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Agent skill

Gtars Genomic Interval Toolkit

by davila7 in davila7/claude-code-templates

Works with genomic intervals using gtars, a Rust toolkit with Python bindings: overlap detection, coverage tracks, tokenization for ML models and reference sequences.

MITAuto-check passedResearch & Science

Install Gtars Genomic Interval Toolkit

skills CLI
$ npx skills add davila7/claude-code-templates --skill gtars -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install davila7/claude-code-templates gtars --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/davila7/claude-code-templates.git skills-src && mkdir -p .claude/skills && cp -r skills-src/cli-tool/components/skills/scientific/gtars .claude/skills/gtars && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
gtars
GitHub stars
33k
Used in
11 other repos
Token cost
~1.9k tokens
SKILL.md length
570 words
Files
7 (incl. references)
Skills in repo
479
Repo updated
First seen
Licence
MIT

At a glance

Works with genomic intervals using gtars, a Rust toolkit with Python bindings: overlap detection, coverage tracks, tokenization for ML models and reference sequences.

  • Works in 6 steps: Overlap Detection and IGD Indexing → Coverage Track Generation → Genomic Tokenization → …
  • Finding overlaps between two sets of BED regions
  • SKILL.md covers Overview, Installation, Core Capabilities and Common Workflows, plus 6 more sections
  • Calls cargo and uv

What it does

Gtars is a Rust toolkit for BED-style genomic interval data, offered as Python bindings, a command-line tool and a Rust library. The skill points the agent to its modules: IGD indexing for finding overlaps, such as regulatory elements, variant annotation or ChIP-seq peaks, and the uniwig module for generating coverage tracks as WIG or BigWig files.

It also covers tokenizing genomic regions into discrete tokens for machine learning models, including use with the geniml library, plus fragment analysis in single-cell genomics and reference sequence retrieval and validation. Reference files document the CLI, coverage, overlap, the Python API, refget and tokenizers. The CLI is installed with cargo and needs Rust, while the Python bindings install with `uv pip install gtars`.

When your agent uses it

  • Finding overlaps between two sets of BED regions
  • Generating a BigWig coverage track from fragment files
  • Preparing genomic regions as tokens for a machine learning model
  • Comparing ChIP-seq peaks against annotated regulatory elements

Example prompts

  • “Build an IGD index from enhancers.bed and report which of my peaks overlap it.”
  • “Create a BigWig coverage track from fragments.bed with gtars uniwig.”
  • “Tokenize the regions in my BED file so I can feed them to a transformer.”

Requirements

  • Python with the `gtars` package
  • Rust and Cargo to build the command-line tools

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Overlap Detection and IGD Indexing
  2. Coverage Track Generation
  3. Genomic Tokenization
  4. Reference Sequence Management
  5. Fragment Processing
  6. Fragment Scoring

What it can do on your machine

Read from SKILL.md and the folder at commit c0ca7da. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • cargo
    • uv

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use uv, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Gtars Genomic Interval Toolkit loads about 1.9k tokens when it runs, and up to ~7.6k if it reads all its reference files. Until then it costs about 72 tokens; SKILL.md has 570 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~72
When it runs · the whole SKILL.md, loaded when a task matches
~1.9k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~7.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from davila7/claude-code-templates at commit c0ca7da, republished under its MIT licence (© davila7). 570 words, ~1,937 tokens.

Download SKILL.mdSave it as .claude/skills/gtars/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.
name
gtars
description
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.

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 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 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:

python
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

Quick example:

bash
# Generate BigWig coverage track
gtars uniwig generate --input fragments.bed --output coverage.bw --format bigwig

See references/coverage.md for detailed coverage analysis workflows.

3. Genomic Tokenization

Convert genomic regions into discrete tokens for machine learning applications, particularly for deep learning models on genomic data.

