histolab
Lightweight WSI tile extraction and preprocessing. Use for basic slide processing tissue detection, tile extraction, stain normalization for H&E images. Best for simple pipelines, dataset preparation, quick tile-based analysis. For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines use pathml.
npx skills add foryourhealth111-pixel/Vibe-Skills --skill histolab --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Histolab provides lightweight WSI tile extraction and preprocessing, including tissue detection, tile extraction, and stain normalization for H&E images. It is designed for basic slide processing, dataset preparation, and quick tile-based analysis, with guidance to use PathML for more advanced workflows.
How it works
The skill describes a Python library workflow: load a Slide, configure a tiler (RandomTiler, GridTiler, or ScoreTiler) with tile_size, level, and tissue checks; preview tile locations with locate_tiles; optionally apply tissue masks (e.g., TissueMask or BiggestTissueBoxMask); extract tiles using the chosen tiler; and optionally generate reports. It outlines tissue detection via masks, multiple extraction strategies, and preprocessing via filter pipelines (Compose with image and morphological filters). It also includes examples for sampling tiles, grid coverage, and quality-driven selection, plus workflows for multi-slide processing and custom tissue detection with user-defined masks.
When to use it
Use for basic WSI tile extraction, tissue detection, H&E tile preprocessing, and quick dataset preparation. It is specifically noted that advanced spatial proteomics, multiplexed imaging, or full PathML pipelines are outside this skill.
What it can touch
The skill references tools and modules such as:
Slide,TissueMask,BiggestTissueBoxMask,RandomTiler,GridTiler,ScoreTiler,NucleiScorerand other scorersTileand various filters viaCompose,RgbToGrayscale,OtsuThreshold,BinaryDilation,RemoveSmallHoles,RemoveSmallObjects- Visualization via
locate_mask()andlocate_tiles() - File-based outputs like
tiles_report.csvand figure generation for QC All commands and class names are quoted exactly as shown in the skill.
Caveats
The skill explicitly states that full computational pathology workflows, PathML pipelines, nucleus segmentation, spatial pathology, multiplex pathology, DICOM/IDC retrieval, OMERO server work, and generic image-processing tasks are outside this skill. It targets basic tile extraction and dataset preparation, and best practices emphasize previewing masks and tiles before extraction, as well as using appropriate masks for tissue selection. License is Apache-2.0. The skill includes multiple example workflows and workflows for custom tissue detection, with no guarantees about results beyond those stated (tile extraction and preprocessing steps).
# Histolab ## Overview Histolab is a Python library for processing whole slide images (WSI) in digital pathology. It automates tissue detection, extracts informative tiles from gigapixel images, and prepares datasets for deep learning pipelines. The library handles multiple WSI formats, implements sophisticated tissue segmentation, and provides flexible tile extraction strategies. ## Routing Bound
What does the histolab skill do?
Lightweight WSI tile extraction and preprocessing. Use for basic slide processing tissue detection, tile extraction, stain normalization for H&E images. Best for simple pipelines, dataset preparation, quick tile-based analysis. For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines use pathml.
How do I install it?
Run `npx skills add foryourhealth111-pixel/Vibe-Skills --skill histolab --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From foryourhealth111-pixel/Vibe-Skills, a repository with 2,593 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.