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Agent skill

Bio Flow Cytometry Fcs Handling

by GPTomics in GPTomics/bioSkills

Reads, inspects, and writes Flow Cytometry Standard (FCS) files from conventional, spectral, and mass cytometry (CyTOF), and parses FlowJo/Cytobank/Diva workspaces.

MITAuto-check passedResearch & Science

Install Bio Flow Cytometry Fcs Handling

skills CLI
$ npx skills add GPTomics/bioSkills --skill bio-flow-cytometry-fcs-handling -a claude-code

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

GitHub CLI
$ gh skill install GPTomics/bioSkills bio-flow-cytometry-fcs-handling --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/GPTomics/bioSkills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/flow-cytometry/fcs-handling .claude/skills/bio-flow-cytometry-fcs-handling && 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
bio-flow-cytometry-fcs-handling
GitHub stars
1.2k
Used in
1 other repo
Token cost
~2.5k tokens
SKILL.md length
876 words
Files
4
Skills in repo
559
Repo updated
First seen
Licence
MIT

At a glance

Reads, inspects, and writes Flow Cytometry Standard (FCS) files from conventional, spectral, and mass cytometry (CyTOF), and parses FlowJo/Cytobank/Diva workspaces.

  • Mass cytometry data
  • SKILL.md covers Version Compatibility, The Single Most Important…, FCS Standard Internals (what… and Reader Taxonomy, plus 8 more sections
  • Runs R and Python scripts from its folder; calls pip
  • Mapping detector channels to antibodies

What it does

Bio Flow Cytometry Fcs Handling is an agent skill from GPTomics/bioSkills. Reads, inspects, and writes Flow Cytometry Standard (FCS) files from conventional, spectral, and mass cytometry (CyTOF), and parses FlowJo/Cytobank/Diva workspaces. Covers FCS 2.0/3.0/3.1/3.2 internals ($PnE linear-vs-log, $DATATYPE, $SPILLOVER vs SPILL vs $COMP, $TIMESTEP), channel/parameter metadata, the silent linearize/truncate defaults, and R (flowCore, flowWorkspace, CytoML) plus Python (FlowKit, readfcs) readers. Use when loading flow or mass cytometry data, mapping detector channels to antibodies…

Its SKILL.md is about 2.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files (for example `examples/load_fcs.py` and `usage-guide.md`).

It sits in Research & Science, covering Bioinformatics. It works with Python, AnnData and Scanpy. The repository describes itself as: a set of SKILLS.md for doing bioinformatics with agents like claude code. The licence is MIT.

When your agent uses it

  • Mass cytometry data
  • Mapping detector channels to antibodies
  • Extracting the event matrix
  • Choosing a reader

Example prompts

  • “/bio-flow-cytometry-fcs-handling”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit d91ed3d. 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 (R and Python), which the agent can run.

    Shell commands in SKILL.md call:

    • pip

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.

    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

Bio Flow Cytometry Fcs Handling loads about 2.5k tokens when it runs. Until then it costs about 165 tokens; SKILL.md has 876 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~165
When it runs · the whole SKILL.md, loaded when a task matches
~2.5k

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 GPTomics/bioSkills at commit d91ed3d, republished under its MIT licence (© GPTomics). 876 words, ~2,453 tokens.

Download SKILL.mdSave it as .claude/skills/bio-flow-cytometry-fcs-handling/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
bio-flow-cytometry-fcs-handling
description
Reads, inspects, and writes Flow Cytometry Standard (FCS) files from conventional, spectral, and mass cytometry (CyTOF), and parses FlowJo/Cytobank/Diva workspaces. Covers FCS 2.0/3.0/3.1/3.2 internals ($PnE linear-vs-log, $DATATYPE, $SPILLOVER vs SPILL vs $COMP, $TIMESTEP), channel/parameter metadata, the silent linearize/truncate defaults, and R (flowCore, flowWorkspace, CytoML) plus Python (FlowKit, readfcs) readers. Use when loading flow or mass cytometry data, mapping detector channels to antibodies, extracting the event matrix, choosing a reader, or bridging FCS to the scanpy/AnnData ecosystem before preprocessing.
tool_type
mixed
primary_tool
flowCore

Version Compatibility

Reference examples tested with: flowCore 2.14+, flowWorkspace 4.14+, CytoML 2.14+; Python flowkit 1.1+, readfcs 1.1+.