When to use:

  • Preprocessing for genomic ML models
  • Integration with geniml library
  • Creating position encodings
  • Training transformer models on genomic sequences

Quick example:

python
from gtars.tokenizers import TreeTokenizer

tokenizer = TreeTokenizer.from_bed_file("training_regions.bed")
token = tokenizer.tokenize("chr1", 1000, 2000)

See references/tokenizers.md for tokenization documentation.

4. Reference Sequence Management

Handle reference genome sequences and compute digests following the GA4GH refget protocol.

When to use:

  • Validating reference genome integrity
  • Extracting specific genomic sequences
  • Computing sequence digests
  • Cross-reference comparisons

Quick example:

python
# Load reference and extract sequences
store = gtars.RefgetStore.from_fasta("hg38.fa")
sequence = store.get_subsequence("chr1", 1000, 2000)

See references/refget.md for reference sequence operations.

5. Fragment Processing

Split and analyze fragment files, particularly useful for single-cell genomics data.

When to use:

  • Processing single-cell ATAC-seq data
  • Splitting fragments by cell barcodes
  • Cluster-based fragment analysis
  • Fragment quality control

Quick example:

bash
# Split fragments by clusters
gtars fragsplit cluster-split --input fragments.tsv --clusters clusters.txt --output-dir ./by_cluster/

See references/cli.md for fragment processing commands.

6. Fragment Scoring

Score fragment overlaps against reference datasets.

When to use:

  • Evaluating fragment enrichment
  • Comparing experimental data to references
  • Quality metrics computation
  • Batch scoring across samples

Quick example:

bash
# Score fragments against reference
gtars scoring score --fragments fragments.bed --reference reference.bed --output scores.txt
Show full SKILL.md (217 more words)Show less

Common Workflows

Workflow 1: Peak Overlap Analysis

Identify overlapping genomic features:

python
import gtars

# Load two region sets
peaks = gtars.RegionSet.from_bed("chip_peaks.bed")
promoters = gtars.RegionSet.from_bed("promoters.bed")

# Find overlaps
overlapping_peaks = peaks.filter_overlapping(promoters)

# Export results
overlapping_peaks.to_bed("peaks_in_promoters.bed")
Workflow 2: Coverage Track Pipeline

Generate coverage tracks for visualization:

bash
# Step 1: Generate coverage
gtars uniwig generate --input atac_fragments.bed --output coverage.wig --resolution 10

# Step 2: Convert to BigWig for genome browsers
gtars uniwig generate --input atac_fragments.bed --output coverage.bw --format bigwig
Workflow 3: ML Preprocessing

Prepare genomic data for machine learning:

python
from gtars.tokenizers import TreeTokenizer
import gtars

# Step 1: Load training regions
regions = gtars.RegionSet.from_bed("training_peaks.bed")

# Step 2: Create tokenizer
tokenizer = TreeTokenizer.from_bed_file("training_peaks.bed")

# Step 3: Tokenize regions
tokens = [tokenizer.tokenize(r.chromosome, r.start, r.end) for r in regions]

# Step 4: Use tokens in ML pipeline
# (integrate with geniml or custom models)

Python vs CLI Usage

Use Python API when:

  • Integrating with analysis pipelines
  • Need programmatic control
  • Working with NumPy/Pandas
  • Building custom workflows

Use CLI when:

  • Quick one-off analyses
  • Shell scripting
  • Batch processing files
  • Prototyping workflows

Reference Documentation

Comprehensive module documentation:

  • references/python-api.md - Complete Python API reference with RegionSet operations, NumPy integration, and data export
  • references/overlap.md - IGD indexing, overlap detection, and set operations
  • references/coverage.md - Coverage track generation with uniwig
  • references/tokenizers.md - Genomic tokenization for ML applications
  • references/refget.md - Reference sequence management and digests
  • references/cli.md - Command-line interface complete reference

Integration with geniml

Gtars serves as the foundation for the geniml Python package, providing core genomic interval operations for machine learning workflows. When working on geniml-related tasks, use gtars for data preprocessing and tokenization.

