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/spatial-analysis/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/spatial-analysis)<a href="https://agentmods.dev/skills/cafferychen777/chatspatial/spatial-analysis"><img src="https://agentmods.dev/badge/skills/cafferychen777/chatspatial/spatial-analysis/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/spatial-analysis"><img src="https://agentmods.dev/badge/skills/cafferychen777/chatspatial/spatial-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.00080 | $0.01172 |
| Opus 5.5 | $0.00032 | $0.00469 |
| Sonnet 5.5 | $0.00016 | $0.00234 |
| Haiku 4.5 | $0.00008 | $0.00117 |
Grade A, and why
spatial-analysis 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 1mo 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 — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Spatial Analysis
Overview
This skill guides complete spatial transcriptomics analysis from raw data to biological insights. The fundamental question: What is the spatial organization of this tissue?
Workflow Decision Tree
START: User provides spatial data
│
├─ Q: What platform?
│ ├─ Visium/Visium HD → spot-based, may have histology
│ ├─ Xenium/MERFISH/CosMx → single-cell resolution, imaging-based
│ ├─ Slide-seq → spot-based, no histology
│ └─ Unknown → check adata.uns for platform info
│
├─ Q: Single sample or multiple?
│ ├─ Single → proceed to standard workflow
│ └─ Multiple → include integration step (Harmony recommended)
│
└─ Execute workflow based on answers
Standard Workflow
Step 1: Data Loading and Understanding
1. Load data using load_data tool
2. Examine dataset profile:
- n_cells, n_genes
- Available annotations (adata.obs columns)
- Spatial coordinates availability
- Tissue image availability
3. Understand the biological context from user
Key questions to ask user:
- What tissue/organ is this?
- What is your biological question?
- Do you have a reference dataset for cell type annotation?
Step 2: Quality Control and Preprocessing
Platform-specific QC thresholds:
| Metric | Visium | Xenium/MERFISH | Slide-seq |
|--------|--------|----------------|-----------|
| min_genes | 200 | 50 | 100 |
| min_cells | 3 | 3 | 3 |
| max_mito% | 20% | 10% | 15% |
| HVG count | 2000-3000 | 500-1000 | 1500-2000 |
Use preprocess_data tool with appropriate parameters.
Step 3: Spatial Domain Identification
Method Selection Guide:
| Scenario | Recommended Method | Reason |
|---|---|---|
| Visium with histology | SpaGCN | Uses image features |
| Visium without histology | STAGATE/Leiden | Graph-based |
| Single-cell resolution | GraphST | Handles high resolution |
| Quick exploration | Leiden/Louvain | Fast, interpretable |
| Need fine structure | Higher resolution (1.5-2.0) | More clusters |
| Need broad domains | Lower resolution (0.3-0.5) | Fewer clusters |
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.
- 1mo ago First seen · 155 lines · 80 tokens per session scan A b73407194c97
spatial-analysis is a skill published in the GitHub repository cafferychen777/ChatSpatial (45 stars, last pushed 2d ago), licensed MIT. It adds 80 tokens to every session and 1,172 once invoked, about $0.0003 per session on Opus 5.5. 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.
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bulkrna-deconvolution
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