cellpose-cell-segmentation SKILL.md skillA
cellpose-cell-segmentation from jaechang-hits/sciagent-skills is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add jaechang-hits/sciagent-skills/cellpose-cell-segmentation@git:20260428.81ea2d2Pins git:20260428.81ea2d2 in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- jaechang-hits/sciagent-skills
- Path
skills/cell-biology/cellpose-cell-segmentation/SKILL.md- Versions recorded
- 2
- Latest version
- git:20260428.81ea2d2, committed Apr 28, 2026
- First version recorded
- Feb 18, 2026
- Size
- 14,183 bytes
- Repository stars
- 381
- License
- a license file GitHub does not identify
DL cell/nucleus segmentation for fluorescence and brightfield microscopy. Pre-trained models (cyto3, nuclei, tissuenet) and a generalist flow-based algorithm segment cells without retraining. Outputs label masks for morphology and tracking. Use scikit-image watershed for rule-based; Cellpose when DL generalization across staining is needed.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# Cellpose — Deep Learning Cell Segmentation ## Overview Cellpose uses a flow-based neural network to segment individual cells or nuclei in fluorescence microscopy images without manual parameter tuning. Pre-trained models (`cyto3`, `nuclei`, `tissuenet`) generalize across cell types, magnifications, and staining conditions — eliminating the need for manual threshold selection or watershed parameter optimization. Cellpose outputs integer label masks (each cell = unique integer) compatible with scikit-image `regionprops` for morphology measurement and with TrackPy for tracking. A built-in diameter estimator removes the need to specify cell size, though providing an approximate diameter improves accuracy. ## When to Use - Segmenting cells or nuclei in fluorescence microscopy images where rule-based thresholding fails due to varying intensity or cell touching - Processing large microscopy datasets in batch without per-image parameter tuning - Segmenting diverse cell types (adherent cells, blood cells, bacteria, organoids) with a single model - Producing label masks for downstream region property measurement (area, intensity, shape) with scikit-image …
Read the whole file at its exact version.
How to install
mdr add jaechang-hits/sciagent-skills/cellpose-cell-segmentation@sha256:b6459be1780e57c4The line at the top of this page pins the label git:20260428.81ea2d2, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_zv6zmykyr2yop5xh)
4 badge image fetches in 30 days. What the badge shows.
Versions
The registry has recorded 2 versions of this file, between Feb 18, 2026 and Apr 28, 2026, about one every 69 days.
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| git:20260428.81ea2d2 latest | 2026-04-28 | 81ea2d2 | 14,183 B | A | view · diff |
| git:20260218.8df1694 | 2026-02-18 | 8df1694 | 14,308 B | A | view |
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (14183 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
jaechang-hits/sciagent-skills · 381 stars · license a license file GitHub does not identify · pushed 2026-10-16 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_zv6zmykyr2yop5xh GET https://markdownregistry.com/api/v1/resolve?ref=jaechang-hits/sciagent-skills/cellpose-cell-segmentation GET https://markdownregistry.com/api/v1/blob/b6459be1780e57c4021781836b68e75205ab75cdefed995fb107fb0a6162ce3f
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.