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🔬 Cell Segmentation · clawbio | DecimalAI

scienceby clawbio

🔬 Cell Segmentation

clawbio/cell-detection

Source clawbio/clawbio ↗License MIT

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.

SkillScore82Maintained

Automatic

Research pack

Last verified 8/16/2026

What it changes

Passed 16 of 20 test cases with the skill, and 4 of 20 without it.

Without skill18%

With skill82%

Checks it passed that the model alone missed

Identifies report.md in the root output directory
Includes --do_3D flag in the command line invocation
Refrains from writing custom image processing scripts

and 17 more in the full report

14 fixed4 passed either way4 still failing0 broke

Pass-rate lift: +64 pp
Tokens: +54%
Agent turns: 0%
Response time: -47%

Model gemini-3.6-flashMeasured 8/16/2026v118 of 22 cases passed with skill · 20 comparable

Tokens, turns and time describe resource use, not a dollar price.

Use this skill

Save it into your project's skills folder. Claude Code picks it up in its next session.

curl -fsSL --create-dirs -o .claude/skills/clawbio-cell-detection/SKILL.md https://app.decimal.ai/s/clawbio-cell-detection/SKILL.md

Want it pinned and updatable? pip install "decimalai>=0.15", then decimalai skills add clawbio-cell-detection, and start Claude Code with claude --plugin-dir "$(git rev-parse --show-toplevel)/.agents"

1 · One-time — install the CLI (docs ↗)

pip install "decimalai>=0.15"

2 · Get the skill — writes .agents/skills/clawbio-cell-detection/ and pins it in .decimal/skills.lock

decimalai skills add clawbio-cell-detection

No CLI? Get the same file — curl · raw file

curl -fsSL --create-dirs -o .claude/skills/clawbio-cell-detection/SKILL.md https://app.decimal.ai/s/clawbio-cell-detection/SKILL.md

Raw file: /s/clawbio-cell-detection/SKILL.md ↗ · machine-readable summary at /s/clawbio-cell-detection.json ↗

Safety checks: 3 of 3 passed

Code scan CleanIntent PassedContent Clean

See it in action

One of 13 test cases the skill fixed. Same model, same prompt, with and without the skill.

1 of 13

Prompt

If I pass a multi-plane Z-stack stack_image.tif to the segmentation tool without specifying --z_projection none or --do_3D, how is the Z dimension handled by default?

What was checked, without and with the skill
Checked	Without skill	With skill
States that max projection over Z is the default projection policy	Missed	Passed

case-15 · gemini-3.6-flash · 8/16/2026 · answers are capped and redacted

What's inside

The instructions your model reads when the skill loads.

SKILL.md164 linessingle file

🔬 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

Track record

Benchmark

16 / 20

test cases passed, vs 4 / 20 without

Adoption

Users: 0
Times used: 0

Safety and source details

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/3/2026

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

clawbio / clawbio

skills/cell-detection/SKILL.md

View on GitHub ↗

License MITImported 8/3/2026Version v1

More skills from the same source and nearby in the registry, with their headline benchmark lift.

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See it in action

What's inside

🔬 Cell Segmentation

Why This Exists

Core Capabilities

Input Formats

Workflow

Track record

Benchmark

Adoption

SkillSafety

Source

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