spatial-oncology

spatial-oncology is a skill for Claude Code, Codex from ShangBioLab/SpatialClaw. It costs 25 tokens per session (351 once invoked), scanned A, original, Apache-2.0.

A Python API for exploratory cancer-focused analysis of spatial-omics data, which combines biological measurements with the locations of cells or tissue spots. It maps tumor ecosystems and summarizes spatial patterns linked to conditions, outcomes, or treatment response.

In plain words
What is it for?
Use it to map tumor cores, edges, and interfaces; find condition-associated niches; explore cohort outcome associations; and summarize response-associated spatial patterns.
Why use it?
It organizes several oncology spatial analyses in one interface instead of requiring separate manual workflows. Its results are research summaries, not validated clinical predictions or treatment advice.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to map tumor cores, edges, and interfaces; find condition-associated niches; explore cohort outcome associations; and summarize response-associated spatial patterns.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/shangbiolab/spatialclaw/spatial-oncology
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 ShangBioLab/SpatialClaw --skill spatial-oncology
Clone the repo
git clone --depth 1 https://github.com/ShangBioLab/SpatialClaw

Made for: Claude Code, Codex.

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-oncology

README.md
[![agentmods](https://agentmods.dev/badge/skills/shangbiolab/spatialclaw/spatial-oncology/github.svg)](https://agentmods.dev/skills/shangbiolab/spatialclaw/spatial-oncology)
Your own site
<a href="https://agentmods.dev/skills/shangbiolab/spatialclaw/spatial-oncology"><img src="https://agentmods.dev/badge/skills/shangbiolab/spatialclaw/spatial-oncology/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-oncology

Your own site · 80×15
<a href="https://agentmods.dev/skills/shangbiolab/spatialclaw/spatial-oncology"><img src="https://agentmods.dev/badge/skills/shangbiolab/spatialclaw/spatial-oncology.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 25 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 351 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original 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 $0.00025 $0.00351
Opus 5.5 $0.00010 $0.00140
Sonnet 5.5 $0.00005 $0.00070
Haiku 4.5 $0.00003 $0.00035

Measured 1mo ago against content hash cc44d9bcaa8e, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

spatial-oncology 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.

The scan reads SKILL.md. This mod also ships 1 executable file (spatial_oncology.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/spatial/spatial-oncology/SKILL.md · 54 lines

What it actually says

Spatial Oncology

Spatial Oncology maps cancer-related spatial ecosystems and summarizes tumor, immune, stromal, and outcome-associated research patterns.

Interface

Python API only. This skill is intentionally not registered for spatialclaw run or other CLI execution routing.

Research-Use Boundary

Outputs are exploratory and research-use only. They do not diagnose disease, predict individual patient outcomes, recommend treatment, or provide clinical decision support. Placeholder or fallback outputs must not be interpreted as validated clinical predictions.

Python API

from skills.spatial._lib.oncology import run_oncology

result = run_oncology(adata, method="tumor_ecosystem", cell_type_key="cell_type")

Capabilities

  • Tumor core, edge, and interface mapping.
  • Supervised niche discovery by condition.
  • Exploratory cohort outcome association helpers.
  • Response-associated spatial pattern summaries.

Validation

Covered by tests/spatial/test_library_only_skills.py::test_spatial_oncology_smoke.

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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. 1mo ago First seen · 54 lines · 25 tokens per session scan A cc44d9bcaa8e

Subscribe to this mod's changes

spatial-oncology is a skill published in the GitHub repository ShangBioLab/SpatialClaw (11 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 25 tokens to every session and 351 once invoked, about $0.0001 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.

Related

Other skills, from other repositories

Gene Panel Selection Workflow

End-to-end workflow for gene panel design in scRNA-seq and spatial transcriptomics, that should be STRICTLY followed: dataset understanding + smart downsampling + train/test splits, algorithmic selection (HVG/DE/RF/scGeneFit/SpaPROS), optimal sub-panel discovery (ARI vs size), biological completion with a stability…

aristoteleo/PantheonOS · 110 tokens

spatial-cnv

Load when inferring copy-number variation per spot on a preprocessed spatial AnnData with chromosome-annotated genes via infercnvpy (default — log-ratio sliding-window) or Numbat (R, allele-aware clone deconvolution). Skip when var["chromosome"] / var["start"] / var["end"] gene-coord metadata is missing; no…

TianGzlab/OmicsClaw · 93 tokens

cancer-research

Deep cancer research skill for finding maximum life-extension treatment strategies. Use this skill whenever a user asks about cancer treatment options, cancer research, oncology treatment plans, survival optimization, clinical trials for cancer, immunotherapy options, or any query about extending life for a cancer…

romeo111/OpenOnco · 146 tokens

torchdrug

Builds and troubleshoots TorchDrug 0.2.1 workflows for molecular graphs, property prediction, self-supervised pretraining, molecule generation, retrosynthesis, protein representation learning, and knowledge graph reasoning. Use when code imports torchdrug or needs its datasets, models, tasks, or Engine.

K-Dense-AI/scientific-agent-skills · 64 tokens

arboreto

Infers candidate gene regulatory networks from bulk or single-cell expression data using AertsLab Arboreto GRNBoost2 and GENIE3. Use for transcription factor-target association ranking, compatible Dask execution, sparse expression inputs, and network stability checks.

K-Dense-AI/scientific-agent-skills · 55 tokens

deepspot-m

Generates transcriptome-wide virtual spatial transcriptomics from H&E histology with DeepSpot-M. Used for predicted log1p-CPM expression from 224x224 tiles at about 20x, querying the released protein-coding gene panel by symbol, and whole-slide prediction after resolution-aware tiling with histolab.

K-Dense-AI/scientific-agent-skills · 70 tokens