OpenBioMed is an agent platform and toolkit collection for biomedical research and drug discovery, covering areas such as molecular design, protein analysis, and single-cell data analysis. It is intended for researchers and provides the biomedical skills listed in the catalogue as workflows for Claude Code.
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.
npx agentmods add skills/pharmolix/openbiomed/spatial-transcriptomics-spatial-data-ionpx skills add PharMolix/OpenBioMed --skill spatial-transcriptomics-spatial-data-iogit clone --depth 1 https://github.com/PharMolix/OpenBioMedWrote 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.
[](https://agentmods.dev/skills/pharmolix/openbiomed/spatial-transcriptomics-spatial-data-io)<a href="https://agentmods.dev/skills/pharmolix/openbiomed/spatial-transcriptomics-spatial-data-io"><img src="https://agentmods.dev/badge/skills/pharmolix/openbiomed/spatial-transcriptomics-spatial-data-io.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00103 | $0.02872 |
| Opus 5 | $0.00051 | $0.01436 |
| Sonnet 5 | $0.00021 | $0.00574 |
| Haiku 4.5 | $0.00010 | $0.00287 |
Grade A, and why
spatial-transcriptomics-spatial-data-io 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 5d 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.
How it starts
The opening of the file, as written. The whole thing — 343 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Spatial Transcriptomics Data I/O
Load spatial transcriptomics data from Visium, Xenium, MERFISH, Slide-seq, and other platforms using Squidpy and SpatialData. Read Space Ranger outputs, convert formats, and access spatial coordinates. Use when loading Visium, Xenium, MERFISH, or other spatial data.
What it does
- Takes your platform-specific spatial data directory (e.g., Space Ranger output) as input.
- Identifies and parses the correct platform format (Visium, Xenium, MERFISH, Slide-seq, CosMx, Stereo-seq).
- Reads the raw expression matrix (spot-level or single-cell resolution).
- Automatically loads and scales spatial coordinates (
x,ypixels or physical distances). - Ingests accompanying tissue images and spatial scale factors directly into the data object structure.
- Returns a standardized
AnnDataorSpatialDataobject, binding expression data, shapes, and images together for immediate downstream analysis.
Why this exists
- Uses
squidpyandspatialdata_io, the gold-standard parsers for spatial data. - Automatically handles platform-specific quirks (e.g., Xenium single-cell vs. Visium spot resolution).
- Safely nests coordinates in
obsm['spatial']and images/scale factors inuns['spatial']to ensure compatibility with all major spatial plotting tools. - Provides future-proof integration by offering
SpatialData(Zarr) representations for modern, multi-modal spatial projects.
Usage
"Load my Visium spatial data" → Read spatial transcriptomics outputs (Visium, Xenium, MERFISH, Slide-seq) into AnnData objects with spatial coordinates and tissue images.
- Python:
squidpy.read.visium('spaceranger_out/'),spatialdata.read_zarr()
Load and work with spatial transcriptomics data from various platforms.
Required Imports
import squidpy as sq
import scanpy as sc
import anndata as ad
import spatialdata as sd
import spatialdata_io as sdio
Load 10X Visium Data
Goal: Load Visium spatial transcriptomics data from Space Ranger output into an AnnData object.
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.
- 5d ago First seen · 343 lines · 103 tokens per session scan A 1a8676d3b6fb
spatial-transcriptomics-spatial-data-io is a skill published in the GitHub repository PharMolix/OpenBioMed (1,107 stars, last pushed 1mo ago), licensed MIT. It adds 103 tokens to every session and 2,872 once invoked, about $0.0005 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 2026-08-30.
Other skills, from other repositories
spatial-microenvironment-subset
Load when extracting a niche / microenvironment subset around a center cell-type by spatial radius from a labelled spatial AnnData, producing a smaller AnnData of centers + their within-radius neighbours. Skip when running global tissue-domain detection (use spatial-domains); cross-condition comparison (use…
spatial-preprocess
Load when running the foundational spatial transcriptomics QC + filtering + normalisation + HVG + PCA + neighbour-graph + Leiden pipeline on a Visium / Xenium / generic spatial AnnData. Skip when raw FASTQs need converting first (use spatial-raw-processing); tissue-domain detection on already-preprocessed data (use…
Developmental Gene Panel Design Workflow
Panel design for DEVELOPING / dynamic systems (embryonic organs, differentiation, regeneration). The target experiment is usually a LATE / terminal stage, but the biology is a trajectory: terminal cell types are end-products of earlier lineage programs. A panel built from the target stage alone resolves terminal…
spatial-statistics
Load when running spatial autocorrelation / hotspot / co-occurrence / neighbourhood-enrichment / Ripley K stats on a clustered spatial AnnData via squidpy. Skip when ranking spatially variable genes (use spatial-genes); tissue domain detection (use spatial-domains).
spatial-raw-processing
Load when converting spatial transcriptomics raw FASTQ pairs through ST-Pipeline into a rawcounts.h5ad ready for spatial-preprocess. Skip when input is already a count-matrix AnnData (use spatial-preprocess); non-spatial bulk / scRNA FASTQ (use bulkrna-read-qc).
spatial-preprocessing
Load spatial transcriptomics data (Visium, Xenium, MERFISH, Slide-seq, generic h5ad), perform QC filtering, normalization, HVG selection, PCA, UMAP, and Leiden clustering.