cell-detection
Cell segmentation in fluorescence microscopy images. Supports Cellpose/cpsam (Cellpose 4.0) with additional backends planned. Produces segmentation masks, per-cell morphology metrics (area, diameter, centroid, eccentricity), overlay figures, and a report.md.
npx skills add BioTender-max/awesome-bio-agent-skills --skill cell-detection --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.
# 🔬 Cell Segmentation You are the **cell-detection** agent, a specialised ClawBio skill for cell segmentation in fluorescence microscopy images. The default backend is `cpsam` (Cellpose 4.0); additional backends (e.g. StarDist) are planned. ## Why This Exists Manual cell counting and segmentation are slow, inconsistent, and hard to reproduce. - **Without it**: Users open ImageJ, draw ROIs by hand, export CSVs with no provenance. - **With it**: One command segments cells, extracts morphology metrics, saves an overlay figure, and writes a reproducible `report.md`. - **Why ClawBio**: Fully local, no data upload, structured outputs ready for downstream analysis. ## Core Capabilities 1. **Segment**: Run `cpsam` on any TIFF, PNG, or JPG fluorescence image 2. **Measure**: Extract area, equivalent diameter, centroid, and eccentricity per cell 3. **Report**: Produce `report.md`, `{stem}_measurements.csv`, and histogram figures ## Input Formats | Format | Extension | Notes | |--------|-----------|-------| | Greyscale TIFF | `.tif`, `.tiff` | H×W — passed directly | | 2-channel TIFF | `.tif`, `.tiff` | H×W×2 — cytoplasm + nuclear, any order | | 3-channel TIFF | `.tif`, `.tiff` | H×W×3 — H&E
- Why This Exists
- Core Capabilities
- Input Formats
- Workflow
- CLI Reference
- Demo
- Algorithm / Methodology
- Example Queries
- Output Structure
- Dependencies
- Safety
- Integration with Bio Orchestrator
- Citations
Standard usage — greyscale or multi-channel (cpsam handles channels automatically) python skills/cell-detection/cell_detection.py \ Override diameter estimate (pixels) Demo (synthetic image, no user file needed) python skills/cell-detection/cell_detection.py --demo --output /tmp/cell_detection_demo
What does the cell-detection skill do?
Cell segmentation in fluorescence microscopy images. Supports Cellpose/cpsam (Cellpose 4.0) with additional backends planned. Produces segmentation masks, per-cell morphology metrics (area, diameter, centroid, eccentricity), overlay figures, and a report.md.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill cell-detection --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 BioTender-max/awesome-bio-agent-skills, a repository with 135 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.