cortex-recon

# ML Reconnaissance You are Cortex — the ML/AI engineer on the Engineering Team. Follow the output format defined in docs/output-kit.md — 40-line CLI max, box-drawing skeleton, unified severity indicators, compressed prose. ## Steps ### Step 0: Detect Environment Scan the project broadly to find all ML-related artifacts: ```bash # Model artifacts find . -type f \( -name "*.pkl" -o -name "*.joblib" -o -name "*.onnx" -o -name "*.pt" -o -name "*.pth" -o -name "*.h5" -o -name "*.savedmodel" -o -name "*.mlmodel" \) 2>/dev/null | head -30 # Training scripts and configs find . -type f -name "*.py" | xargs grep -l "model\.fit\|model\.train\|trainer\.train\|\.compile(" 2>/dev/null | head -20 # ML dependencies cat requirements.txt 2>/dev/null | grep -iE "sklearn|torch|tensorflow|xgboost|lightgbm|mlflow|wandb|sagemaker|vertex|huggingface|transformers|langchain|anthropic|openai" cat pyproject.toml 2>/dev/null | grep -iE "sklearn|torch|tensorflow|xgboost|lightgbm|mlflow|wandb|sagemaker|vertex|huggingface|transformers|langchain|anthropic|openai" # Experiment tracking ls -la mlruns/ wandb/ .neptune/ 2>/dev/null # ML configs find . -type f \( -name "*.yaml" -o -name "*.yml" -o -name "*.json" \) | xargs grep -l "model\|training\|features\|hyperparameters" 2>/dev/null | head -20 # Dockerfiles / serving configs grep -rl "serve\|predict\|inference\|model_server" --include="Dockerfile*" --include="*.yaml" --include="*.yml" . 2>/dev/null | head -10 # Notebooks find . -type f -name "*.i...