---

name: feature-extraction description: "Convert CLAM patch coordinate files into feature .h5 and .pt artifacts with ResNet50, UNI, or CONCH encoders." disable-model-invocation: true metadata: disco-role: operating license: GPL 3.0

Feature Extraction

Use this sub-skill when a task is about turning CLAM patch-coordinate .h5 files into slide-level feature bags, choosing resnet50_trunc, uni_v1, or conch_v1, setting checkpoint environment variables, or aligning downstream embed_dim values with extracted features.

Route First

• If the user still needs WSI segmentation, patch coordinates, process lists, or presets, read ../wsi-preprocessing/SKILL.md first.
• If the user already has feature .pt files and is configuring splits, training, evaluation, or model checkpoints, read ../training-evaluation/SKILL.md.
• If the user is configuring attention heatmaps or visualizing trained CLAM checkpoints, read ../heatmap-visualization/SKILL.md.
• If the user asks why feature extraction failed, start with references/troubleshooting.md, then inspect the workflow or encoder reference that matches the symptom.

Reference Map

• Read references/workflows.md for the fast extract_features_fp.py workflow, required inputs, command syntax, output layout, and downstream data_root_dir expectations.
• Read references/encoder-reference.md to choose between resnet50_trunc, uni_v1, and conch_v1, including checkpoint variables, feature dimensions, and batch-size implications.
• Read references/legacy-pipeline.md only for older saved-patch .h5 projects that used extract_features.py instead of the current fast coordinate-based pipeline.
• Read references/troubleshooting.md to diagnose coordinate-file, csv_path, slide-extension, OpenSlide, checkpoint, auto-skip, batch-size, and embed_dim failures.
• Run scripts/clam_feature_command_builder.py before a heavy extraction job to validate safe CLI options and print an extract_features_fp.py command plus expected outputs without opening slides, loading checkpoints, or downloading weights.

Default Workflow

1. Confirm data_h5_dir/patches/<slide_id>.h5 exists from the fast CLAM patching workflow and contains coordinate data rather than saved image patches.
2. Confirm csv_path has a slide_id column whose values match both coordinate files and slide files after applying --slide_ext.
3. Pick an encoder in references/encoder-reference.md; set UNI_CKPT_PATH or CONCH_CKPT_PATH before using UNI or CONCH.
4. Use the bundled command builder to produce a safe command and inspect naming assumptions.
5. Run CLAM feature extraction with extract_features_fp.py, then point downstream training/evaluation at the parent feature root described in references/workflows.md.