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-phasinggit 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-phasing)<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/genomics-phasing"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/genomics-phasing/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-phasing"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/genomics-phasing.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 | $0.00077 | $0.01007 |
| Opus 5.5 | $0.00031 | $0.00403 |
| Sonnet 5.5 | $0.00015 | $0.00201 |
| Haiku 4.5 | $0.00008 | $0.00101 |
Grade A, and why
genomics-phasing 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 — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
genomics-phasing
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-phasing")
data = read_input("input.vcf", reader=library.read_records)
result = library.analyze(data)
write_output(result, "tables/result.csv")
write_output(library.distribution_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_records(path: str | Path) -> pd.DataFrame
Read records through read_input(path, reader=library.read_records).
:param path: Existing input file in the format documented under Inputs and outputs. :returns: Parsed records as a DataFrame. :raises ValueError: Input values or file structure cannot be parsed.
analyze(data: pd.DataFrame) -> pd.DataFrame
Compute phasing summaries and return a new table, leaving data unchanged.
A header-only VCF yields an empty table with zero-valued summary metrics.
:param data: Records containing chrom, pos, gt, is_phased, is_het, phase_set.
:returns: Result table with diagnostics and summary in attrs['run_info']. :raises ValueError: Required columns are absent or records are invalid.
run_info(data: pd.DataFrame, *, keep: bool=True) -> dict
Return the analysis diagnostics and summary.
:param data: Result returned by analyze. :param keep: Keep diagnostics by default; the CLI passes False. :returns: Independent diagnostics dictionary. :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 · +42 lines a0397b978255
- 1mo ago First seen · 89 lines · 77 tokens per session scan A 1c83822184ef
genomics-phasing is a skill published in the GitHub repository TianGzlab/OmicsClaw (161 stars, last pushed 18d ago), licensed Apache-2.0. It adds 77 tokens to every session and 1,007 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 2026-08-30.
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