flowio

Parse FCS 2.0-3.1 flow cytometry files into event arrays and metadata.

74|5|Updated Dec 10, 2025
One-click install
npx skills add https://github.com/dralkh/seerai --skill flowio-dralkh
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: flowio
Source: https://github.com/dralkh/seerai/tree/main/skills/flowio
Command: npx skills add https://github.com/dralkh/seerai --skill flowio-dralkh

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill removes the friction of reading and cleaning flow cytometry files, letting you inspect FCS metadata, extract event data, and prepare research datasets without manual parsing.

Core Features & Use Cases

  • FCS Parsing: Read FCS 2.0, 3.0, and 3.1 files while handling offset issues and multi-dataset layouts.
  • Data Extraction and Export: Convert event data into NumPy arrays, pandas DataFrames, or CSV outputs for downstream analysis.
  • Research Workflow Support: Inspect channels, detect scatter or fluorescence signals, preserve metadata, and re-export modified files for cytometry preprocessing.
  • Use Case: A researcher loads a batch of instrument exports, reviews channel names and acquisition metadata, extracts the event matrix, and saves a clean analysis-ready table for review.

Quick Start

Use the flowio skill to read the attached FCS file, extract its metadata and event matrix, and return an analysis-ready summary with channel labels.

Frequently Asked Questions about flowio

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I parse FCS flow cytometry files into a pandas DataFrame for analysis?

To parse FCS flow cytometry files into a DataFrame, use this Skill to read event matrices and extract metadata. It converts single or multi-dataset FCS 2.0-3.1 files directly into analysis-ready NumPy arrays or pandas DataFrames.

What is the best way to extract metadata and event data from multi-dataset FCS files?

Extracting metadata and event data from multi-dataset FCS files involves handling offset discrepancies and null channels. This Skill robustly parses multi-dataset layouts, preserving acquisition metadata while converting events into clean arrays.

Does this FCS parsing method support older flow cytometry file versions like FCS 2.0?

Yes, this FCS parsing method supports older flow cytometry file versions. It maintains full FCS 2.0, 3.0, and 3.1 compatibility while applying optional time, gain, and logarithmic transformations to the event data.

How do I export extracted flow cytometry event data to CSV?

You can export extracted flow cytometry event data to CSV by first parsing the FCS file into a DataFrame. This Skill facilitates CSV or DataFrame export, allowing you to save clean analysis-ready tables for downstream research workflows.

Why does my FCS file parsing fail due to offset discrepancies or null channels?

FCS file parsing often fails due to offset discrepancies or null channels when standard readers cannot handle malformed headers. This Skill applies robust handling for these specific issues, accurately extracting event matrices without manual intervention.

Can I inspect fluorescence channel names directly from an FCS file before processing?

Yes, you can inspect fluorescence channel names directly from an FCS file. This Skill allows you to review channel names, detect scatter or fluorescence signals, and validate acquisition metadata before extracting the event matrix.