Before using code patterns, verify installed versions match. If versions differ:

  • R: packageVersion('<pkg>') then ?function_name to verify parameters
  • Python: pip show <package> then help(module.function) to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

FCS File Handling

"Load my FCS files and inspect the channels" -> Parse FCS format into event matrix + parameter metadata, map detector channels to antibodies, and choose a reader appropriate to the instrument and downstream ecosystem.

  • R: flowCore::read.FCS() / read.flowSet() -> flowFrame/flowSet; CytoML::flowjo_to_gatingset() for FlowJo workspaces
  • Python: flowkit.Sample() (full workflow) or readfcs.read() -> AnnData (scanpy/scverse bridge)

The Single Most Important Modern Insight -- read.FCS Silently Transforms by Default

flowCore::read.FCS() defaults to transformation = "linearize", which APPLIES the $PnE log-amplification scaling on read. Two pipelines reading "the same raw FCS" (flowCore default vs fcsparser/transformation=FALSE) therefore return different numbers, and a compensation matrix computed on one will silently mismatch the other. For any preprocessing pipeline that will compensate and transform downstream, read with transformation = FALSE (or NULL) to get the genuinely raw values, and set truncate_max_range = FALSE so out-of-$PnR events (common on CyTOF and some digital instruments) are not silently clipped. Decide the read settings deliberately; they are not nuisance defaults.

FCS Standard Internals (what the keywords mean)

KeywordMeaningDecision-relevant nuance
$PnEamplification type "decades,offset""0,0" = linear; FCS 3.1 FORBIDS log-stored floats (a float param must be "0,0"); log $PnE survives only on legacy integer analog-log data
$DATATYPEI (uint) / F (float) / D (double) / A (ASCII, deprecated 3.1)FCS 3.2 allows MIXED types per parameter via $PnDATATYPE (integer Time + float fluorescence)
$PnRparameter rangefor integers defines the bit mask via next power of two ($PnR=1024 -> 10-bit), NOT a value clamp
$SPILLOVERstandardized compensation matrix (3.1+)digital BD instruments wrote non-standard SPILL (no $); 3.0 $COMP stored a matrix WITHOUT naming parameters (ambiguous -> why $SPILLOVER exists)
$TIMESTEPseconds per Time-channel unitthe master axis for all time-based QC; missing/wrong $TIMESTEP silently breaks flow-rate/drift checks

FCS standards: 3.0 (Seamer 1997 Cytometry 28:118), 3.1 (Spidlen 2010 Cytometry A 77:97), 3.2 (Spidlen 2021 Cytometry A 99:100). Area/Height/Width = pulse integral/peak/duration; FSC-A vs FSC-H is the doublet axis. CyTOF channels are <Metal><Mass>Di (e.g. Yb176Di) and report dual counts (pulse-counting at low signal, intensity at high).

Reader Taxonomy

ReaderLanguageWhat it doesWhen to use
flowCore::read.FCS/read.flowSetRcore FCS -> flowFrame/flowSetthe default for any R/Bioconductor pipeline
flowWorkspace GatingSetRgated hierarchy containerwhen carrying gates/populations
CytoMLRFlowJo (wsp) / Cytobank / Diva import-exportround-tripping a manual analysis (Finak 2018 Cytometry A 93:1189)
flowkit (Session/Sample)PythonFCS + GatingML 2.0 + FlowJo wsp + compensation/transformsPython pipelines, FlowJo interop (White 2021 Front Immunol 12:768541)
readfcsPythonFCS -> AnnDatabridge to scanpy/scverse and the single-cell categories
fcsparser / FlowCalPythonlow-level reader / reader + MEF calibrationquick parse; FlowCal for MESF/MEF work

Load and Inspect FCS (R)

Goal: Read one file (or a directory) raw, inspect parameters, and map channels to antibodies.

