Bioconductor Zellkonverter logo

Bioconductor Zellkonverter

Organization
bioMate-AI
bioconductor-zellkonverter

Provides methods to convert between Python AnnData objects and SingleCellExperiment objects. These are primarily intended for use by downstream Bioconductor packages that wrap Python methods for single-cell data analysis. It also includes f

Overview

PublisherbioMate-AI
Repositorybiomate-bioconductor-kb
Skill namebioconductor-zellkonverter
Stars
804
Forks
66
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by bioMate-AI on GitHub. Read the source before you install it.

Installation

Install the Bioconductor Zellkonverter AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/bioMate-AI/biomate-bioconductor-kb/tree/main/skills/single-cell/zellkonverter
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/bioMate-AI/biomate-bioconductor-kb.git /tmp/biomate-bioconductor-kb
mkdir -p .claude/skills
cp -r /tmp/biomate-bioconductor-kb/skills/single-cell/zellkonverter .claude/skills/bioconductor-zellkonverter
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Bioconductor Zellkonverter in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Bioconductor Zellkonverter on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Bioconductor Zellkonverter is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

zellkonverter

Dependencies & Environment

Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.

  • Version: 1.22.0 · Bioconductor: 3.23 · R: ≥ 4.6
  • Depends: cli, DelayedArray, Matrix, reticulate, S4Vectors, SingleCellExperiment, SparseArray, SummarizedExperiment
  • Imports: basilisk, cli, DelayedArray, Matrix, reticulate, S4Vectors, SingleCellExperiment, SparseArray, SummarizedExperiment
  • Install: BiocManager::install("zellkonverter")

When to Use

  • Reading a SingleCellExperiment from a .h5ad file using readH5AD.
  • Writing a SingleCellExperiment to a .h5ad file using writeH5AD.
  • Converting directly between SingleCellExperiment and Python AnnData objects in memory using SCE2AnnData and AnnData2SCE.
  • Setting up consistent Python environments for package developers using AnnDataDependencies.

When NOT to Use

  • For converting Seurat objects directly to Python, use SeuratDisk or convert to SingleCellExperiment first, because zellkonverter strictly interfaces with SingleCellExperiment.
  • For managing Python environments generally, use basilisk directly, as zellkonverter uses it internally but isn't a general-purpose environment manager.

Data Requirements

  • A valid .h5ad file on disk for reading.
  • A SingleCellExperiment object in R for writing or conversion.
  • An AnnData object in Python (via reticulate) for in-memory conversion.

Key Parameters

  • file: Path to the input .h5ad file in readH5AD or output destination in writeH5AD.
  • verbose (FALSE): Logical indicating whether to display progress messages during conversion in readH5AD.
  • version: String specifying the version of anndata to return dependencies for in AnnDataDependencies (e.g., "0.7.6").
  • env: The basilisk environment to use, typically set to zellkonverterAnnDataEnv() in basiliskRun.

Best Practices

  • Use basiliskRun with zellkonverterAnnDataEnv() to safely execute Python code on AnnData objects within a controlled environment.
  • Use AnnDataDependencies() if you are a package developer to guarantee you are using the same versions of Python packages as zellkonverter.
  • Turn on global progress messages using setZellkonverterVerbose(TRUE) if you want to monitor large conversions.

Common Pitfalls

  • Python environment mismatches: Conversion failures due to missing or incompatible Python packages. Fix: Use basilisk to set up the Python environment before using SCE2AnnData or AnnData2SCE.
  • Incompatible anndata versions: Custom environments failing to parse .h5ad files correctly. Fix: Check required versions with AnnDataDependencies() to ensure compatibility.
  • Silent execution during long conversions: The process seems stalled when reading large .h5ad files. Fix: Set verbose = TRUE in readH5AD to display progress messages.

Alternatives

  • basilisk: For general R/Python environment management without specific single-cell object conversion.
  • reticulate: For direct Python interoperability in R, which zellkonverter wraps specifically for AnnData.
  • scRNAseq: For accessing standard single-cell datasets natively in R without needing .h5ad files.

Citations

  • Luke Zappia and Alan O'Callaghan (2021). "zellkonverter: Coercion Between Python AnnData and R SingleCellExperiment Objects." Journal of Open Source Software, 6(68), 3705.

References


Run this on BioMate

This skill is the knowledge layer — when, why, and how to use zellkonverter. To run this analysis on your own data with managed compute, automated QC, and reproducible outputs, use BioMate — free to start.

▶ Open zellkonverter on BioMate →

Frequently asked questions

What does the Bioconductor Zellkonverter AI skill do?

Provides methods to convert between Python AnnData objects and SingleCellExperiment objects. These are primarily intended for use by downstream Bioconductor packages that wrap Python methods for single-cell data analysis. It also includes f

Why use Bioconductor Zellkonverter on TypingMind?

Because you install it once and use it with any model. Bioconductor Zellkonverter is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Bioconductor Zellkonverter in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/bioMate-AI/biomate-bioconductor-kb/tree/main/skills/single-cell/zellkonverter. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Bioconductor Zellkonverter?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Bioconductor Zellkonverter?

As many as you like. As long as a model supports skills, you can use Bioconductor Zellkonverter with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Bioconductor Zellkonverter AI skill free?

It is published on GitHub by bioMate-AI. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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