini_options]
+testpaths = ["tests"]
+python_files = ["test_*.py"]
+addopts = "-v --tb=short"
+
+[tool.black]
+line-length = 88
+target-version = ["py310"]
+
+[tool.ruff]
+line-length = 88
+select = ["E", "F", "I", "N", "W"]
+```
+
+## PyTorch + CUDA Compatibility
+
+| CUDA Version | PyTorch Version | Install Command |
+|--------------|-----------------|-----------------|
+| 11.8 | 2.0+ | `uv pip install torch --index-url https://download.pytorch.org/whl/cu118` |
+| 12.1 | 2.1+ | `uv pip install torch --index-url https://download.pytorch.org/whl/cu121` |
+| CPU only | Any | `uv pip install torch --index-url https://download.pytorch.org/whl/cpu` |
+
+## Environment Verification
+
+```bash
+# Check Python
+python --version
+
+# Check PyTorch
+python -c "import torch; print(f'PyTorch {torch.__version__}')"
+
+# Check CUDA
+python -c "import torch; print(f'CUDA available: {torch.cuda.is_available()}')"
+python -c "import torch; print(f'CUDA version: {torch.version.cuda}')"
+
+# Check GPU
+python -c "import torch; print(f'GPU: {torch.cuda.get_device_name(0) if torch.cuda.is_available() else \"N/A\"}')"
+```
+
+## Troubleshooting
+
+### CUDA Not Found
+
+```bash
+# Check NVIDIA driver
+nvidia-smi
+
+# Reinstall PyTorch with correct CUDA
+uv pip install torch --index-url https://download.pytorch.org/whl/cu118 --force-reinstall
+```
+
+### Dependency Conflicts
+
+```bash
+# Clear cache and reinstall
+uv cache clean
+uv pip install -e ".[dev]" --force-reinstall
+```
+
+### Permission Errors (Windows)
+
+```powershell
+# Run as Administrator or:
+Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser
+```
+
+## Best Practices
+
+1. **One environment per paper**: Don't mix dependencies
+2. **Pin versions in pyproject.toml**: For reproducibility
+3. **Use dev dependencies**: Keep test tools separate
+4. **Document CUDA version**: In README.md
+5. **Commit pyproject.toml**: Not .venv/
+
+## Quick Reference
+
+```bash
+# Full setup sequence (Linux/Mac)
+conda activate ai_base || conda create -n ai_base python=3.10 cuda-toolkit=11.8 -y && conda activate ai_base
+cd workspace/{paper_name}
+uv venv --python $(which python)
+source .venv/bin/activate
+uv pip install -e ".[dev]"
+pytest tests/ -v
+```
+
+```powershell
+# Full setup sequence (Windows)
+conda activate ai_base
+# If not exists: conda create -n ai_base python=3.10 -y && conda activate ai_base
+cd workspace\{paper_name}
+$env:PYTHON_PATH = (conda run -n ai_base python -c "import sys; print(sys.executable)")
+uv venv --python $env:PYTHON_PATH
+.venv\Scripts\activate
+uv pip install -e ".[dev]"
+pytest tests\ -v
+```