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 genomics-cnv-callinggit 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/genomics-cnv-calling)<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/genomics-cnv-calling"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/genomics-cnv-calling/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/genomics-cnv-calling"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/genomics-cnv-calling.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 | $0.00095 | $0.01040 |
| Opus 5.5 | $0.00038 | $0.00416 |
| Sonnet 5.5 | $0.00019 | $0.00208 |
| Haiku 4.5 | $0.00010 | $0.00104 |
Grade A, and why
genomics-cnv-calling 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 — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
genomics-cnv-calling
When to use
Load this skill for the file-based analysis named in the description. The function library and CLI share the same calculations; no external aligner, assembler, caller or annotation service is started.
Use from a step
from skills._sdk.notebook import load_skill, read_input, write_output
library = load_skill("genomics-cnv-calling")
data = read_input("input.csv", reader=library.read_bins)
result = library.analyze(data)
write_output(result, "tables/result.csv")
write_output(library.copy_ratio_figure(result), "figures/distribution.png")
Run examples/example_step.py through the step runner for a small,
hand-worked synthetic fixture. It asserts known summary values.
The reader materializes the input in memory; use bounded FASTQ reads or
pre-filter large genomic files before loading them.
API
read_bins(path: str | Path) -> pd.DataFrame
Read a bin CSV through read_input(path, reader=library.read_bins).
:param path: CSV containing chrom, start, end and log2_ratio. :returns: Bin table. :raises ValueError: The CSV cannot be parsed.
analyze(data: pd.DataFrame, *, method: str='cbs', alpha: float=0.01) -> pd.DataFrame
Return new CNV segments from bin-level log2 ratios; leave data unchanged.
:param data: Bins with chrom, start, end and finite log2_ratio. :param method: CLI default cbs, or none to classify individual bins. :param alpha: CLI default 0.01; smaller values require stronger splits. :returns: Segments with cn_state, estimated_cn and attrs['run_info']. :raises ValueError: Columns, coordinates, method or alpha are invalid.
run_info(data: pd.DataFrame, *, keep: bool=True) -> dict
Return analysis diagnostics and numeric summary.
:param data: Result returned by analyze. :param keep: Keep diagnostics by default; the CLI passes False. :returns: Independent copy of run diagnostics. :raises ValueError: analyze has not populated diagnostics.
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 · +34 lines 07b7ec1f8865
- 1mo ago First seen · 95 lines · 95 tokens per session scan A 6809295b1398
genomics-cnv-calling is a skill published in the GitHub repository TianGzlab/OmicsClaw (161 stars, last pushed 18d ago), licensed Apache-2.0. It adds 95 tokens to every session and 1,040 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 2026-08-30.
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