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Agent skill

Bio Flow Cytometry Gating Analysis

by GPTomics in GPTomics/bioSkills

Defines cell populations in flow and spectral cytometry through manual gates (rectangle, polygon, quadrant, boolean) and reproducible automated gating (openCyto gating templates, flowDensity…

MITAuto-check passedData & Analytics

Install Bio Flow Cytometry Gating Analysis

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

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

GitHub CLI
$ gh skill install GPTomics/bioSkills bio-flow-cytometry-gating-analysis --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/gating-analysis .claude/skills/bio-flow-cytometry-gating-analysis && 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-gating-analysis
GitHub stars
1.2k
Used in
1 other repo
Token cost
~2.3k tokens
SKILL.md length
843 words
Files
3
Skills in repo
559
Repo updated
First seen
Licence
MIT

At a glance

Defines cell populations in flow and spectral cytometry through manual gates (rectangle, polygon, quadrant, boolean) and reproducible automated gating (openCyto gating templates, flowDensity…

  • Building a gating strategy
  • SKILL.md covers Version Compatibility, The Single Most Important…, Automated-Gating Taxonomy and Build a Gating Hierarchy, plus 7 more sections
  • Runs R scripts from its folder
  • Automating a manual FlowJo scheme across samples

What it does

Bio Flow Cytometry Gating Analysis is an agent skill from GPTomics/bioSkills. Defines cell populations in flow and spectral cytometry through manual gates (rectangle, polygon, quadrant, boolean) and reproducible automated gating (openCyto gating templates, flowDensity data-driven thresholds, flowClust model-based gates), organized as a hierarchical GatingSet (flowWorkspace) and round-tripped with FlowJo via CytoML. Covers the canonical gate order (time - debris - singlets - live - lineage), FMO-vs-isotype boundary setting, gate-order dependence and recompute semantics, rare-event/MRD…

Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files (for example `usage-guide.md`).

It sits in Data & Analytics, covering Statistics. 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

  • Building a gating strategy
  • Automating a manual FlowJo scheme across samples
  • Choosing manual vs data-driven gates
  • Extracting population frequencies

Example prompts

  • “Use the bio-flow-cytometry-gating-analysis skill to define cell populations in flow and spectral cytometry through manual gates (rectangle, polygon…”
  • “/bio-flow-cytometry-gating-analysis”

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), which the agent can run.

    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

Bio Flow Cytometry Gating Analysis loads about 2.3k tokens when it runs. Until then it costs about 188 tokens; SKILL.md has 843 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~188
When it runs · the whole SKILL.md, loaded when a task matches
~2.3k

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). 843 words, ~2,271 tokens.

Download SKILL.mdSave it as .claude/skills/bio-flow-cytometry-gating-analysis/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
bio-flow-cytometry-gating-analysis
description
Defines cell populations in flow and spectral cytometry through manual gates (rectangle, polygon, quadrant, boolean) and reproducible automated gating (openCyto gating templates, flowDensity data-driven thresholds, flowClust model-based gates), organized as a hierarchical GatingSet (flowWorkspace) and round-tripped with FlowJo via CytoML. Covers the canonical gate order (time -> debris -> singlets -> live -> lineage), FMO-vs-isotype boundary setting, gate-order dependence and recompute semantics, rare-event/MRD gating, and per-population statistics. Use when building a gating strategy, automating a manual FlowJo scheme across samples, choosing manual vs data-driven gates, or extracting population frequencies.
tool_type
r
primary_tool
flowWorkspace

Version Compatibility

Reference examples tested with: flowWorkspace 4.14+, openCyto 2.14+, flowDensity 1.36+, flowCore 2.14+, CytoML 2.14+.

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

  • R: packageVersion('<pkg>') then ?function_name to verify parameters

openCyto gating-method names drift across versions - confirm with gt_list_methods() on the installed package (e.g. gate_flowclust_2d vs flowClust.2d). Adapt rather than retrying.

Gating Analysis

"Gate my data to identify cell populations" -> Define populations by drawing boundaries in marker space, organized as a hierarchy, manually or with reproducible data-driven methods.

  • R (manual + hierarchy): flowCore gates -> flowWorkspace::GatingSet -> gs_pop_add -> recompute
  • R (automated): openCyto gating template (CSV) or flowDensity::deGate

The Single Most Important Modern Insight -- FMO, Not Isotype, Sets the Boundary; and Gate Order Is a Funnel

The position of a positive/negative boundary is governed by SPREADING ERROR - the variance that every other bright fluorophore spills into the channel of interest - NOT by nonspecific antibody binding (Roederer 2001 Cytometry 45:194). An FMO control (full panel minus the one channel) reproduces exactly that spreading and is the correct way to set the gate; an isotype control addresses only nonspecific binding, has a different total fluorochrome load, and sits in the wrong place. Isotypes are deprecated for boundary-setting (still fine for a qualitative new-reagent check). Equally load-bearing is gate ORDER: time -> debris (FSC/SSC) -> singlets (FSC-A vs FSC-H) -> live/dead -> lineage. This is a funnel that removes the broadest, least-specific contaminants first (time instability corrupts ALL channels; doublets are scatter-normal AND viable AND double-positive; dead cells bind antibody nonspecifically) so each narrower downstream gate operates on clean input. Reorder it - gate lineage before singlets - and artifacts are baked into the result that no later gate can remove.

