Agent skill · Testing & QA

gtars

High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.

BioTender-maxgithub.com/BioTender-maxGitHub ↗
claude-codeNOASSERTION
Install
npx skills add BioTender-max/awesome-bio-agent-skills --skill gtars --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 7
SKILL.md size: 8 KB
Bundled scripts: none
Path: skills/kdense/gtars/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 135
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# 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: ```bash uv pip install gtars ``` ### CLI Installation Install command-line tools (requires Rust/Cargo): ```bash # 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: ```toml [dependencies] gtars = { version = "0.1", features = ["tokenizers", "overlaprs"] } ``` ## Core Capabilities Gtars is organized into specialized modul

What's inside
Steps it walks through
  1. Overview
  2. Installation
  3. Python Installation
  4. CLI Installation
  5. Rust Library
  6. Core Capabilities
  7. 1. Overlap Detection and IGD Indexing
  8. 2. Coverage Track Generation
  9. 3. Genomic Tokenization
  10. 4. Reference Sequence Management
  11. 5. Fragment Processing
  12. 6. Fragment Scoring
  13. Common Workflows
  14. Workflow 1: Peak Overlap Analysis
Ships with 6 files
  • references/cli.md
  • references/coverage.md
  • references/overlap.md
  • references/python-api.md
  • references/refget.md
  • references/tokenizers.md
Commands it runs
uv pip install gtars
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"
Generate BigWig coverage track
gtars uniwig generate --input fragments.bed --output coverage.bw --format bigwig
Split fragments by clusters
gtars fragsplit cluster-split --input fragments.tsv --clusters clusters.txt --output-dir ./by_cluster/
Score fragments against reference
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About this skill
What does the gtars skill do?

High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.

How do I install it?

Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill gtars --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going