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 GPTomics/bioSkills --skill hla-typinggit clone --depth 1 https://github.com/GPTomics/bioSkillsWrote 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/gptomics/bioskills/hla-typing)<a href="https://agentmods.dev/skills/gptomics/bioskills/hla-typing"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/hla-typing/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/gptomics/bioskills/hla-typing"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/hla-typing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00139 | $0.07609 |
| Opus 5 | $0.00069 | $0.03805 |
| Sonnet 5 | $0.00028 | $0.01522 |
| Haiku 4.5 | $0.00014 | $0.00761 |
Grade A, and why
bio-clinical-databases-hla-typing 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 7d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- bio-clinical-databases-hla-typing — 98% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 387 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: OptiType 1.3.5, HLA-LA 1.0.4, T1K 1.0.6 (Song 2023), Polysolver 4.0, HLA-HD 1.7.1, arcasHLA 0.6.0, StarPhase 1.0+ (PacBio), HIBAG 1.40+, samtools 1.19+, bwa-mem 0.7.17+. IPD-IMGT/HLA database release frequency is quarterly; tools must be re-bundled with the current release to capture new alleles (~38,000 alleles at Jan 2024; ~43,000+ by Jul 2025).
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show <package>thenhelp(module.function)to check signatures - CLI:
<tool> --versionthen<tool> --helpto confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. Tool reference-bundle vintage matters more than algorithm choice for non-European cohorts; a 2022-bundled HLA-LA will silently miss thousands of post-2022 alleles dominant in African and South Asian ancestry.
HLA Typing for Clinical Applications
'Determine HLA genotype for HSCT / neoantigen prediction / PGx screening' -> Call HLA class I (A, B, C) and class II (DRB1, DRB3/4/5, DQA1, DQB1, DPA1, DPB1) alleles at the resolution required by the downstream application.
- CLI (general-purpose all-rounder):
t1k --preset hla -1 R1.fq -2 R2.fq -f hla_reference.fa - CLI (class I gold standard from WES/WGS):
OptiTypePipeline.py -i R1.fq R2.fq -d - CLI (class I + II with PRG):
HLA-LA.pl --BAM input.bam --graph PRG_MHC_GRCh38_withIMGT - CLI (RNA-seq):
arcasHLA extract sample.bam -o out && arcasHLA genotype out/sample.extracted.fq.gz - CLI (long-read transplant-grade): PacBio HiFi StarPhase
- R (imputation from SNP arrays):
HIBAG::predict()with ancestry-stratified reference panel
Resolution Levels and What Each Application Requires
HLA nomenclature: HLA-A*02:01:01:01 = family : protein-changing : synonymous : intronic/UTR. Expression suffixes: N (null; DNA present, no protein expressed); L (low expression); S (secreted); Q (questionable); A (aberrant). A serologically apparent DR4-positive donor carrying DRB4*01:03:01:02N is functionally DR53-negative; a classic HSCT donor-selection failure.
What ships with it
3 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.
- 7d ago First seen · 387 lines · 139 tokens per session scan A 3af3b38a23ff
bio-clinical-databases-hla-typing is a skill published in the GitHub repository GPTomics/bioSkills (1,201 stars, last pushed 26d ago), licensed MIT. It adds 139 tokens to every session and 7,609 once invoked, about $0.0007 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 2026-09-03.
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