Automated-Gating Taxonomy

MethodCitationMechanismWhen to use
openCytoFinak 2014 PLoS Comput Biol 10:e1003806CSV gatingTemplate + per-gate algorithmsreproduce a manual SOP across many samples; human-readable + automated
mindensity (openCyto)-KDE valley between two peaksclear bimodal marker, 1D cut
tailgate (openCyto)-KDE-derivative tail onsetrare positive tail, no clean second peak
quantileGate (openCyto)-cut at a fixed event quantilethreshold should track a fraction
flowDensityMalek 2015 Bioinformatics 31:606sequential bivariate density cutoffsreproduce an entire predefined manual strategy
flowClust / gate_flowclust_2dLo 2009 BMC Bioinformatics 10:145t-mixture + Box-Cox, K by BICoverlapping elliptical populations
DAFiLee 2018 Cytometry A 93:597recursive filter + clustering on a hierarchydiscovery WITH interpretability

Rule of thumb: 1D bimodal -> mindensity; rare tail -> tailgate; overlapping ellipses -> flowClust.2d; replicate a full manual SOP -> flowDensity; discovery-with-interpretability -> DAFi.

Build a Gating Hierarchy

Goal: Apply gates in the canonical order and extract population statistics.

Approach: Build a GatingSet, add gates parent-by-parent, then recompute() - WITHOUT it, child populations are empty. Gates apply on the TRANSFORMED scale if the GatingSet is transformed.

r
library(flowWorkspace); library(flowCore)

gs <- GatingSet(fs)
# matrix dimnames preserve 'FSC-A'/'FSC-H'; data.frame() would mangle them to FSC.A
singlet <- polygonGate('singlets', .gate = matrix(
  c(2e4, 1e4, 25e4, 2e5, 25e4, 26e4, 2e4, 4e4), ncol = 2, byrow = TRUE,
  dimnames = list(NULL, c('FSC-A', 'FSC-H'))))
gs_pop_add(gs, singlet, parent = 'root')
gs_pop_add(gs, rectangleGate('CD3+', CD3 = c(1.5, Inf)), parent = 'singlets')  # transformed scale
recompute(gs)                                   # REQUIRED - else children are empty
gs_pop_get_stats(gs, type = 'count')

Automated Gating with an openCyto Template

Goal: Apply a reproducible, declarative gating strategy across all samples.

Approach: A CSV template (alias/pop/parent/dims/gating_method/gating_args) defines the hierarchy; gt_gating applies it. Confirm method names with gt_list_methods().

r
library(openCyto); library(data.table)

tmpl <- fread('
alias,pop,parent,dims,gating_method,gating_args
nonDebris,+,root,FSC-A,mindensity,
singlets,+,nonDebris,"FSC-A,FSC-H",singletGate,
live,-,singlets,"Live_Dead",mindensity,
CD3,+,live,CD3,mindensity,
CD4CD8,+,CD3,"CD4,CD8",gate_flowclust_2d,K=2
')
gt <- gatingTemplate(tmpl)
gs <- GatingSet(fs)
gt_gating(gt, gs)

Rare-Event / MRD Gating

Goal: Detect a rare population (e.g. MRD at 1e-4 to 1e-5).

Approach: Unsupervised clustering FAILS here (a 1e-5 population is ~10 events, invisible to density/SOM); MRD stays supervised/template-gated. Compute the acquisition depth needed from the target sensitivity and the ~50-event Poisson rule BEFORE acquiring; never downsample.

r
# Need ~50-60 target events for CV < ~15%; sensitivity 1e-5 => acquire ~1e6 cells.
target_sensitivity <- 1e-5
events_needed <- ceiling(50 / target_sensitivity)   # cells to acquire
# Gate the rare population with a prespecified template; report observed LOD from cells acquired.
Show full SKILL.md (319 more words)Show less

Per-Method Failure Modes

Empty child populations

Trigger: querying stats right after gs_pop_add. Mechanism: membership not computed. Symptom: zero counts. Fix: recompute(gs).

Gate coordinates on the wrong scale

Trigger: raw-scale gate values on a transformed GatingSet (or vice versa). Mechanism: scale mismatch. Symptom: gate in the wrong place / empty. Fix: set gate values on the same (transformed) scale the GS uses.

