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-annotategit 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-annotate)<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/spatial-annotate"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/spatial-annotate/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-annotate"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/spatial-annotate.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 | $0.00071 | $0.01057 |
| Opus 5.5 | $0.00028 | $0.00423 |
| Sonnet 5.5 | $0.00014 | $0.00211 |
| Haiku 4.5 | $0.00007 | $0.00106 |
Grade A, and why
spatial-annotate 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 14d 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 — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
spatial-annotate
When to use
Assign discrete cell labels using marker overlap, Tangram, scANVI or CellAssign. Use spatial-deconv for proportions and spatial-domains for tissue regions.
Use from a step
from skills._sdk.notebook import load_skill, read_input, write_output
library = load_skill("spatial-annotate")
data = read_input("input.h5ad")
data = library.annotate(data, cluster_key="leiden")
write_output(library.cell_type_counts(data), "tables/results.csv")
Run examples/example_step.py through the step runner.
API
annotate(adata, *, method: str='marker_based', reference=None, species: str='human', marker_genes: dict | None=None, random_state: int=0, **parameters)
Annotate in place and return the same AnnData without file writes.
Marker and Tangram methods read log-normalized X. scANVI/CellAssign require raw counts in the requested layer (normally counts), raw, or explicitly selected X. Reference AnnData is copied before training.
:param adata: Spatial AnnData with normalized X and method-required labels/counts. :param method: CLI default marker_based; tangram, scanvi or cellassign are optional. :param reference: Labelled reference AnnData for Tangram/scANVI; None otherwise. :param species: Human by default or mouse for built-in marker dictionaries. :param marker_genes: Optional CellAssign marker mapping; None uses species markers. :param random_state: Training seed, 0 by default; change to assess sensitivity. :param parameters: Method keyword options in references/parameters.md. Defaults match the CLI, including cluster_key=leiden and n_marker_genes=50. :returns: The same AnnData with cell_type labels and JSON run diagnostics. :raises ValueError: Invalid method, species, missing reference or invalid counts. :raises TypeError: Unknown method parameter. :raises ImportError: Missing backend; use install_skill_deps for the named package.
What ships with it
10 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.
- _api.py 6.1 KB runs code
- examples/example_step.py 419 B runs code
- r_visualization/annotation_publication_template.R 1.6 KB
- r_visualization/README.md 1.4 KB
- references/methodology.md 11 KB
- references/output_contract.md 1.7 KB
- references/parameters.md 4.3 KB
- spatial_annotate.py 36 KB runs code
- tests/test_api.py 1.4 KB runs code
- tests/test_spatial_annotate.py 5.5 KB runs code
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.
- 14d ago Changed · -29 lines dd0c56bcd0c4
- 1mo ago First seen · 146 lines · 71 tokens per session scan A bea087e528c6
spatial-annotate is a skill published in the GitHub repository TianGzlab/OmicsClaw (161 stars, last pushed 14d ago), licensed Apache-2.0. It adds 71 tokens to every session and 1,057 once invoked, about $0.0003 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 2026-09-03.
Other skills, from other repositories
spatial-cell-annotation
Cell type annotation for spatial transcriptomics data using marker-based scoring, Tangram mapping, scANVI transfer, or CellAssign probabilistic models.
spatial-deconvolution
Cell-type deconvolution for spatial transcriptomics by mapping a single-cell RNA-seq reference onto spatial data.
spatial-omics
Spatial transcriptomics and spatial proteomics analysis covering technology-specific workflows, spatial statistics, deconvolution, and niche analysis.
cell-type-annotation
Generate code to assign cell type labels to single-cell RNA-seq clusters using CellTypist, SingleR, marker-based annotation, or reference label transfer (scANVI/ingest). Triggers on requests to "annotate cell types", "label clusters", "run CellTypist", "SingleR annotation", "marker gene dotplot", "transfer labels from…
spatial-omics-integrate
Integrate two aligned spatial omics modalities on the same cells using SpatialGlue or SpaDDM. Standard input contract: --input --omics2 . Supports demo mode and validated modality pairs such as RNA+Protein and RNA+ATAC.
uniprot
Access UniProt for protein sequence and annotation retrieval. Use this skill when: (1) Looking up protein sequences by accession, (2) Finding functional annotations, (3) Getting domain boundaries, (4) Finding homologs and variants, (5) Cross-referencing to PDB structures. For structure retrieval, use pdb. For sequence…