Agent skill

Cell Detection

Cell segmentation in fluorescence microscopy images. An agent skill from ClawBio/ClawBio.

MITAuto-check passedAI & LLM Engineering

Install Cell Detection

skills CLI

$ npx skills add ClawBio/ClawBio --skill cell-detection -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install ClawBio/ClawBio cell-detection --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/ClawBio/ClawBio.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/cell-detection .claude/skills/cell-detection && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "cell-detection" agent skill from https://github.com/ClawBio/ClawBio/tree/main/skills/cell-detection into .claude/skills/cell-detection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cell-detection", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/ClawBio/ClawBio/tree/main/skills/cell-detection

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add ClawBio/ClawBio --skill cell-detection -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install ClawBio/ClawBio cell-detection --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/ClawBio/ClawBio.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/cell-detection .agents/skills/cell-detection && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "cell-detection" agent skill from https://github.com/ClawBio/ClawBio/tree/main/skills/cell-detection into .agents/skills/cell-detection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cell-detection", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add ClawBio/ClawBio --skill cell-detection -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install ClawBio/ClawBio cell-detection --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/ClawBio/ClawBio.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/cell-detection .cursor/skills/cell-detection && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "cell-detection" agent skill from https://github.com/ClawBio/ClawBio/tree/main/skills/cell-detection into .cursor/skills/cell-detection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cell-detection", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/ClawBio/ClawBio.git --path skills/cell-detection

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add ClawBio/ClawBio --skill cell-detection -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install ClawBio/ClawBio cell-detection --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/ClawBio/ClawBio.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/cell-detection .gemini/skills/cell-detection && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "cell-detection" agent skill from https://github.com/ClawBio/ClawBio/tree/main/skills/cell-detection into .gemini/skills/cell-detection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cell-detection", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install ClawBio/ClawBio cell-detection

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add ClawBio/ClawBio --skill cell-detection -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/ClawBio/ClawBio.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/cell-detection .github/skills/cell-detection && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "cell-detection" agent skill from https://github.com/ClawBio/ClawBio/tree/main/skills/cell-detection into .github/skills/cell-detection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cell-detection", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add ClawBio/ClawBio --skill cell-detection -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install ClawBio/ClawBio cell-detection --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/ClawBio/ClawBio.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/cell-detection .opencode/skills/cell-detection && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "cell-detection" agent skill from https://github.com/ClawBio/ClawBio/tree/main/skills/cell-detection into .opencode/skills/cell-detection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cell-detection", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: cell-detection
GitHub stars: 1.2k
Used in: 1 other repo
Token cost: ~2.1k tokens
SKILL.md length: 738 words
Files: 4
Skills in repo: 104
Repo updated: Oct 7, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Cell segmentation in fluorescence microscopy images. An agent skill from ClawBio/ClawBio.

Works in 4 steps: Segment: Run cpsam on TIFF, CZI, ND2,… → Measure: Extract area, equivalent… → Report: Produce report.md,… → …
AI & LLM Engineering work in your project
SKILL.md covers Why This Exists, Core Capabilities, Input Formats and Workflow, plus 10 more sections
Runs Python scripts from its folder; calls python

What it does

Cell Detection is an agent skill from ClawBio/ClawBio. 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.

Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files (for example `cell_detection.py` and `tests/test_cell_detection.py`).

It sits in AI & LLM Engineering. The repository describes itself as: 🦖 ClawBio - The first bioinformatics-native AI agent skill library. Local-first. Reproducible. Open. Free. The licence is MIT.

When your agent uses it

AI & LLM Engineering work in your project

Example prompts

“/cell-detection”

Requirements

Python 3

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

Segment: Run cpsam on TIFF, CZI, ND2, PNG, or JPG fluorescence images
Measure: Extract area, equivalent diameter, centroid, and eccentricity per cell
Report: Produce report.md, {stem}_measurements.csv, and histogram figures
Execution control: GPU auto by default, with explicit --use_gpu / --use_cpu override flags

What it can do on your machine

Read from SKILL.md and the folder at commit 5e045e3. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships script files (Python), which the agent can run.
Shell commands in SKILL.md call:
- python
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
- doi.org
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Cell Detection loads about 2.1k tokens when it runs. Until then it costs about 68 tokens; SKILL.md has 738 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~68
When it runs · the whole SKILL.md, loaded when a task matches: ~2.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from ClawBio/ClawBio at commit 5e045e3, republished under its MIT licence (© ClawBio). 738 words, ~2,079 tokens.

