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flowio skill

by K-Dense-AI·K-Dense-AI/scientific-agent-skills·47k stars·MIT

Read, inspect, and write Flow Cytometry Standard (FCS) 2.0, 3.0, and 3.1 files with FlowIO. Use for low-level FCS metadata and channel inspection, NumPy event extraction, multi-dataset files, table export, and FCS 3.1 creation; use FlowKit for compensation, cytometry transforms, gating, or FlowJo workspaces.

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Install the flowio skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description. 

git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills.git /tmp/scientific-agent-skills
mkdir -p ~/.claude/skills
cp -r /tmp/scientific-agent-skills/skills/flowio ~/.claude/skills/flowio
available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

FlowIO

Purpose

Use FlowIO as a lightweight, low-level reader and writer for Flow Cytometry Standard files. Examples in this skill target FlowIO 1.4.0, the current stable release verified on 2026-07-23.

FlowIO is appropriate for:

  • Reading FCS 2.0, 3.0, and 3.1 files
  • Inspecting HEADER, TEXT, ANALYSIS, and channel metadata
  • Retrieving event data as a two-dimensional NumPy array
  • Reading legacy files that contain multiple datasets
  • Writing list-mode, single-precision FCS 3.1 files
  • Preparing data for pandas, machine-learning, or downstream cytometry tools

FlowIO does not perform compensation, logicle/biexponential transforms, gating, clustering, or FlowJo workspace processing. Use FlowKit or another analysis package for those tasks.

Install

Create or activate a Python environment, then install the verified release:

uv pip install "flowio==1.4.0"

Confirm the runtime version:

uv run python -c "import flowio; print(flowio.__version__)"

FlowIO 1.4.0 supports Python 3.9 through 3.13 and depends on NumPy.

Operating Workflow

file repair, conversion, and downstream biological analysis.

  1. Clarify the operation. Distinguish metadata inventory, event extraction,

work, especially with large or unfamiliar files.

  1. Inspect before loading events. Use only_text=True for metadata-only

gain/log/time scaling from FCS metadata, or preprocess=False for values as encoded in the DATA segment. Record the choice.

  1. Choose event semantics explicitly. Use as_array(preprocess=True) for

errors. Relax checks only for a known vendor-format defect, and review the resulting event data.

  1. Keep parsing strict by default. Do not automatically suppress offset

sample, subject, operator, and instrument identifiers. Export only fields needed for the task.

  1. Treat metadata as potentially sensitive. FCS TEXT values can include

metadata, and representative values after any FCS export.

  1. Validate writes by reopening them. Check event/channel counts, labels,

Critical Semantics

TEXT keys are normalized

FlowData.text stores keys in lowercase and strips the leading $ from standard FCS keywords:

from flowio import FlowData

flow = FlowData("sample.fcs", only_text=True)
acquisition_date = flow.text.get("date")
instrument = flow.text.get("cyt")
next_dataset = int(flow.text.get("nextdata", "0"))

Do not look up "$DATE", "$CYT", or other uppercase dollar-prefixed keys. TEXT values remain strings. FlowIO 1.4.0 also removes every $ character from the decoded TEXT segment, including $ characters inside values; preserve the original file when exact metadata fidelity matters.

Events have two representations

float64 array.

  • flow.events is the unprocessed, flattened one-dimensional event array.
  • flow.asarray() returns shape (eventcount, channel_count) as a NumPy

scaling. It does not apply compensation or logicle/biexponential display transforms.

  • flow.as_array(preprocess=True) applies FCS gain, logarithmic, and time

those scaling steps.

  • flow.as_array(preprocess=False) reshapes the encoded event values without

as_array() creates another in-memory array. FlowIO does not provide chunked or memory-mapped event access.

Channel numbering uses two conventions

zero-based indices.

  • NumPy columns and fluoroindices, scatterindices, and time_index use

nullchannellist, including supplied labels that were not found.

  • flow.channels uses FCS parameter numbers beginning at 1.
  • null_channels contains the PnN label strings supplied through

labels appear as empty strings.

  • pnslabels always matches pnnlabels in length; missing optional PnS

Writing is intentionally limited

create_fcs() requires:

  • An already-open binary file handle
  • Flattened one-dimensional event data in row-major event/channel order
  • One PnN name per channel
  • Optional PnS names and string-valued metadata via metadata_dict

It writes FCS 3.1 list-mode ($MODE=L) single-precision float ($DATATYPE=F) data. Required interpretation keywords are generated by FlowIO and cannot be overridden through metadata.

Quick Start: Read an FCS File

from pathlib import Path

from flowio import FlowData

flow = FlowData(Path("sample.fcs"))
events = flow.as_array(preprocess=True)

print(
    {
        "version": flow.version,
        "events": flow.event_count,
        "channels": flow.channel_count,
        "shape": events.shape,
        "pnn": flow.pnn_labels,
        "pns": flow.pns_labels,
        "date": flow.text.get("date"),
        "instrument": flow.text.get("cyt"),
    }
)

For metadata only:

from flowio import FlowData

flow = FlowData("sample.fcs", only_text=True)
print(flow.version, flow.event_count, flow.pnn_labels)

Do not call as_array() on a metadata-only instance because its event data was not loaded.

Prefer a path or Path over a caller-owned file handle. FlowData closes a provided handle after parsing. In FlowIO 1.4.0, readmultipledata_sets(handle) can fail after the first dataset because the handle has been closed; pass a filesystem path for multi-dataset files.

Quick Start: Read Multiple Datasets

Use the standalone helper rather than manually interpreting $NEXTDATA offsets:

from flowio import read_multiple_data_sets

datasets = read_multiple_data_sets("legacy-multi-dataset.fcs")
for index, dataset in enumerate(datasets):
    values = dataset.as_array(preprocess=True)
    print(index, dataset.event_count, dataset.pnn_labels, values.shape)

The FCS 3.1 specification deprecated multiple datasets in one file, but FlowIO can read legacy files that use them.

Quick Start: Create an FCS 3.1 File

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