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.
npx agentmods add skills/shangbiolab/spatialclaw/spatial-histologynpx skills add ShangBioLab/SpatialClaw --skill spatial-histologygit clone --depth 1 https://github.com/ShangBioLab/SpatialClawWrote 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/shangbiolab/spatialclaw/spatial-histology)<a href="https://agentmods.dev/skills/shangbiolab/spatialclaw/spatial-histology"><img src="https://agentmods.dev/badge/skills/shangbiolab/spatialclaw/spatial-histology.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00030 | $0.00327 |
| Opus 5 | $0.00015 | $0.00163 |
| Sonnet 5 | $0.00006 | $0.00065 |
| Haiku 4.5 | $0.00003 | $0.00033 |
Grade A, and why
spatial-histology 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 6d 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.
What it actually says
Spatial Histology
Spatial Histology provides downstream image analysis for spatial transcriptomics and spatial pathology datasets. It wraps reusable functions from skills.spatial._lib.histology.
Interface
Python API only. This skill is intentionally not registered for spatialclaw run or other CLI execution routing.
Python API
from skills.spatial._lib.histology import run_histology
result = run_histology(image, task="tissue_segmentation", method="otsu")
Capabilities
- Tissue segmentation with Otsu or watershed methods.
- Nucleus detection with optional Cellpose or StarDist backends.
- Cell segmentation with optional Cellpose-compatible backends.
- Patch-level histology analysis for downstream spatial workflows.
Validation
Covered by tests/spatial/test_library_only_skills.py::test_spatial_histology_smoke.
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.
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.
- 6d ago First seen · 51 lines · 30 tokens per session scan A d342ae8d18b6
spatial-histology is a skill published in the GitHub repository ShangBioLab/SpatialClaw (11 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 30 tokens to every session and 327 once invoked, about $0.0002 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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