bio-flow-cytometry-clustering-phenotyping

bio-flow-cytometry-clustering-phenotyping is a skill for Claude Code, Codex from BioTender-max/awesome-bio-agent-skills. It costs 55 tokens per session (1,807 once invoked), scanned A, original, no licence file.

A guide to grouping cells with similar measurements in flow or mass cytometry data. Flow and mass cytometry measure many markers on individual cells, helping identify cell types.

In plain words
What is it for?
Use it to cluster cytometry data and identify cell types with FlowSOM, Phenograph, or CATALYST.
Why use it?
It helps discover cell populations when you do not already know which measurement thresholds, or gates, to set by hand.

Skill for Claude CodeCodex

Which agent this was written for is unclear — body not stored (licence); the path alone says nothing.

Good fit Use it to cluster cytometry data and identify cell types with FlowSOM, Phenograph, or CATALYST.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/biotender-max/awesome-bio-agent-skills/clustering-phenotyping
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add BioTender-max/awesome-bio-agent-skills --skill clustering-phenotyping
Clone the repo
git clone --depth 1 https://github.com/BioTender-max/awesome-bio-agent-skills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for bio-flow-cytometry-clustering-phenotyping

README.md
[![agentmods](https://agentmods.dev/badge/skills/biotender-max/awesome-bio-agent-skills/clustering-phenotyping/github.svg)](https://agentmods.dev/skills/biotender-max/awesome-bio-agent-skills/clustering-phenotyping)
Your own site
<a href="https://agentmods.dev/skills/biotender-max/awesome-bio-agent-skills/clustering-phenotyping"><img src="https://agentmods.dev/badge/skills/biotender-max/awesome-bio-agent-skills/clustering-phenotyping/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for bio-flow-cytometry-clustering-phenotyping

Your own site · 80×15
<a href="https://agentmods.dev/skills/biotender-max/awesome-bio-agent-skills/clustering-phenotyping"><img src="https://agentmods.dev/badge/skills/biotender-max/awesome-bio-agent-skills/clustering-phenotyping.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 55 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,807 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin unknown No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00055 $0.01807
Opus 5 $0.00028 $0.00903
Sonnet 5 $0.00011 $0.00361
Haiku 4.5 $0.00006 $0.00181

Measured 9d ago against content hash 22772fa46a20, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

bio-flow-cytometry-clustering-phenotyping scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/bioskills/clustering-phenotyping/SKILL.md · 238 lines

The source is not reproduced here

A licence we could not identify

The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.

Read it on GitHub

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 9d ago First seen · 238 lines · 55 tokens per session scan A 22772fa46a20

Subscribe to this mod's changes

bio-flow-cytometry-clustering-phenotyping is a skill published in the GitHub repository BioTender-max/awesome-bio-agent-skills (178 stars, last pushed 2mo ago), with no licence file. It adds 55 tokens to every session and 1,807 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

Related

Other skills, from other repositories

arboreto-grn-inference

GRN inference from expression via GRNBoost2 (gradient boosting) or GENIE3 (Random Forest). Load matrix, filter by TFs, infer TF-target-importance links, save network. Dask-parallelized to single-cell scale. Core SCENIC component.

FridrichMethod/awesome-skills · 64 tokens

bio-machine-learning-atlas-mapping

Maps query single-cell data onto reference atlases and transfers cell-type labels using scArches surgery (scVI/scANVI), Symphony, Azimuth, CellTypist, scPoli, popV, and foundation models, with explicit out-of-distribution and label-transfer uncertainty. Use when annotating new single-cell datasets against a…

FridrichMethod/awesome-skills · 122 tokens

bio-metagenomics-abundance

Turns shotgun classifier output into a defensible abundance table with Bracken Bayesian re-estimation, then compositional treatment (CLR, zero handling), library-size normalization, reference-frame differential abundance, and optional absolute quantification. Covers why a relative-abundance change is not a change, why…

FridrichMethod/awesome-skills · 158 tokens

bio-methylation-array-preprocessing

Turns raw Illumina Infinium methylation BeadChip IDATs (450K, EPIC, EPICv2) into a defensible beta/M matrix with sesame (openSesame/SigDF) or minfi (RGChannelSet -> MethylSet -> GenomicRatioSet). Covers Type I vs Type II probe chemistry and why raw Type II beta is compressed, the signal-to-beta math (beta =…

FridrichMethod/awesome-skills · 251 tokens

bio-chipseq-motif-analysis

De novo motif discovery and known motif enrichment analysis using HOMER and MEME-ChIP. Identify transcription factor binding motifs in ChIP-seq, ATAC-seq, or other genomic peak data. Use when finding enriched DNA motifs in peak sequences.

FridrichMethod/awesome-skills · 57 tokens

arboreto

Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for…

FridrichMethod/awesome-skills · 66 tokens