Isotype-defined boundary

Trigger: isotype control to set positivity. Mechanism: spreading error, not nonspecific binding, sets the edge. Symptom: wrong negative boundary. Fix: use FMO.

Clustering used for rare events

Trigger: FlowSOM for a 1e-5 population. Mechanism: too few events. Symptom: rare pop absorbed into a neighbor. Fix: supervised/template gating; size acquisition for the Poisson floor.

Quantitative Thresholds

ThresholdSourceRationale
~50-60 events for CV < 15%Poisson statisticsrare-event detection floor
sensitivity 1e-5 needs ~1e6 cellsPoisson floorto collect ~50 events at that frequency
FMO for boundary, not isotypeRoederer 2001; Maecker & Trotter 2006spreading error dominates the boundary

Common Errors

Error / symptomCauseSolution
zero counts in childrenno recompute()call it after adding gates
gt_gating method not foundversion-renamed methodcheck gt_list_methods()
filter() vs Subset() confusionfilter returns a mask, Subset the datause Subset(ff, gate) for the population
FlowJo .jo won't importonly .wsp supportedre-save as wsp; use CytoML

References

  • Roederer 2001 Cytometry 45(3):194-205 — spreading error sets the gate boundary.
  • Maecker & Trotter 2006 Cytometry A 69(9):1037-1042 — FMO doctrine, controls, positivity.
  • Finak 2014 PLoS Comput Biol 10(8):e1003806 — openCyto automated gating templates.
  • Malek 2015 Bioinformatics 31(4):606-607 — flowDensity data-driven gating.
  • Lo 2009 BMC Bioinformatics 10:145 — flowClust model-based gating.
  • Lee 2018 Cytometry A 93(6):597-610 — DAFi directed filtering + clustering.
  • Spidlen 2015 Cytometry A 87(7):683-687 — Gating-ML 2.0 portable gate standard.
  • compensation-transformation - Preprocess before gating; gate on the transformed scale
  • doublet-detection - The singlet step of the gating funnel
  • clustering-phenotyping - Unsupervised alternative for high-dim discovery
  • differential-analysis - Compare gated population frequencies between conditions
  • fcs-handling - Load FCS and import FlowJo workspaces via CytoML

© 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 2 other files in flow-cytometry/gating-analysis of GPTomics/bioSkills.

  • SKILL.md
  • examples/gating_workflow.R
  • 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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Categories

Questions about Bio Flow Cytometry Gating Analysis

What does Bio Flow Cytometry Gating Analysis do?

Defines cell populations in flow and spectral cytometry through manual gates (rectangle, polygon, quadrant, boolean) and reproducible automated gating (openCyto gating templates, flowDensity…. Bio Flow Cytometry Gating Analysis is an agent skill from GPTomics/bioSkills. Defines cell populations in flow and spectral cytometry through manual gates (rectangle, polygon, quadrant, boolean) and reproducible automated gating (openCyto gating templates, flowDensity data-driven thresholds, flowClust model-based gates), organized as a hierarchical GatingSet (flowWorkspace) and round-tripped with FlowJo via CytoML.

When should I use Bio Flow Cytometry Gating Analysis?

Bio Flow Cytometry Gating Analysis fits situations like: building a gating strategy; automating a manual FlowJo scheme across samples; choosing manual vs data-driven gates; extracting population frequencies.

How do I install Bio Flow Cytometry Gating Analysis in Claude Code?

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

How do I install Bio Flow Cytometry Gating Analysis in Codex?

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

Can I use Bio Flow Cytometry Gating Analysis 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-gating-analysis -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-gating-analysis, .gemini/skills/bio-flow-cytometry-gating-analysis, .github/skills/bio-flow-cytometry-gating-analysis and .opencode/skills/bio-flow-cytometry-gating-analysis in your project.

What does Bio Flow Cytometry Gating Analysis need to run?

Going by SKILL.md and its folder, Bio Flow Cytometry Gating Analysis needs R for the scripts in its folder.

Does Bio Flow Cytometry Gating Analysis 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 Bio Flow Cytometry Gating Analysis 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 Gating Analysis use?

Bio Flow Cytometry Gating Analysis 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 Gating Analysis use?

About 2.3k tokens (SKILL.md is roughly 9.1k 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 Gating Analysis?

Skills that share tags, products or a category with Bio Flow Cytometry Gating Analysis: Sandbox Bench (vercel/next.js, 143k stars), Statistical Analysis (spacering-net/codeg, 3.9k stars), Statsmodels (zLanqing/codex-claude-academic-skills, 4.7k stars) and AI Daily Digest (vigorX777/ai-daily-digest, 1.6k 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 Gating Analysis?

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.