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-cnvgit 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-cnv)<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/spatial-cnv"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/spatial-cnv/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-cnv"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/spatial-cnv.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 | $0.00093 | $0.01271 |
| Opus 5.5 | $0.00037 | $0.00508 |
| Sonnet 5.5 | $0.00019 | $0.00254 |
| Haiku 4.5 | $0.00009 | $0.00127 |
Grade A, and why
spatial-cnv 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 13d 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 — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
spatial-cnv
When to use
Infer expression-based CNV with infercnvpy, or allele-aware CNV with R Numbat. Gene coordinates and an appropriate diploid reference are needed for interpretation.
Use from a step
from skills._sdk.notebook import load_skill
library = load_skill("spatial-cnv")
library.cnv(adata, reference_key='cell_type', reference_cat=['Normal'])
Run examples/example_step.py with the step runner for a synthetic, executable example.
API
cnv(adata, *, method: str='infercnvpy', reference_key: str | None=None, reference_cat: list[str] | str | None=None, window_size: int=100, step: int=10, method_params: dict | None=None, random_state: int=0, allele_counts: pd.DataFrame | None=None)
Infer CNV in place from log-normalized X or Numbat raw counts.
infercnvpy needs var chromosome/start/end. Numbat needs layers['counts'], phased allele_counts and a diploid reference annotation. Counts and metadata are exchanged with R inside a temporary directory.
:param adata: AnnData with expression and method-specific annotations. :param method: infercnvpy (CLI default) or numbat. :param reference_key: Observation column marking reference cells; None uses all cells. :param reference_cat: Reference labels; None uses the backend's global reference. :param window_size: Genomic smoothing window, 100 genes by default. :param step: Sliding-window stride, 10 genes by default. :param method_params: CLI options with underscores, such as infercnv_n_jobs=1; None keeps defaults listed in references/parameters.md. :param random_state: infercnvpy PCA/graph/clustering and Numbat R seed, default 0. :param allele_counts: Numbat long-form DataFrame with cell/snp_id/CHROM/POS/AD/DP/GT/gene; None reads the legacy obsm['allele_counts'] table. Multiple SNPs per cell are allowed. :returns: The same AnnData with CNV matrices, scores and JSON diagnostics. :raises ValueError: Missing genomic annotations, raw counts or invalid parameters. :raises ImportError: Missing infercnvpy or R backend; use install_skill_deps.
What ships with it
11 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 5.1 KB runs code
- examples/example_step.py 868 B runs code
- r_visualization/cnv_publication_template.R 2.8 KB
- r_visualization/README.md 1.4 KB
- references/methodology.md 8.1 KB
- references/output_contract.md 2.0 KB
- references/parameters.md 2.8 KB
- rscripts/numbat.R 5.3 KB
- spatial_cnv.py 43 KB runs code
- tests/test_api.py 4.2 KB runs code
- tests/test_spatial_cnv.py 4.4 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.
- 13d ago Changed · -14 lines 19ef708ec06e
- 1mo ago First seen · 133 lines · 93 tokens per session scan A ada7cb51a38a
spatial-cnv is a skill published in the GitHub repository TianGzlab/OmicsClaw (161 stars, last pushed 13d ago), licensed Apache-2.0. It adds 93 tokens to every session and 1,271 once invoked, about $0.0004 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.
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