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-variant-annotationgit 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-variant-annotation)<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/genomics-variant-annotation"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/genomics-variant-annotation.svg" alt="Measured on agentmods" 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 | $0.00091 | $0.01231 |
| Opus 5 | $0.00046 | $0.00616 |
| Sonnet 5 | $0.00018 | $0.00246 |
| Haiku 4.5 | $0.00009 | $0.00123 |
Grade A, and why
genomics-variant-annotation 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 8d 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 — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
genomics-variant-annotation
When to use
The user has a CSV containing per-variant annotations (lowercase
columns chrom, pos, ref, alt, consequence, impact,
gene, optionally cadd_phred) — typically the output of running
VEP, snpEff, or ANNOVAR upstream and exporting the resulting VCF
to CSV (e.g. via bcftools +split-vep). This skill computes
per-IMPACT counts, top consequences, and the count of distinct
genes affected.
The script does NOT run VEP / snpEff / ANNOVAR, and does NOT
parse a raw VCF — it only reads CSV. For raw calling use
genomics-variant-calling; for VCF filtering use
genomics-vcf-operations.
Inputs & Outputs
Inputs
- File types:
.csv - Accepts artifact
genomics.variant_table(csv)
Outputs
tables/annotated_variants.csvtables/impact_distribution.csvreport.mdresult.json- Produces artifact
genomics.annotated_variantsastables/annotated_variants.csv(csv)
Flow
- Load CSV (
--input <annotated.csv>) or generate a demo annotated CSV atoutput_dir/demo_annotated_variants.csvwith--n-variantsrecords (variant_annotation.py:227). - Read columns directly via
pd.read_csv(variant_annotation.py:356) — no VCF / VEP / snpEff parser exists in this skill. - Aggregate per-IMPACT counts (
variant_annotation.py:240); pick top-N consequences (:241); count distinct genes touched (:252). - Write
tables/annotated_variants.csv(variant_annotation.py:366) +tables/impact_distribution.csv(:377) +report.md+result.json(:383).
Gotchas
- CSV-only — no VCF parser exists.
variant_annotation.py:356ispd.read_csv(input_path); passing a.vcfraisesValueError("Could not parse input file: ...")atvariant_annotation.py:358. Convert VCFs to CSV first withbcftools +split-vep -d -f '%CHROM,%POS,%REF,%ALT,%CSQ\n'and post-process to the required column names. - Required CSV columns are LOWERCASE. Code reads
df["impact"](:240),df["consequence"](:241),df["gene"](:252), and optionallydf["cadd_phred"](:271). A CSV withIMPACT/Consequence/GeneraisesKeyError. --inputREQUIRED unless--demo.variant_annotation.py:348raisesValueError("--input required when not using --demo"); non-existent paths raiseFileNotFoundErrorat:351.- No annotator is invoked. This skill consumes an already-annotated CSV — it does NOT run VEP / snpEff / ANNOVAR. Run an annotator upstream and convert its output to CSV.
- CADD scoring is optional. When
cadd_phredis absent the report omits the CADD section; do NOT add a placeholder NaN column or the value-counts will mis-render. - Demo CSV uses fixed IMPACT proportions (~10% HIGH, 30% MODERATE, 50% LOW, 10% MODIFIER). Useful for orchestrator smoke tests; not biologically meaningful.
What ships with it
5 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.
- 8d ago First seen · 93 lines · 91 tokens per session scan A c74fb1eade16
genomics-variant-annotation is a skill published in the GitHub repository TianGzlab/OmicsClaw (160 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 91 tokens to every session and 1,231 once invoked, about $0.0005 per session on Opus 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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