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Agent skill

Flowio

by aipoch in aipoch/medical-research-skills

Parse Flow Cytometry Standard (FCS) files v2.0–3.1 and extract events/metadata for preprocessing workflows (e.g., when you need NumPy arrays, channel info, or CSV/DataFrame export from cytometry…

MITAuto-check passedData & Analytics

Install Flowio

skills CLI
$ npx skills add aipoch/medical-research-skills --skill flowio -a claude-code

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

GitHub CLI
$ gh skill install aipoch/medical-research-skills flowio --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/aipoch/medical-research-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/'scientific-skills/Data Analysis/flowio' .claude/skills/flowio && 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
flowio
GitHub stars
2k
Token cost
~1.8k tokens
SKILL.md length
456 words
Files
3 (incl. references)
Skills in repo
567
Repo updated
First seen
Licence
MIT

At a glance

Parse Flow Cytometry Standard (FCS) files v2.0–3.1 and extract events/metadata for preprocessing workflows (e.g., when you need NumPy arrays, channel info, or CSV/DataFrame export from cytometry…

  • Works in 3 steps: Gain scaling (PnG): Values are… → Log/exponential transform (PnE): If… → Time scaling: If a time channel is…
  • Tasks that involve DataFrames
  • SKILL.md covers When to Use, Key Features, Dependencies and Example Usage, plus 1 more section
  • Calls python

What it does

Flowio is an agent skill from aipoch/medical-research-skills. Parse Flow Cytometry Standard (FCS) files v2.0–3.1 and extract events/metadata for preprocessing workflows (e.g., when you need NumPy arrays, channel info, or CSV/DataFrame export from cytometry files).

Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including reference files (for example `flowio_audit_result_v1.json` and `references/api_reference.md`).

It sits in Data & Analytics, covering DataFrames and CSV and tabular files. It works with NumPy. The repository describes itself as: Hundreds of agent skills for medical research, including protocol design, data analysis, evidence insights, and academic writing. The licence is MIT.

When your agent uses it

  • Tasks that involve DataFrames
  • Tasks that involve CSV and tabular files

Example prompts

  • “/flowio”

Requirements

  • Python 3

Workflow steps

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

  1. Gain scaling (PnG): Values are multiplied by the per-parameter gain.
  2. Log/exponential transform (PnE): If present, applies
  3. Time scaling: If a time channel is detected, values may be scaled into appropriate units.

What it can do on your machine

Read from SKILL.md and the folder at commit 686e09d. 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

    Shell commands in SKILL.md call:

    • python

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

  • Network

    No URLs in SKILL.md.

    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

Flowio loads about 1.8k tokens when it runs, and up to ~4.5k if it reads all its reference files. Until then it costs about 52 tokens; SKILL.md has 456 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~52
When it runs · the whole SKILL.md, loaded when a task matches
~1.8k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~4.5k

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 aipoch/medical-research-skills at commit 686e09d, republished under its MIT licence (© aipoch). 456 words, ~1,756 tokens.

Download SKILL.mdSave it as .claude/skills/flowio/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
flowio
description
Parse Flow Cytometry Standard (FCS) files v2.0–3.1 and extract events/metadata for preprocessing workflows (e.g., when you need NumPy arrays, channel info, or CSV/DataFrame export from cytometry files).
license
MIT
author
AIPOCH

Source: https://github.com/aipoch/medical-research-skills

When to Use

  • You need to read FCS v2.0/3.0/3.1 files and extract event matrices for downstream preprocessing.
  • You want to inspect or validate FCS metadata (TEXT segment) without loading event data (memory-efficient parsing).
  • You need channel definitions (PnN/PnS), ranges (PnR), and automatic identification of scatter/fluorescence/time channels.
  • You need to handle problematic FCS files with offset inconsistencies or multi-dataset content.
  • You want to export cytometry events to CSV/Pandas DataFrame or write new/modified FCS files.

Key Features

  • FCS parsing (v2.0–3.1): Reads HEADER/TEXT/DATA and optional ANALYSIS segments.
  • Event extraction to NumPy: Returns event data as ndarray with shape (events, channels).
  • Optional preprocessing: Applies standard FCS transformations (gain/log/time scaling) when enabled.
  • Metadata access: Exposes TEXT keywords and common instrument/acquisition fields.
  • Channel utilities: Provides PnN/PnS labels, ranges, and indices for scatter/fluorescence/time channels.
  • Robust parsing options: Flags for offset discrepancy handling and null-channel exclusion.
  • Multi-dataset support: Detects and reads files containing multiple datasets.
  • FCS writing: Create new FCS files from arrays and optionally preserve/override metadata.