Approach: Read with transformation=FALSE, truncate_max_range=FALSE; the channel->antibody map lives in pData(parameters(fcs)) (name = detector, desc = antibody).

r
library(flowCore)

fcs <- read.FCS('sample.fcs', transformation = FALSE, truncate_max_range = FALSE)
params <- pData(parameters(fcs))          # name (detector), desc (antibody), range, minRange
channel_map <- setNames(params$desc, params$name)

fs <- read.flowSet(list.files('data', pattern = '\\.fcs$', full.names = TRUE),
                   transformation = FALSE, truncate_max_range = FALSE)
expr <- exprs(fcs)                         # cells x channels
Show full SKILL.md (363 more words)Show less

Access the Compensation Matrix from Keywords

Goal: Retrieve the acquisition-recorded spillover matrix, handling the three keyword conventions.

Approach: Try $SPILLOVER, then the legacy SPILL, then $COMP; flowCore::spillover() resolves the standard slots.

r
kw <- keyword(fcs)
spill <- kw$`$SPILLOVER`
if (is.null(spill)) spill <- kw$SPILL          # digital BD convention
if (is.null(spill)) spill <- kw$`$COMP`        # legacy FCS 3.0 (unnamed columns)

Load FCS in Python (FlowKit / readfcs)

Goal: Read FCS in a Python pipeline, either for FlowKit's compensation/gating or as an AnnData for scanpy.

Approach: flowkit.Sample exposes raw/compensated/transformed events as DataFrames; readfcs.read returns AnnData with channels in var.

python
import flowkit as fk
import readfcs

sample = fk.Sample('sample.fcs')
events = sample.as_dataframe(source='raw')   # source in {'raw','comp','xform'}

adata = readfcs.read('sample.fcs')           # AnnData; adata.var has channel + antibody names

Rename Channels, Subset, Write, Annotate Samples

Goal: Standardize channel names to antibodies and attach sample-level metadata for downstream tools.

Approach: Replace blank desc with name; attach a pData table keyed by sampleNames(fs) (CATALYST/diffcyt require this).

r
new <- ifelse(is.na(params$desc) | params$desc == '', params$name, params$desc)
colnames(fcs) <- new

fcs_markers <- fcs[, c('CD4', 'CD8', 'CD3')]          # subset channels
write.FCS(fcs, 'out.fcs')

pData(fs) <- data.frame(name = sampleNames(fs),
                        condition = c('Control','Control','Treatment','Treatment'),
                        patient = c('P1','P2','P1','P2'),
                        row.names = sampleNames(fs))

Per-Method Failure Modes

Silent log-linearization on read

Trigger: read.FCS('x.fcs') with default args. Mechanism: transformation="linearize" applies $PnE scaling. Symptom: values differ from fcsparser; compensation matrix mismatch. Fix: transformation = FALSE.

Out-of-range clipping

Trigger: instrument wrote values above $PnR (common CyTOF). Mechanism: truncate_max_range=TRUE (default) clamps them. Symptom: a ceiling artifact at the channel max. Fix: truncate_max_range = FALSE.

Channel names break formulas

Trigger: channels like FSC-A, Pacific Blue-A. Mechanism: hyphens/spaces are not syntactic R names. Symptom: formula/gating errors. Fix: alter.names = TRUE on read.

FlowJo parsing in the wrong package

Trigger: looking for FlowJo import in flowWorkspace. Mechanism: parsing lives in CytoML. Symptom: function-not-found. Fix: CytoML::open_flowjo_xml() -> flowjo_to_gatingset(); only .wsp (FlowJo 10+), not legacy .jo.

Common Errors

Error / symptomCauseSolution
exprs() numbers differ across toolsdefault linearizeread with transformation=FALSE everywhere
spillover keyword is NULLinstrument used SPILL/$COMPtry all three keyword names
editing exprs(ff) corrupts rangesdirect reassignment skips parameters() updateuse transform/Subset workflows
readfcs compensation not appliedmatrix names don't match var_namesalign channel names before relying on it

