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-sc2spatialnpx skills add ShangBioLab/SpatialClaw --skill spatial-sc2spatialgit clone --depth 1 https://github.com/ShangBioLab/SpatialClawWhat 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 | $0.00025 | $0.00316 |
| Opus 5 | $0.00013 | $0.00158 |
| Sonnet 5 | $0.00005 | $0.00063 |
| Haiku 4.5 | $0.00003 | $0.00032 |
Grade A, and why
spatial-sc2spatial 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 3d 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 Reference Mapping
Spatial Reference Mapping transfers information from reference single-cell data to spatial transcriptomics datasets.
Interface
Python API only. This skill is intentionally not registered for spatialclaw run or other CLI execution routing.
Python API
from skills.spatial._lib.sc2spatial import run_sc2spatial_mapping
result = run_sc2spatial_mapping(adata_sp, adata_ref, method="label_transfer")
Capabilities
- KNN label transfer from reference cell annotations.
- Optional Tangram-style mapping.
- Optional SpaGE/gimVI expression imputation and embedding helpers.
- Optional CellTrek-style cell localization.
Validation
Covered by tests/spatial/test_library_only_skills.py::test_spatial_sc2spatial_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.
- 3d ago First seen · 50 lines · 25 tokens per session scan A 41659f1dc5db
spatial-sc2spatial is a skill published in the GitHub repository ShangBioLab/SpatialClaw (11 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 25 tokens to every session and 316 once invoked, about $0.0001 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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