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Agent skill

Cellprofiler

by K-Dense-AI in K-Dense-AI/scientific-agent-skills

Runs reproducible CellProfiler microscopy pipelines for nuclear segmentation, cell counts, and per-object fluorescence measurements.

MITAuto-check passedResearch & Science

Install Cellprofiler

skills CLI
$ npx skills add K-Dense-AI/scientific-agent-skills --skill cellprofiler -a claude-code

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

GitHub CLI
$ gh skill install K-Dense-AI/scientific-agent-skills cellprofiler --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/K-Dense-AI/scientific-agent-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/cellprofiler .claude/skills/cellprofiler && 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/

Facts

Skill name
cellprofiler
GitHub stars
48k
Used in
1 other repo
Token cost
~1.9k tokens
SKILL.md length
835 words
Files
4 (incl. scripts, references, assets)
Skills in repo
153
Repo updated
First seen
Licence
MIT

At a glance

Runs reproducible CellProfiler microscopy pipelines for nuclear segmentation, cell counts, and per-object fluorescence measurements.

  • Works in 5 steps: Establish the acquisition unit: plate,… → Create the CSV manifest below.… → Use assets/nuclei.cppipe as a starting… → …
  • Research & Science work in your project
  • SKILL.md covers Workflow, Run the bounded assay, Interpret the outputs and Sources
  • Runs Python scripts from its folder; calls python

What it does

Cellprofiler is an agent skill from K-Dense-AI/scientific-agent-skills. Runs reproducible CellProfiler microscopy pipelines for nuclear segmentation, cell counts, and per-object fluorescence measurements. Supports image/channel manifests, headless batch execution, segmentation overlays, and measurement QC for 2D fluorescence assays.

Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts, reference files and assets (for example `references/runtime-and-qc.md` and `scripts/nuclei_assay.py`). Compatibility notes: Python 3.12+ with numpy and tifffile for current helper-only dependencies; a separate CellProfiler 4.2.8 application/container for segmentation. Full…

It sits in Research & Science. The repository describes itself as: Turn any AI agent into an AI Scientist. The 1 Agent Skills library for science, used by 250,000+ scientists worldwide. 177 ready-to-use validated skills plus 100+ scientific… The licence is MIT.

When your agent uses it

  • Research & Science work in your project

Example prompts

  • “/cellprofiler”

Requirements

  • Python 3
  • Compatibility (from SKILL.md): Python 3.12+ with numpy and tifffile for current helper-only dependencies; a separate CellProfiler 4.2.8 application/container for segmentation. Full CellProfiler has older native dependencies. Network access is needed for installation only. No credentials required.

Workflow steps

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

  1. Establish the acquisition unit: plate, well, site, time point if present, pixel size, nuclear
  2. Create the CSV manifest below. image_path is absolute or relative to the manifest; sample IDs use letters, digits, dots, dashes, or…
  3. Use assets/nuclei.cppipe as a starting pipeline: LoadData →
  4. Run a small pilot spanning controls, low/high density, dim images, and plate edges. Inspect
  5. Freeze the tuned pipeline and analyze the batch. Review input saturation warnings, zero

What it can do on your machine

Read from SKILL.md and the folder at commit 92ace75. 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 1 file in scripts/ (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):

    • cellprofiler.org
    • cellprofiler-manual.s3.amazonaws.com
    • pypi.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.

  • Compatibility

    Python 3.12+ with numpy and tifffile for current helper-only dependencies; a separate CellProfiler 4.2.8 application/container for segmentation. Full CellProfiler has older native dependencies. Network access is needed for installation only. No credentials required.

    From compatibility in the SKILL.md frontmatter.

Context cost

Cellprofiler loads about 1.9k tokens when it runs, and up to ~3.8k if it reads all its reference files. Until then it costs about 69 tokens; SKILL.md has 835 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~69
When it runs · the whole SKILL.md, loaded when a task matches
~1.9k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.8k

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); the scripts in this folder are not scanned.

SKILL.md

The full file from K-Dense-AI/scientific-agent-skills at commit 92ace75, republished under its MIT licence (© K-Dense-AI). 835 words, ~1,923 tokens.