Download SKILL.mdSave it as .claude/skills/cell-detection/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.

name: cell-detection
description: 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.
license: MIT
metadata.version: 0.1.0
metadata.author: ClawBio
metadata.tags: microscopy, segmentation, cellpose, fluorescence, imaging, cell-biology

🔬 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

Segment: Run cpsam on TIFF, CZI, ND2, PNG, or JPG fluorescence images
Measure: Extract area, equivalent diameter, centroid, and eccentricity per cell
Report: Produce report.md, {stem}_measurements.csv, and histogram figures
Execution control: GPU auto by default, with explicit --use_gpu / --use_cpu override flags

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 or fluorescence, any order
>3-channel TIFF	`.tif`, `.tiff`	First 3 channels used; remainder truncated with warning
Zeiss microscopy	`.czi`	Reads CZI via `czifile` and uses CZI axis metadata (`CziFile.axes`) to map C/Z/Y/X deterministically
Nikon microscopy	`.nd2`	Reads ND2 via `nd2` and uses ND2 named dimensions (`ND2File.sizes`) for deterministic C/Z/Y/X mapping
PNG / JPEG	`.png`, `.jpg`, `.jpeg`	Greyscale or RGB

Channel handling: cpsam is channel-order invariant for 2D inputs — cytoplasm and nuclear channels can be in any order. For 2D segmentation, if you have more than 3 channels, the first 3 are used and the rest are truncated with a warning. For 3D segmentation (--do_3D) with --z_projection none, 4D stacks are preserved as Z×C×Y×X (no channel truncation at load time).

Workflow

Load image; detect greyscale vs multi-channel
Prepare
- 2D mode: pass 1–3 channels through unchanged; truncate >3 to first 3 with a warning
- 3D mode (--do_3D + --z_projection none): keep 4D volume as Z×C×Y×X
Segment with CellposeModel()
- 2D mode: no explicit channel mapping needed
- 3D multichannel mode: call with z_axis=0, channel_axis=1
- Device mode: defaults to GPU-auto; --use_cpu forces CPU
Metrics via skimage.measure.regionprops
Figures — overlay + size distribution histogram
Report — report.md + {stem}_measurements.csv + reproducibility bundle (commands.sh, environment.yml, checksums.sha256)

CLI Reference

bash

# Standard usage — greyscale or multi-channel (cpsam handles channels automatically)
python skills/cell-detection/cell_detection.py \
  --input <image.tif> --output <report_dir>

# Override diameter estimate (pixels)
python skills/cell-detection/cell_detection.py \
  --input <image.tif> --diameter 30 --output <report_dir>

# Demo (synthetic image, no user file needed)
python skills/cell-detection/cell_detection.py --demo --output /tmp/cell_detection_demo

# Override 4D stack Z handling (default is max projection)
python skills/cell-detection/cell_detection.py \
  --input <image.nd2> --z_projection none --do_3D --output <report_dir>

# Force CPU mode
python skills/cell-detection/cell_detection.py \
  --input <image.tif> --use_cpu --output <report_dir>

Demo

bash

python skills/cell-detection/cell_detection.py --demo --output /tmp/cell_detection_demo

Expected output: report.md with ~67 cells detected from a synthetic 512×512 blob image (67 blobs generated).

Show full SKILL.md (356 more words)Show less

Algorithm / Methodology

Load image with tifffile (TIFF), czifile (CZI), nd2 (ND2), or PIL (PNG/JPG); use CZI/ND2 metadata axes to assign C/Z/Y/X
Channel preparation:
- 2D mode: if >3 channels, truncate to first 3 with a warning
- 3D mode with --z_projection none: preserve 4D volume as Z×C×Y×X
Instantiate CellposeModel(gpu=<flag>)
Call model.eval(img, diameter=<arg_or_None>)
- 2D: no channels/channel_axis needed (cpsam is channel-order invariant)
- 3D Z×C×Y×X: pass z_axis=0, channel_axis=1
Extract per-cell stats from masks via skimage.measure.regionprops
Save {stem}_measurements.csv, figures, report.md

Key parameters:

Model: cpsam (Cellpose 4.0 unified model — channel-order invariant)
Channels:
- 2D: channel-order invariant; first 3 channels are used when input has >3 channels
- 3D with --z_projection none: multichannel 4D stacks are kept as Z×C×Y×X
Diameter: None triggers Cellpose auto-estimation
4D stack policy:
- --z_projection max (default): max-project over Z while preserving channels for 2D segmentation (H×W×C)
- --z_projection none: preserve Z; 4D stacks remain volumetric (Z×C×Y×X) for 3D segmentation
3D guardrails:
- --do_3D requires volumetric input (Z×Y×X or Z×C×Y×X)
- non-volumetric input with --do_3D falls back to 2D mode when safe, otherwise errors

Notes

Measurements are reported in pixel units (px, px²). Physical calibration metadata (um/pixel) is not currently propagated into per-cell metrics.
For volumetric segmentation outputs, outlines PNG is replaced with a note file ({stem}_cp_outlines_unavailable.txt) because Cellpose does not emit 3D outlines PNGs.

