spatial-transcriptomics-cell-cell-communication

spatial-transcriptomics-cell-cell-communication is a skill for Claude Code from jaechang-hits/SciAgent-Skills. It costs 189 tokens per session (10,286 once invoked), scanned A, original, no licence file.

A spatial transcriptomics analysis workflow for studying possible communication between cell types through ligand–receptor pairs. Ligands are signalling molecules released by one cell, while receptors are matching proteins on another cell.

In plain words
What is it for?
Use it to rank ligand–receptor interactions, test them with permutation statistics, and examine communication in spatial neighbourhoods or microenvironments.
Why use it?
It combines several analysis methods and spatial proximity so cell pairs are not treated as communicating solely because their genes are present.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the sciagent-skills plugin — 206 skills shipped together

Good fit Use it to rank ligand–receptor interactions, test them with permutation statistics, and examine communication in spatial neighbourhoods or microenvironments.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/jaechang-hits/sciagent-skills/spatial-transcriptomics-cell-cell-communication
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 jaechang-hits/SciAgent-Skills --skill spatial-transcriptomics-cell-cell-communication
Clone the repo
git clone --depth 1 https://github.com/jaechang-hits/SciAgent-Skills

Made for: Claude Code.

Or install sciagent-skills, the plugin that ships this one along with the rest of its 206 skills.

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 spatial-transcriptomics-cell-cell-communication

README.md
[![agentmods](https://agentmods.dev/badge/skills/jaechang-hits/sciagent-skills/spatial-transcriptomics-cell-cell-communication/github.svg)](https://agentmods.dev/skills/jaechang-hits/sciagent-skills/spatial-transcriptomics-cell-cell-communication)
Your own site
<a href="https://agentmods.dev/skills/jaechang-hits/sciagent-skills/spatial-transcriptomics-cell-cell-communication"><img src="https://agentmods.dev/badge/skills/jaechang-hits/sciagent-skills/spatial-transcriptomics-cell-cell-communication/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 spatial-transcriptomics-cell-cell-communication

Your own site · 80×15
<a href="https://agentmods.dev/skills/jaechang-hits/sciagent-skills/spatial-transcriptomics-cell-cell-communication"><img src="https://agentmods.dev/badge/skills/jaechang-hits/sciagent-skills/spatial-transcriptomics-cell-cell-communication.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 189 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 10,286 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. 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 $0.00189 $0.10286
Opus 5.5 $0.00076 $0.04114
Sonnet 5.5 $0.00038 $0.02057
Haiku 4.5 $0.00019 $0.01029

Measured today against content hash 18ed243bfc0d, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

spatial-transcriptomics-cell-cell-communication scanned grade A with 1 finding 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 today.

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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

r = requests.get(EUTILS, params={"db": "pubmed", "term": term, "retmode": "json", "retmax": 0}, timeout=30)
skills/genomics-bioinformatics/spatial-transcriptomics/spatial-transcriptomics-cell-cell-communication/SKILL.md · 706 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

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. today First seen · 706 lines · 189 tokens per session scan A 18ed243bfc0d

Subscribe to this mod's changes

spatial-transcriptomics-cell-cell-communication is a skill published in the GitHub repository jaechang-hits/SciAgent-Skills (381 stars, last pushed yesterday), with no licence file. It adds 189 tokens to every session and 10,286 once invoked, about $0.0008 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-23.

Related

Other skills, from other repositories

bioinformatics-on-healthomics

Guides users through bioinformatics analysis on AWS HealthOmics. Covers analysis type selection (alignment, variant calling, QC, interpretation), workflow generation in Nextflow or WDL, container sourcing from public registries, infrastructure setup (S3, ECR, IAM), workflow registration, parameter preparation…

awslabs/hcls-agent-skills · 199 tokens

biomcp-research

Do biomedical literature and variant research with the BioMCP CLI, and file what you learn about the tool itself as issues in the biomcp repo.

genomoncology/biomcp · 36 tokens

drug-discovery-search

End-to-end drug discovery platform combining ChEMBL compounds, DrugBank, targets, and FDA labels. Natural language powered by Valyu.

beita6969/ScienceClaw · 33 tokens

genomics-alignment

Load when computing alignment QC metrics (mapping rate, MAPQ distribution, insert size, duplicate rate, proper-pair rate) from a text SAM file produced by any short-/long-read aligner (BWA / Bowtie2 / Minimap2); convert a BAM with samtools view -h first. Skip when running the alignment step itself; only FASTQ-level QC…

TianGzlab/OmicsClaw · 89 tokens

genomics-cnv-calling

Load when calling CNV segments via CBS-style segmentation on a bin-level log2-ratio CSV from exome / WGS coverage — emits per-segment 5-class CN state (amplification / gain / neutral / loss / deepdeletion), per-chromosome summary, genome-fraction-altered. Skip when working with single-cell / spatial CNV (use…

TianGzlab/OmicsClaw · 95 tokens

genomics-epigenomics

Load when summarising a peak file (BED / narrowPeak) from ATAC-seq / ChIP-seq / CUT&Tag — peak count, width distribution, per-chromosome counts, score statistics. Skip when calling peaks from BAM (run MACS / Genrich externally first); working with single-cell ATAC (use scatac-preprocessing).

TianGzlab/OmicsClaw · 79 tokens