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 sc-standardize-inputgit 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/sc-standardize-input)<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/sc-standardize-input"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/sc-standardize-input/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/sc-standardize-input"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/sc-standardize-input.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 | $0.00073 | $0.01413 |
| Opus 5.5 | $0.00029 | $0.00565 |
| Sonnet 5.5 | $0.00015 | $0.00283 |
| Haiku 4.5 | $0.00007 | $0.00141 |
Grade A, and why
sc-standardize-input 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 17d 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 — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
sc-standardize-input
When to use
The user has a single-cell expression file from outside OmicsClaw (a public
.h5ad, a 10X mtx directory, a .loom, etc.) and needs the canonical
AnnData contract every downstream scRNA skill assumes: raw counts in
layers["counts"] and adata.raw, harmonised feature names, and a
uns["omicsclaw_matrix_contract"] provenance record. Run this once before
sc-qc / sc-preprocessing / etc.
Use from a step
standardizer = load_skill("sc-standardize-input")
adata = standardizer.standardize(read_input("external.h5ad"), species="human")
write_output(adata, "intermediate/adata_standardized.h5ad")
standardize returns a new AnnData. It does not filter, normalize or
cluster. A runnable PBMC example is in examples/example_step.py.
API
standardize(adata, *, species: str='auto')
Return a canonical copy with count-like X, layers['counts'] and raw.
Select counts from layers['counts'], aligned raw or X, in that order; harmonize feature names and record the matrix and input contracts.
:param adata: AnnData with at least one count-like expression matrix. :param species: 'auto' uses infer_species; 'human' or 'mouse' overrides it. :returns: A new AnnData. run_info reports the selected matrix and warnings. :raises ValueError: The species is invalid or no count-like matrix is present.
infer_species(adata) -> str
Infer human or mouse from feature names, defaulting to human.
:param adata: AnnData whose feature names or gene-symbol metadata are examined. :returns: The species hint used by standardize(species="auto").
run_info(adata, *, keep: bool=True) -> dict
Read standardization diagnostics stored as JSON in adata.uns.
:param adata: The AnnData returned by standardize. :param keep: False removes these diagnostics after reading; default True. :returns: A dictionary, or an empty dictionary before standardize runs.
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.
- 17d ago Changed · +44 lines ddeb2e1140b1
- 1mo ago First seen · 90 lines · 73 tokens per session scan A e018f34b0d01
sc-standardize-input is a skill published in the GitHub repository TianGzlab/OmicsClaw (161 stars, last pushed 17d ago), licensed Apache-2.0. It adds 73 tokens to every session and 1,413 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.
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