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-interaction/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-interaction)<a href="https://agentmods.dev/skills/cafferychen777/chatspatial/cell-interaction"><img src="https://agentmods.dev/badge/skills/cafferychen777/chatspatial/cell-interaction/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-interaction"><img src="https://agentmods.dev/badge/skills/cafferychen777/chatspatial/cell-interaction.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 | $0.00075 | $0.01129 |
| Opus 5 | $0.00037 | $0.00564 |
| Sonnet 5 | $0.00015 | $0.00226 |
| Haiku 4.5 | $0.00007 | $0.00113 |
Grade A, and why
cell-interaction 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 — 170 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cell-Cell Interaction Analysis
Overview
This skill answers: How do cells communicate with each other in this tissue?
Cell communication fundamentally requires:
- A sender cell expressing a ligand
- A receiver cell expressing a receptor
- Spatial proximity enabling the interaction
Prerequisites
Before running cell communication analysis:
- Cell type annotations available (from deconvolution or direct annotation)
- Species identified (human or mouse)
- Biological question defined (specific pathways? global patterns?)
Method Selection
Decision Framework
Q: What level of analysis do you need?
│
├─ Ligand-receptor pairs only
│ └─ LIANA+ (Python) - Multi-method consensus, recommended default
│
├─ Signaling pathway analysis
│ └─ CellChat (R) - Pathway-level insights, communication networks
│
├─ Fast computation needed
│ └─ FastCCC (C++) - Human only, very fast
│
└─ Classic/benchmark comparison
└─ CellPhoneDB - Original method, good for comparisons
Method Comparison
| Aspect | LIANA+ | CellChat | CellPhoneDB | FastCCC |
|---|---|---|---|---|
| Language | Python | R | Python | C++ |
| Speed | Fast | Moderate | Moderate | Very Fast |
| Output | LR pairs | Pathways + Networks | LR pairs | LR pairs |
| Species | Human/Mouse | Human/Mouse | Human | Human |
| Strength | Multi-method consensus | Pathway interpretation | Literature standard | Scale |
Workflow
Step 1: Verify Cell Type Annotations
Required in adata.obs:
- Cell type column (e.g., 'cell_type', 'annotation')
- Clean labels (no "unknown", "unassigned")
- Biologically meaningful categories
Step 2: Configure Species
Species-specific databases:
- Human: liana_resource="consensus" (default)
- Mouse: liana_resource="mouseconsensus"
Step 3: Run Analysis
Use analyze_cell_communication tool with:
cell_type_key: Column name for cell type annotationsspecies: "human" or "mouse"method: Selected from decision framework
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 · 170 lines · 75 tokens per session scan A 4db803ff62ac
cell-interaction is a skill published in the GitHub repository cafferychen777/ChatSpatial (44 stars, last pushed 25d ago), licensed MIT. It adds 75 tokens to every session and 1,129 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.
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