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 genetic-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/genetic-variant-annotation)<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/genetic-variant-annotation"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/genetic-variant-annotation/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/genetic-variant-annotation"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/genetic-variant-annotation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00004 | $0.04794 |
| Opus 5 | $0.00002 | $0.02397 |
| Sonnet 5 | $0.00001 | $0.00959 |
| Haiku 4.5 | $0.00000 | $0.00479 |
Grade A, and why
Genetic 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 9d 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 — 446 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Genetic Variant Annotation
Annotate genomic variants in VCF files with functional effects, clinical significance, and pathogenicity predictions.
When to Use This Skill
Use this skill when you have:
- ✅ VCF files from variant calling (GATK, bcftools, FreeBayes, etc.)
- ✅ Need functional annotation (gene impact, consequence types)
- ✅ Need clinical interpretation (pathogenicity, ClinVar, ACMG classification)
- ✅ Need variant filtering by consequence, frequency, or pathogenicity
- ✅ Working with human, mouse, or 38,000+ other genomes
Use cases:
- Clinical diagnostics (identify pathogenic variants in patient samples)
- Population genetics (annotate with allele frequencies)
- Cancer genomics (somatic variant annotation with COSMIC)
- Research variant prioritization (rank by predicted impact)
- Non-model organism analysis (38,000+ genomes supported)
Don't use for:
- ❌ Variant calling (use GATK/bcftools first) → this skill starts with VCF files
- ❌ Structural variant annotation (limited support, use specialized tools)
Quick Start (Example Data)
Test this skill with example variants:
from load_example_data import load_clinvar_pathogenic_sample
data = load_clinvar_pathogenic_sample() # Creates test VCF (~10 variants)
print(f"Example VCF created: {data['vcf_path']}")
What you get:
- Dataset: 10 ClinVar pathogenic variants (BRCA1/BRCA2)
- Expected: ~6 HIGH impact, ~8 pathogenic classifications
- Runtime: VEP ~5 min, SNPEff ~2 min
Next steps: Use this VCF in the Standard Workflow below.
Optional validation test (for developers): python assets/eval/simple_test.py to verify installation.
For your own data: See Inputs and Clarification Questions.
Installation
Choose one annotation tool:
| Tool | Best For | Installation | Cache/Database Size |
|---|---|---|---|
| VEP | Human clinical, comprehensive annotations | conda install -c bioconda ensembl-vep |
15-20 GB (one-time) |
| SNPEff | Non-model organisms, quick analysis | conda install -c bioconda snpeff |
2-5 GB per genome |
What ships with it
26 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.
- references/auto_installation_implementation.md 7.4 KB
- references/consequence_terms.md 9.4 KB
- references/filtering_strategies.md 19 KB
- references/installation_guide.md 10 KB
- references/pathogenicity_interpretation.md 12 KB
- references/qc_guidelines.md 15 KB
- references/snpeff_best_practices.md 10 KB
- references/tool_selection_guide.md 12 KB
- references/troubleshooting_guide.md 11 KB
- references/vep_best_practices.md 9.3 KB
- scripts/annotate_genes.py 8.0 KB runs code
- scripts/export_results.py 15 KB runs code
- scripts/filter_variants.py 10 KB runs code
- scripts/install_tools.py 6.2 KB runs code
- scripts/load_example_data.py 5.2 KB runs code
- scripts/parse_snpeff_output.py 7.5 KB runs code
- scripts/parse_vep_output.py 6.8 KB runs code
- scripts/plot_variant_distribution.py 11 KB runs code
- scripts/prioritize_variants.py 9.0 KB runs code
- scripts/run_snpeff.py 10 KB runs code
- scripts/run_vep.py 9.2 KB runs code
- scripts/select_tool.py 6.0 KB runs code
- scripts/test_complete_workflow.py 11 KB runs code
- scripts/test_pickle_load.py 3.1 KB runs code
- scripts/validate_vcf.py 9.9 KB runs code
- scripts/verify_changes.py 6.8 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.
- 9d ago First seen · 446 lines · 4 tokens per session scan A b470d3cf5b6f
Genetic Variant Annotation is a skill published in the GitHub repository TianGzlab/OmicsClaw (160 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 4 tokens to every session and 4,794 once invoked, about $0.0000 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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