Download SKILL.mdSave it as .claude/skills/cellprofiler/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
cellprofiler
description
Runs reproducible CellProfiler microscopy pipelines for nuclear segmentation, cell counts, and per-object fluorescence measurements. Supports image/channel manifests, headless batch execution, segmentation overlays, and measurement QC for 2D fluorescence assays.
compatibility
Python 3.12+ with numpy and tifffile for current helper-only dependencies; a separate CellProfiler 4.2.8 application/container for segmentation. Full CellProfiler has older native dependencies. Network access is needed for installation only. No credentials required.
license
MIT
metadata.version
1.1
metadata.skill-author
K-Dense Inc.
metadata.upstream-version
4.2.8
metadata.last-reviewed
2026-09-30

CellProfiler quantitative microscopy

Use this skill when a user needs a repeatable CellProfiler .cppipe, nuclear counts, nuclear fluorescence, or batch microscopy measurements. The bundled assay accepts one 2D grayscale TIFF nuclear channel per field, with black-is-zero (MINISBLACK) pixels and bright nuclei on a dark background. Palette and white-is-zero TIFFs need an explicit conversion. For volumetric segmentation, multichannel cell painting, or tissue-specific models, design a separate pipeline and validate those assumptions rather than silently projecting or splitting the images.

The official application and manual remain 4.2.8. PyPI publishes 4.2.8.1; its seven modules used here and embedded Threshold module match the 4.2.8 source, but this review did not execute that native distribution. Keep the helper environment separate from CellProfiler's older dependency stack; see the runtime reference for the verification boundary.

Workflow

  1. Establish the acquisition unit: plate, well, site, time point if present, pixel size, nuclear channel identity, camera bit depth, exposure, and biological replicate. Keep original image intensities. Convert proprietary formats explicitly with Bio-Formats before using this helper.
  2. Create the CSV manifest below. image_path is absolute or relative to the manifest; sample IDs use letters, digits, dots, dashes, or underscores; sample IDs and plate/well/site combinations are unique. Use a nonnumeric sample ID such as sample_001: LoadData infers column types and can otherwise turn 001 into 1. Avoid surrounding whitespace in identifiers. TIFFs must be uint8 or uint16, single plane/series/resolution, and nonconstant. The helper rejects RGB, z-stacks, and float images rather than guessing channels.
  3. Use assets/nuclei.cppipe as a starting pipeline: LoadData → IdentifyPrimaryObjects → intensity/size measurements → outline overlay → CSV export. The initial diameter range is 8–80 pixels, with global Otsu thresholding, no threshold smoothing, and border objects excluded. Calibrate this range from representative images and acquisition pixel size before comparing conditions.
  4. Run a small pilot spanning controls, low/high density, dim images, and plate edges. Inspect saved overlays for missed nuclei, splits, merges, and edge exclusions. Adjust thresholding and declumping in CellProfiler, export the tuned .cppipe, and pass --pipeline to preserve it. Do not choose settings separately for each treatment to make their counts agree.
  5. Freeze the tuned pipeline and analyze the batch. Review input saturation warnings, zero counts, count/area distributions, and control behavior. Aggregation for inference belongs at the biological replicate level; thousands of cells from one well are not independent wells.

Run the bounded assay

From this skill directory, create images.csv:

csv
sample_id,image_path,plate,well,site
control_A01_1,images/control_A01_1_DAPI.tif,Plate1,A01,1
bash
python scripts/nuclei_assay.py prepare images.csv load_data.csv
python scripts/nuclei_assay.py run images.csv results --executable cellprofiler
python scripts/nuclei_assay.py summarize results

run requires a fresh/empty output directory and executes CellProfiler with -c -r, a saved pipeline copy, --data-file, output folder, and --done-file. Success requires exit code zero, a Complete marker, and valid measurement tables. It records the command, pipeline checksum, input image checksums, and sample QC in assay_qc.json before execution, retaining failed status and the error if execution or output validation fails. CellProfiler output goes to cellprofiler.log. Rerun in a new output folder. summarize checks CSV contents independently; it does not prove an engine run completed.

Custom pipelines must preserve DNA, Nuclei, Metadata_Sample, integer-dtype scaling, and the unprefixed single-object Image.csv/Nuclei.csv export contract. Keep the required intensity/area measurements. A renamed object set or different intensity scale needs a corresponding helper adaptation, not an unchecked --pipeline substitution.

The executable can also be the CellProfiler application launcher or a local container launcher; see references/runtime-and-qc.md for the container target, filesystem mapping, and verification evidence. prepare and summarize work without CellProfiler.

Show full SKILL.md (288 more words)Show less

Interpret the outputs

  • Image.csv: one image/field row, including Count_Nuclei and acquisition metadata.
  • Nuclei.csv: one accepted object per row, with mean/integrated DNA intensity, area, and shape.
  • *_nuclei.png: green nuclear boundaries over the input image for visual QC.
  • pipeline.cppipe and cellprofiler.done: the exact pipeline copy and engine completion marker.
  • assay_qc.json: run status, unique image/object keys, exact counts, finite mean/integrated intensity and positive area checks, field mean area in pixels, and storage saturation flags.