Dependencies

  • python >= 3.9
  • flowio (install via pip/uv; version depends on your environment)
  • Example-only:
    • numpy >= 1.20
    • pandas >= 1.5

Example Usage

python
"""
End-to-end example:
1) Read an FCS file (metadata + events)
2) Convert to a Pandas DataFrame and export CSV
3) Filter events and write a new FCS file
4) Handle multi-dataset files
"""

from pathlib import Path

import numpy as np
import pandas as pd

from flowio import (
    FlowData,
    create_fcs,
    read_multiple_data_sets,
    MultipleDataSetsError,
    FCSParsingError,
    DataOffsetDiscrepancyError,
)

FCS_PATH = "sample.fcs"

def read_fcs_safely(path: str) -> FlowData:
    try:
        return FlowData(path)
    except DataOffsetDiscrepancyError:
        # Common workaround for files with inconsistent offsets
        return FlowData(path, ignore_offset_discrepancy=True)
    except FCSParsingError:
        # Looser mode if the file is malformed
        return FlowData(path, ignore_offset_error=True)

def main() -> None:
    # --- 1) Read file (single dataset) ---
    try:
        flow = read_fcs_safely(FCS_PATH)
    except MultipleDataSetsError:
        # --- 4) Multi-dataset handling ---
        datasets = read_multiple_data_sets(FCS_PATH)
        flow = datasets[0]  # pick the first dataset for this demo

    print("File:", getattr(flow, "name", Path(FCS_PATH).name))
    print("FCS version:", flow.version)
    print("Events:", flow.event_count)
    print("Channels:", flow.channel_count)
    print("PnN labels:", flow.pnn_labels)

    # Metadata (TEXT segment)
    print("Instrument ($CYT):", flow.text.get("$CYT", "N/A"))
    print("Acquisition date ($DATE):", flow.text.get("$DATE", "N/A"))

    # --- 2) Events -> NumPy -> DataFrame -> CSV ---
    events = flow.as_array(preprocess=True)  # default preprocessing behavior
    df = pd.DataFrame(events, columns=flow.pnn_labels)
    df.to_csv("events.csv", index=False)
    print("Wrote CSV:", "events.csv")

    # --- 3) Filter and write a new FCS ---
    # Example: threshold on first scatter channel if available, else channel 0
    fsc_idx = flow.scatter_indices[0] if getattr(flow, "scatter_indices", []) else 0
    threshold = np.percentile(events[:, fsc_idx], 50)  # median threshold
    mask = events[:, fsc_idx] > threshold
    filtered = events[mask]

    create_fcs(
        "filtered.fcs",
        filtered,
        flow.pnn_labels,
        opt_channel_names=flow.pns_labels,
        metadata={**flow.text, "$SRC": "Filtered via FlowIO example"},
    )
    print("Wrote FCS:", "filtered.fcs")

    # --- Metadata-only read (memory efficient) ---
    meta_only = FlowData(FCS_PATH, only_text=True)
    print("Metadata-only read: $DATE =", meta_only.text.get("$DATE", "N/A"))

if __name__ == "__main__":
    main()

Implementation Details

Data Model and Segments

An FCS file is organized into segments:

  • HEADER: FCS version and byte offsets for other segments.
  • TEXT: Keyword/value metadata (e.g., $DATE, $CYT, $PnN, $PnS, $PnR, $PnG, $PnE).
  • DATA: Event matrix encoded as integer/float/double/ASCII depending on file keywords.
  • ANALYSIS (optional): Post-processing results if present.

In FlowIO, these are exposed via FlowData attributes such as:

  • flow.header (HEADER info)
  • flow.text (TEXT keyword dictionary)
  • flow.analysis (ANALYSIS keyword dictionary, if present)
  • flow.as_array(...) (decoded event matrix)
Show full SKILL.md (200 more words)Show less
Preprocessing (as_array(preprocess=True))

When preprocessing is enabled, FlowIO applies common FCS transformations:

  1. Gain scaling (PnG): Values are multiplied by the per-parameter gain.
  2. Log/exponential transform (PnE): If present, applies:
    • value = a * 10^(b * raw_value) where PnE = "a,b".
  3. Time scaling: If a time channel is detected, values may be scaled into appropriate units.

