Borrowing it
Nothing to install: this file belongs to cafferychen777/ChatSpatial. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/cafferychen777/ChatSpatial/main/.agents/skills/cell-composition/SKILL.mdgit clone --depth 1 https://github.com/cafferychen777/ChatSpatialWrote 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/cafferychen777/chatspatial/cell-composition)<a href="https://agentmods.dev/skills/cafferychen777/chatspatial/cell-composition"><img src="https://agentmods.dev/badge/skills/cafferychen777/chatspatial/cell-composition/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.
<a href="https://agentmods.dev/skills/cafferychen777/chatspatial/cell-composition"><img src="https://agentmods.dev/badge/skills/cafferychen777/chatspatial/cell-composition.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 5.1 | $0.00078 | $0.01531 |
| Opus 5 | $0.00039 | $0.00766 |
| Sonnet 5 | $0.00016 | $0.00306 |
| Haiku 4.5 | $0.00008 | $0.00153 |
Grade A, and why
cell-composition 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.
How it starts
The opening of the file, as written. The whole thing — 187 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cell Composition Analysis
Overview
This skill answers the fundamental question: What cell types exist at each spatial location, and in what proportions?
For spot-based data (Visium), this requires deconvolution to estimate cell type mixtures. For single-cell resolution data (Xenium/MERFISH), this requires direct cell type annotation.
Decision Tree: Which Approach?
START: User wants cell type information
│
├─ Q: What is the data resolution?
│ │
│ ├─ Spot-based (Visium, Slide-seq)
│ │ └─ Q: Do you have a reference scRNA-seq dataset?
│ │ ├─ YES → Deconvolution (see below)
│ │ └─ NO → Q: Do you have marker genes?
│ │ ├─ YES → Marker-based annotation
│ │ └─ NO → Use public atlas as reference
│ │
│ └─ Single-cell (Xenium, MERFISH, CosMx)
│ └─ Q: Do you have a reference dataset?
│ ├─ YES → Transfer learning (Tangram/scANVI)
│ └─ NO → Marker-based or LLM annotation
│
└─ Execute appropriate workflow
Deconvolution Method Selection
Quick Reference Table
| Your Scenario | Recommended Method | Why |
|---|---|---|
| Quick exploration | FlashDeconv | Fastest, good accuracy |
| Publication quality | RCTD (doublet mode) | Gold standard, well-validated |
| Large dataset (>50k spots) | Cell2location | Scalable, GPU-accelerated |
| Need spatial imputation | CARD | Can impute cell-type-specific expression |
| No matched reference | Tangram | More flexible with reference |
| Deep learning preference | DestVI/Stereoscope | Variational inference |
Detailed Method Guide
FlashDeconv (Recommended Default)
- Speed: Fastest (~seconds to minutes)
- Accuracy: Good for most applications
- When to use: Initial exploration, iterative analysis
- Limitations: Less accurate for rare cell types
RCTD (Publication Standard)
- Speed: Moderate (~minutes)
- Accuracy: Excellent, especially with doublet mode
- When to use: Final results, manuscript figures
- Modes:
doublet: High-resolution platforms (Visium HD, Slide-seq)full: Standard Visiummulti: When spots may contain >2 cell types
- Note: R-based, requires rpy2
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.
- 9d ago First seen · 187 lines · 78 tokens per session scan A 430900ac1c23
cell-composition is a skill published in the GitHub repository cafferychen777/ChatSpatial (44 stars, last pushed 24d ago), licensed MIT. It adds 78 tokens to every session and 1,531 once invoked, about $0.0004 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
cellxgene-census-query
Query CZ CELLxGENE Census (61M+ cells). Filter by cell type/tissue/disease, retrieve expression data, and integrate with scanpy/PyTorch for population-scale single-cell analysis. Use this skill when: (1) Querying single-cell expression data by cell type, tissue, or disease, (2) Exploring available single-cell datasets…
single-cell-multi-omics-analysis-scvi
Probabilistic deep learning framework for single-cell multi-omics data analysis. Use this skill when: (1) Analyzing single-cell RNA-seq data with batch correction, (2) Integrating multi-modal data (CITE-seq, ATAC-seq, multi-omics), (3) Performing cell type annotation with scANVI, (4) Spatial transcriptomics…
Disease Progression Trajectory Analysis
Use this skill when you have longitudinal patient omics data and want to.
Upstream Regulator Analysis
Identify transcription factors (TFs) driving observed differential expression by integrating ChIP-Atlas TF binding data (epigenomics) with RNA-seq DE results (transcriptomics). Ranks TFs by a combined regulatory score incorporating binding enrichment, target-DE overlap (Fisher's exact test), and directional…
bulkrna-coexpression
Load when discovering gene co-expression modules and hub genes in a bulk RNA-seq cohort via WGCNA-style soft-thresholded networks. Skip when direct DE comparison (use bulkrna-de); PPI lookup of an existing gene list (use bulkrna-ppi-network); single-cell co-expression (use sc-grn).
bulkrna-deconvolution
Load when estimating cell-type proportions in bulk RNA-seq samples from a single-cell or signature-matrix reference. Skip when the data is already single-cell (no deconvolution needed); spatial deconvolution (use spatial-deconv).