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name: wsi-preprocessing description: "Prepare CLAM whole-slide-image masks, coordinate patch bags, process lists, segmentation presets, and legacy patching decisions." disable-model-invocation: true metadata: disco-role: operating license: GPL 3.0

WSI Preprocessing

Use this sub-skill when a user needs CLAM whole-slide-image preprocessing before feature extraction: segment tissue masks, tune a process_list CSV, create fast coordinate .h5 patch files, optionally stitch QC images, or author segmentation preset CSVs.

Start Here

• Read references/workflows.md to choose the one-pass, two-pass mask review, process-list rerun, preset, or legacy workflow and assemble concrete create_patches_fp.py commands.
• Read references/parameters.md when tuning segmentation, contour filtering, visualization, patching, process_list, --patch_level, --preset, or --no_auto_skip behavior.
• Run scripts/clam_command_builder.py --help when you want a safe command/output-layout builder that validates preprocessing options without importing CLAM or touching WSI files.
• Run scripts/build_preset_template.py --help when you need to print or write a CLAM preset CSV with the expected columns outside the source repository.
• Read references/troubleshooting.md when OpenSlide fails, slides are skipped, masks look poor, preset columns are wrong, output files are missing, or legacy patching risks storage blowups.
• Read references/legacy-pipeline.md only when the user explicitly needs saved image patches from the old create_patches.py pipeline.

Boundaries

This sub-skill owns WSI segmentation, fast coordinate patch generation, preset authoring, process-list editing, output layout checks, and legacy-pipeline triage. For coordinate .h5 feature extraction, route to ../feature-extraction/SKILL.md; for dataset CSVs, splits, training, and evaluation, route to ../training-evaluation/SKILL.md.