To disable all transformations and obtain raw decoded values:

  • flow.as_array(preprocess=False)
Channel Identification

FlowIO provides convenience indices for common channel types:

  • flow.scatter_indices (e.g., FSC/SSC)
  • flow.fluoro_indices (fluorescence channels)
  • flow.time_index (time channel index or None)

These indices can be used to slice the event matrix:

  • events[:, flow.scatter_indices]
  • events[:, flow.fluoro_indices]
Handling Problematic Files (Offsets and Null Channels)

Some files contain inconsistent offsets between HEADER and TEXT:

  • ignore_offset_discrepancy=True to tolerate HEADER/TEXT offset mismatch.
  • use_header_offsets=True to prefer HEADER offsets.
  • ignore_offset_error=True to bypass offset-related failures more aggressively.

To exclude known null/empty channels during parsing:

  • FlowData(path, null_channel_list=[...])
Multi-Dataset Files

If a file contains multiple datasets, constructing FlowData(path) may raise MultipleDataSetsError. Use:

  • read_multiple_data_sets(path) to load all datasets, or
  • FlowData(path, nextdata_offset=...) to load a specific dataset using $NEXTDATA offsets.
Writing FCS

Two common patterns:

  • Write metadata-only changes: flow.write_fcs("out.fcs", metadata={...})
  • Modify event data: extract array → modify → create_fcs(...) to generate a new file (FlowIO does not modify event data in-place).

© aipoch, 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 2 other files (references) in scientific-skills/Data Analysis/flowio of aipoch/medical-research-skills.

  • SKILL.md
  • flowio_audit_result_v1.json
  • references/api_reference.md

Open the folder on GitHubat commit 686e09d

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Works with

Categories

Questions about Flowio

What does Flowio do?

Parse Flow Cytometry Standard (FCS) files v2.0–3.1 and extract events/metadata for preprocessing workflows (e.g., when you need NumPy arrays, channel info, or CSV/DataFrame export from cytometry…. Flowio is an agent skill from aipoch/medical-research-skills., when you need NumPy arrays, channel info, or CSV/DataFrame export from cytometry files).

When should I use Flowio?

Flowio fits situations like: tasks that involve DataFrames; tasks that involve CSV and tabular files.

How do I install Flowio in Claude Code?

Run `npx skills add aipoch/medical-research-skills --skill flowio -a claude-code`. Or copy the skill folder (scientific-skills/Data Analysis/flowio in aipoch/medical-research-skills) into .claude/skills/flowio in your project. Claude Code loads it when a task matches its description.

How do I install Flowio in Codex?

Run `npx skills add aipoch/medical-research-skills --skill flowio -a codex`. Or copy the skill folder (scientific-skills/Data Analysis/flowio in aipoch/medical-research-skills) into .agents/skills/flowio in your project. Codex loads it when a task matches its description.

Can I use Flowio 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 aipoch/medical-research-skills --skill flowio -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/flowio, .gemini/skills/flowio, .github/skills/flowio and .opencode/skills/flowio in your project.

What does Flowio need to run?

Going by SKILL.md and its folder, Flowio needs the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Flowio access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Flowio 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 Flowio use?

Flowio 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 Flowio use?

About 1.8k tokens (SKILL.md is roughly 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 2.8k tokens, read only when the agent opens those files.

What are the alternatives to Flowio?

Skills that share tags, products or a category with Flowio: Verified Data Analysis with pandas (pipeshub-ai/pipeshub-ai, 3.8k stars), Vaex Out-of-Core DataFrames (davila7/claude-code-templates, 32k stars), Hybrid-Engine Data Analysis (code-yeongyu/oh-my-openagent, 70k stars) and CSV Data Summarizer (coffeefuelbump/csv-data-summarizer-claude-skill, 468 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Flowio?

aipoch (a GitHub organization) maintains it in aipoch/medical-research-skills, which has 1,973 GitHub stars. The repository holds 567 skills in this directory. The repository was last updated on September 17, 2026.

Source: aipoch/medical-research-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.