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 skills add TianGzlab/OmicsClaw --skill spatial-deconvgit clone --depth 1 https://github.com/TianGzlab/OmicsClawWrote 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/tiangzlab/omicsclaw/spatial-deconv)<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/spatial-deconv"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/spatial-deconv/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/tiangzlab/omicsclaw/spatial-deconv"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/spatial-deconv.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
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 →
- high Rogue Agent · line 3 Skill modifies its own code, configuration, or behavior at runtime. Self-modification enables an agent to escalate privileges, disable safety constraints, or install persistent backdoors.Fix: Prevent the skill from modifying its own code, SKILL.md, or configuration files. Treat skill files as read-only at runtime.
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 | $0.00084 | $0.02149 |
| Opus 5 | $0.00042 | $0.01074 |
| Sonnet 5 | $0.00017 | $0.00430 |
| Haiku 4.5 | $0.00008 | $0.00215 |
Grade A, and why
spatial-deconv 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.
How it starts
The opening of the file, as written. The whole thing — 168 lines — stays where its author put it; the contents beside it link to each section on GitHub.
spatial-deconv
When to use
The user has a Visium-style multi-cell-per-spot spatial AnnData PLUS a labelled scRNA reference AnnData and wants per-spot cell-type proportions. Eight backends:
flashdeconv(default) — ultra-fast O(N) CPU sketching. No GPU.cell2location— Bayesian deep learning with spatial priors (--cell2location-n-epochs,--cell2location-detection-alpha,--cell2location-n-cells-per-spot). Requiresscvi-tools+cell2location+torch.rctd— Robust Cell Type Decomposition (R /spacexr).destvi— multi-resolution VAE (--destvi-n-epochs,--destvi-n-hidden/--destvi-n-latent/--destvi-n-layers). Requiresscvi-tools+torch.stereoscope— two-stage probabilistic VAE (--stereoscope-learning-rate). Requiresscvi-tools+torch.tangram— gradient-based mapping (--tangram-n-epochs,--tangram-learning-rate). Requirestangram.spotlight— NMF-based with marker-gene priors (--spotlight-n-top,--spotlight-min-prop,--spotlight-weight-id).card— Conditional Autoregressive R-based deconvolution.
For single-cell-per-spot platforms (Xenium / MERFISH) use
spatial-annotate. For tissue-region detection (no reference needed)
use spatial-domains.
Inputs & Outputs
Inputs
- File types:
.h5ad - Requires a preprocessed AnnData (
Xnormalised, PCA/neighbours present) - Expects
obsm:spatial
Outputs
tables/card_proportions.csvtables/card_refined_proportions.csvtables/celltype_diversity.csvtables/deconv_run_summary.csvtables/deconv_spatial_points.csvtables/deconv_spot_metrics.csvtables/deconv_umap_points.csvtables/dominant_celltype.csvtables/dominant_celltype_counts.csvtables/mean_proportions.csvtables/proportions.csvtables/rctd_proportions.csvtables/ref_celltypes.csvtables/ref_counts.csvtables/ref_meta.csvtables/spatial_coords.csvtables/spatial_counts.csvtables/spotlight_proportions.csvfigures/assignment_margin_distribution.pngfigures/assignment_margin_spatial.pngfigures/celltype_diversity.pngfigures/dominant_celltype.pngfigures/dominant_celltype_distribution.pngfigures/mean_proportions.pngfigures/spatial_proportions.pngfigures/umap_proportions.pngprocessed.h5adreport.mdresult.json- Processed AnnData (
saves_h5ad) — addsobs:deconv_{method}_dominant_cell_type,deconv_{method}_dominant_proportion;obsm:deconvolution_{method}
What ships with it
8 files 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 · 168 lines · 84 tokens per session scan A dd4ae645dc13
spatial-deconv is a skill published in the GitHub repository TianGzlab/OmicsClaw (160 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 84 tokens to every session and 2,149 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 2026-09-03.
Other skills, from other repositories
spatial-deconvolution
Cell-type deconvolution for spatial transcriptomics by mapping a single-cell RNA-seq reference onto spatial data.
spatial-cell-annotation
Cell type annotation for spatial transcriptomics data using marker-based scoring, Tangram mapping, scANVI transfer, or CellAssign probabilistic models.
spatial-sc2spatial
Python API skill for mapping reference single-cell annotations and expression programs onto spatial transcriptomics data.
pkpd-modeling
Pharmacokinetic and pharmacodynamic modelling and simulation - non-compartmental analysis, compartmental and population PK, PK/PD and exposure-response, TMDD, PBPK orientation, bioequivalence, allometric scaling and first-in-human dose, drug interaction prediction, and Bayesian therapeutic drug monitoring. Use when…
neuropixels-analysis
Analyze Neuropixels extracellular recordings end-to-end with SpikeInterface. Covers loading SpikeGLX/Open Ephys/NWB data, preprocessing, drift/motion correction, Kilosort4 (and CPU) spike sorting, quality metrics, and unit curation (threshold-based, model-based UnitRefine, and AI-assisted visual review). Use when…
onekgpd
Query the 1000 Genomes Project dataset (3,202 whole-genome-sequenced individuals, GRCh38) at the level of individual participants. Use when a question is about individuals or variants in the 1000 Genomes Project cohort: which individuals carry variants matching specific criteria in a gene or region, which individuals…