References

  • Seamer 1997 Cytometry 28(2):118-122 — FCS 3.0 standard.
  • Spidlen 2010 Cytometry A 77(1):97-100 — FCS 3.1 standard.
  • Spidlen 2021 Cytometry A 99(1):100-102 — FCS 3.2 standard.
  • Finak 2018 Cytometry A 93(12):1189-1196 — CytoML cross-platform gating import/export.
  • White 2021 Front Immunol 12:768541 — FlowKit Python toolkit.
  • Lee 2008 Cytometry A 73(10):926-930 — MIFlowCyt minimum reporting standard.
  • compensation-transformation - Compensate and transform after loading
  • cytometry-qc - Assess acquisition quality on the loaded data
  • gating-analysis - Define populations from the loaded GatingSet
  • clustering-phenotyping - Unsupervised analysis of the event matrix
  • single-cell/data-io - readfcs bridges FCS to the AnnData/scanpy ecosystem
  • imaging-mass-cytometry/data-preprocessing - Shared metal-channel and FCS conventions

© GPTomics, 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 in flow-cytometry/fcs-handling of GPTomics/bioSkills.

  • SKILL.md
  • examples/load_fcs.R
  • examples/load_fcs.py
  • usage-guide.md

Open the folder on GitHubat commit d91ed3d

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 GPTomics/bioSkills, which our catalogue first saw on October 7, 2026.

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Works with

Categories

Questions about Bio Flow Cytometry Fcs Handling

What does Bio Flow Cytometry Fcs Handling do?

Reads, inspects, and writes Flow Cytometry Standard (FCS) files from conventional, spectral, and mass cytometry (CyTOF), and parses FlowJo/Cytobank/Diva workspaces. Bio Flow Cytometry Fcs Handling is an agent skill from GPTomics/bioSkills. Reads, inspects, and writes Flow Cytometry Standard (FCS) files from conventional, spectral, and mass cytometry (CyTOF), and parses FlowJo/Cytobank/Diva workspaces.

When should I use Bio Flow Cytometry Fcs Handling?

Bio Flow Cytometry Fcs Handling fits situations like: mass cytometry data; mapping detector channels to antibodies; extracting the event matrix; choosing a reader.

How do I install Bio Flow Cytometry Fcs Handling in Claude Code?

Run `npx skills add GPTomics/bioSkills --skill bio-flow-cytometry-fcs-handling -a claude-code`. Or copy the skill folder (flow-cytometry/fcs-handling in GPTomics/bioSkills) into .claude/skills/bio-flow-cytometry-fcs-handling in your project. Claude Code loads it when a task matches its description.

How do I install Bio Flow Cytometry Fcs Handling in Codex?

Run `npx skills add GPTomics/bioSkills --skill bio-flow-cytometry-fcs-handling -a codex`. Or copy the skill folder (flow-cytometry/fcs-handling in GPTomics/bioSkills) into .agents/skills/bio-flow-cytometry-fcs-handling in your project. Codex loads it when a task matches its description.

Can I use Bio Flow Cytometry Fcs Handling 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 GPTomics/bioSkills --skill bio-flow-cytometry-fcs-handling -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/bio-flow-cytometry-fcs-handling, .gemini/skills/bio-flow-cytometry-fcs-handling, .github/skills/bio-flow-cytometry-fcs-handling and .opencode/skills/bio-flow-cytometry-fcs-handling in your project.

What does Bio Flow Cytometry Fcs Handling need to run?

Going by SKILL.md and its folder, Bio Flow Cytometry Fcs Handling needs R and Python for the scripts in its folder and the command-line tools its instructions call (pip). Our summary lists: Python 3.

Does Bio Flow Cytometry Fcs Handling access the network?

SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Bio Flow Cytometry Fcs Handling 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 Bio Flow Cytometry Fcs Handling use?

Bio Flow Cytometry Fcs Handling is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Bio Flow Cytometry Fcs Handling use?

About 2.5k tokens (SKILL.md is roughly 9.8k 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 Bio Flow Cytometry Fcs Handling?

Skills that share tags, products or a category with Bio Flow Cytometry Fcs Handling: Anndata (davila7/claude-code-templates, 33k stars), Scanpy (K-Dense-AI/scientific-agent-skills, 48k stars), Anndata (K-Dense-AI/scientific-agent-skills, 48k stars) and Bio Single Cell Data Io (FreedomIntelligence/OpenClaw-Medical-Skills, 3.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Bio Flow Cytometry Fcs Handling?

GPTomics (a GitHub organization) maintains it in GPTomics/bioSkills, which has 1,218 GitHub stars. The repository holds 559 skills in this directory. The repository was last updated on August 15, 2026.

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