LoadData ignores camera metadata for scaling in this asset and divides by the integer storage maximum: uint8 → 255, uint16 → 65535. A 12-bit camera stored in uint16 therefore has a maximum near 0.0625. Do not compare intensities across different bit depths, exposures, gains, or staining batches without an explicit calibration. A saturated image can pass segmentation while its intensity measurement is unusable. Illumination correction and background subtraction are assay-specific additions; this starter does neither.

The saturation fraction only counts pixels at the storage maximum. A 12-bit detector may saturate at 4095 while the uint16 storage maximum is 65535; inspect the known acquisition ceiling separately. Integrated intensity sums pixel values and may exceed 1; only per-pixel mean intensity is constrained to 0–1. The field's mean nuclear intensity weights each nucleus equally, rather than weighting each pixel equally.

A count check cannot prove correct segmentation. Inspect overlays and independently annotated fields; report boundary exclusions and segmentation errors alongside the biological result. The optional synthetic engine test targets a known three-nucleus example, not assay performance on unseen cell types. It was skipped in the current review because no engine was configured.

Sources

© K-Dense-AI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (scripts, references, assets) in skills/cellprofiler of K-Dense-AI/scientific-agent-skills.

  • SKILL.md
  • assets/nuclei.cppipe
  • references/runtime-and-qc.md
  • scripts/nuclei_assay.py

Open the folder on GitHubat commit 92ace75

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 K-Dense-AI/scientific-agent-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

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Categories

Questions about Cellprofiler

What does Cellprofiler do?

Runs reproducible CellProfiler microscopy pipelines for nuclear segmentation, cell counts, and per-object fluorescence measurements. Cellprofiler is an agent skill from K-Dense-AI/scientific-agent-skills. Runs reproducible CellProfiler microscopy pipelines for nuclear segmentation, cell counts, and per-object fluorescence measurements.

When should I use Cellprofiler?

Cellprofiler fits situations like: research & Science work in your project.

How do I install Cellprofiler in Claude Code?

Run `npx skills add K-Dense-AI/scientific-agent-skills --skill cellprofiler -a claude-code`. Or copy the skill folder (skills/cellprofiler in K-Dense-AI/scientific-agent-skills) into .claude/skills/cellprofiler in your project. Claude Code loads it when a task matches its description.

How do I install Cellprofiler in Codex?

Run `npx skills add K-Dense-AI/scientific-agent-skills --skill cellprofiler -a codex`. Or copy the skill folder (skills/cellprofiler in K-Dense-AI/scientific-agent-skills) into .agents/skills/cellprofiler in your project. Codex loads it when a task matches its description.

Can I use Cellprofiler 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 K-Dense-AI/scientific-agent-skills --skill cellprofiler -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/cellprofiler, .gemini/skills/cellprofiler, .github/skills/cellprofiler and .opencode/skills/cellprofiler in your project.

What does Cellprofiler need to run?

Going by SKILL.md and its folder, Cellprofiler needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3. Compatibility (from SKILL.md): Python 3.12+ with numpy and tifffile for current helper-only dependencies; a separate CellProfiler 4.2.8 application/container for segmentation. Full CellProfiler has older native dependencies. Network access is needed for installation only. No credentials required..

Does Cellprofiler access the network?

SKILL.md names 3 domains. As links in the text: cellprofiler.org, cellprofiler-manual.s3.amazonaws.com and pypi.org. This is read from the text; nothing was executed.

Is Cellprofiler 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Cellprofiler use?

Cellprofiler 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 Cellprofiler use?

About 1.9k tokens (SKILL.md is roughly 7.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.9k tokens, read only when the agent opens those files.

What are the alternatives to Cellprofiler?

Skills that share tags, products or a category with Cellprofiler: Hypothesis Generation (spacering-net/codeg, 3.9k stars), GitHub Deep Research (bytedance/deer-flow, 84k stars), Nature Paper Card (Yuan1z0825/nature-skills, 47k stars) and Content Research Writer (weapp-tailwindcss/weapp-tailwindcss, 1.9k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Cellprofiler?

K-Dense-AI (a GitHub organization) maintains it in K-Dense-AI/scientific-agent-skills, which has 48,095 GitHub stars. The repository holds 153 skills in this directory. The repository was last updated on October 5, 2026.

Source: K-Dense-AI/scientific-agent-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.