Example Queries

"Segment the cells in my DAPI image"
"How many cells are in this microscopy image?"
"Run cellpose on my TIFF and give me a cell count"
"Segment my fluorescence image and export morphology metrics"

Output Structure

output_dir/
├── report.md
├── {stem}_measurements.csv
├── {stem}_cp_masks.tif
├── {stem}_seg.npy
├── figures/
│   ├── {stem}_cp_outlines.png
│   └── {stem}_histogram.png
└── reproducibility/
    ├── checksums.sha256
    ├── commands.sh
    └── environment.yml

Dependencies

cellpose>=4.0 — cpsam model
tifffile — TIFF I/O
czifile>=2019.7.2.2 — Zeiss CZI I/O (manually verified with 2019.7.2.2)
nd2>=0.11.1 — Nikon ND2 I/O (manually verified with 0.11.1)
Pillow — PNG/JPG loading
numpy — array ops
matplotlib — figures
scikit-image — regionprops metrics

Safety

Local-first: no image data leaves the machine
Every report includes the ClawBio medical disclaimer
Reproducibility bundle (commands.sh, environment.yml, checksums.sha256) records the exact invocation, dependencies, and output integrity

Integration with Bio Orchestrator

Trigger conditions:

Input is a TIFF/PNG/JPG microscopy image
User mentions "cellpose", "segment", "cell counting", "microscopy"

Chaining partners:

Future: export ROI centroids to spatial transcriptomics workflows

Citations

Pachitariu, Rariden & Stringer (2025) Cellpose-SAM: superhuman generalization for cellular segmentation. bioRxiv 2025.04.28.651001 — CellposeSAM / cpsam model

Files

SKILL.md and 3 other files in skills/cell-detection of ClawBio/ClawBio.

SKILL.md
cell_detection.py
requirements.txt
tests/test_cell_detection.py

Open the folder on GitHubat commit 5e045e3

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in ClawBio/ClawBio, which our catalogue first saw on October 7, 2026.

BioTender-max/awesome-bio-agent-skillsskills/clawbio/cell-detection

Compare with similar skills

Cell Detection next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Questions about Cell Detection

What does Cell Detection do?

Cell segmentation in fluorescence microscopy images. An agent skill from ClawBio/ClawBio. Cell Detection is an agent skill from ClawBio/ClawBio. Cell segmentation in fluorescence microscopy images.

When should I use Cell Detection?

Cell Detection fits situations like: AI & LLM Engineering work in your project.

How do I install Cell Detection in Claude Code?

Run `npx skills add ClawBio/ClawBio --skill cell-detection -a claude-code`. Or copy the skill folder (skills/cell-detection in ClawBio/ClawBio) into .claude/skills/cell-detection in your project. Claude Code loads it when a task matches its description.

How do I install Cell Detection in Codex?

Run `npx skills add ClawBio/ClawBio --skill cell-detection -a codex`. Or copy the skill folder (skills/cell-detection in ClawBio/ClawBio) into .agents/skills/cell-detection in your project. Codex loads it when a task matches its description.

Can I use Cell Detection in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ClawBio/ClawBio --skill cell-detection -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/cell-detection, .gemini/skills/cell-detection, .github/skills/cell-detection and .opencode/skills/cell-detection in your project.

What does Cell Detection need to run?

Going by SKILL.md and its folder, Cell Detection needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Cell Detection access the network?

SKILL.md names 1 domain. As links in the text: doi.org. This is read from the text; nothing was executed.

Is Cell Detection safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Cell Detection use?

Cell Detection is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Cell Detection use?

About 2.1k tokens (SKILL.md is roughly 8.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Cell Detection?

Skills that share tags, products or a category with Cell Detection: Agent Builder (shareAI-lab/learn-claude-code, 78k stars), Add Uint Support (pytorch/pytorch, 104k stars), Peft Fine Tuning (Orchestra-Research/AI-Research-SKILLs, 13k stars) and Segment Anything Model Guide (Orchestra-Research/AI-Research-SKILLs, 13k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Cell Detection?

ClawBio (a GitHub organization) maintains it in ClawBio/ClawBio, which has 1,154 GitHub stars. The repository holds 104 skills in this directory. The repository was last updated on October 7, 2026.

Source: ClawBio/ClawBio on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Cell Detection

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

🔬 Cell Segmentation

Why This Exists

Core Capabilities

Input Formats

Workflow

CLI Reference

Demo

Algorithm / Methodology

Notes

Example Queries

Output Structure

Dependencies

Safety

Integration with Bio Orchestrator

Citations

Files

Used in 1 other repository

Compare with similar skills

Similar skills

More from ClawBio/ClawBio

Related

Questions about Cell Detection