Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/dralkh/seerainpx agentmods add skills/dralkh/seerai/gtarsWrote 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/dralkh/seerai/gtars)<a href="https://agentmods.dev/skills/dralkh/seerai/gtars"><img src="https://agentmods.dev/badge/skills/dralkh/seerai/gtars.svg" alt="Measured on agentmods" 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 | $0.00050 | $0.01779 |
| Opus 5 | $0.00025 | $0.00890 |
| Sonnet 5 | $0.00010 | $0.00356 |
| Haiku 4.5 | $0.00005 | $0.00178 |
Grade A, and why
gtars 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.
This is a copy
98% identical to gtars — 6 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 283 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Gtars: Genomic Tools and Algorithms in Rust
Overview
Gtars is a high-performance Rust toolkit for manipulating, analyzing, and processing genomic interval data. It provides specialized tools for overlap detection, coverage analysis, tokenization for machine learning, and reference sequence management.
Use this skill when working with:
- Genomic interval files (BED format)
- Overlap detection between genomic regions
- Coverage track generation (WIG, BigWig)
- Genomic ML preprocessing and tokenization
- Fragment analysis in single-cell genomics
- Reference sequence retrieval and validation
Installation
Python Installation
Install gtars Python bindings:
uv pip install gtars
CLI Installation
Install command-line tools (requires Rust/Cargo):
# Install with all features
cargo install gtars-cli --features "uniwig overlaprs igd bbcache scoring fragsplit"
# Or install specific features only
cargo install gtars-cli --features "uniwig overlaprs"
Rust Library
Add to Cargo.toml for Rust projects:
[dependencies]
gtars = { version = "0.1", features = ["tokenizers", "overlaprs"] }
Core Capabilities
Gtars is organized into specialized modules, each focused on specific genomic analysis tasks:
1. Overlap Detection and IGD Indexing
Efficiently detect overlaps between genomic intervals using the Integrated Genome Database (IGD) data structure.
When to use:
- Finding overlapping regulatory elements
- Variant annotation
- Comparing ChIP-seq peaks
- Identifying shared genomic features
Quick example:
import gtars
# Build IGD index and query overlaps
igd = gtars.igd.build_index("regions.bed")
overlaps = igd.query("chr1", 1000, 2000)
See references/overlap.md for comprehensive overlap detection documentation.
2. Coverage Track Generation
Generate coverage tracks from sequencing data with the uniwig module.
When to use:
- ATAC-seq accessibility profiles
- ChIP-seq coverage visualization
- RNA-seq read coverage
- Differential coverage analysis
What ships with it
6 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 · 283 lines · 50 tokens per session scan A 5177f5f4fe9f
gtars is a skill published in the GitHub repository dralkh/seerai (76 stars, last pushed 2mo ago), licensed MIT. It adds 50 tokens to every session and 1,779 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 98% identical to gtars, differing in 6 lines, and is treated as a copy.
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