Performance Characteristics

  • Native Rust performance: Fast execution with low memory overhead
  • Parallel processing: Multi-threaded operations for large datasets
  • Memory efficiency: Streaming and memory-mapped file support
  • Zero-copy operations: NumPy integration with minimal data copying

Data Formats

Gtars works with standard genomic formats:

  • BED: Genomic intervals (3-column or extended)
  • WIG/BigWig: Coverage tracks
  • FASTA: Reference sequences
  • Fragment TSV: Single-cell fragment files with barcodes

Error Handling and Debugging

Enable verbose logging for troubleshooting:

python
import gtars

# Enable debug logging
gtars.set_log_level("DEBUG")
bash
# CLI verbose mode
gtars --verbose <command>

© davila7, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 6 other files (references) in cli-tool/components/skills/scientific/gtars of davila7/claude-code-templates.

  • SKILL.md
  • references/cli.md
  • references/coverage.md
  • references/overlap.md
  • references/python-api.md
  • references/refget.md
  • references/tokenizers.md

Open the folder on GitHubat commit c0ca7da

Used in 11 other repositories

We found 15 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 11 other GitHub owners. This page covers the copy in davila7/claude-code-templates, which our catalogue first saw on October 7, 2026.

Compare with similar skills

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GtarsK-Dense-AI/scientific-agent-skills48k1 repos~3.8kAutomated safety check: NotesMIT
Gtarsaipoch/medical-research-skills1.9k—~1.1kAutomated safety check: PassMIT
Experiment LabCitrus-bit/Anaxa120—~2.1kAutomated safety check: PassMIT
Gtarsjaechang-hits/SciAgent-Skills374—~4.2kAutomated safety check: PassMIT

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Works with

Categories

Questions about Gtars Genomic Interval Toolkit

What does Gtars Genomic Interval Toolkit do?

Works with genomic intervals using gtars, a Rust toolkit with Python bindings: overlap detection, coverage tracks, tokenization for ML models and reference sequences. Gtars is a Rust toolkit for BED-style genomic interval data, offered as Python bindings, a command-line tool and a Rust library. The skill points the agent to its modules: IGD indexing for finding overlaps, such as regulatory elements, variant annotation or ChIP-seq peaks, and the uniwig module for generating coverage tracks as WIG or BigWig files.

When should I use Gtars Genomic Interval Toolkit?

Gtars Genomic Interval Toolkit fits situations like: finding overlaps between two sets of BED regions; generating a BigWig coverage track from fragment files; preparing genomic regions as tokens for a machine learning model; comparing ChIP-seq peaks against annotated regulatory elements.

How do I install Gtars Genomic Interval Toolkit in Claude Code?

Run `npx skills add davila7/claude-code-templates --skill gtars -a claude-code`. Or copy the skill folder (cli-tool/components/skills/scientific/gtars in davila7/claude-code-templates) into .claude/skills/gtars in your project. Claude Code loads it when a task matches its description.

How do I install Gtars Genomic Interval Toolkit in Codex?

Run `npx skills add davila7/claude-code-templates --skill gtars -a codex`. Or copy the skill folder (cli-tool/components/skills/scientific/gtars in davila7/claude-code-templates) into .agents/skills/gtars in your project. Codex loads it when a task matches its description.

Can I use Gtars Genomic Interval Toolkit in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add davila7/claude-code-templates --skill gtars -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/gtars, .gemini/skills/gtars, .github/skills/gtars and .opencode/skills/gtars in your project.

What does Gtars Genomic Interval Toolkit need to run?

Going by SKILL.md and its folder, Gtars Genomic Interval Toolkit needs the command-line tools its instructions call (cargo and uv). Our summary lists: Python with the `gtars` package; Rust and Cargo to build the command-line tools.

Does Gtars Genomic Interval Toolkit access the network?

SKILL.md contains no URLs. Its commands use uv, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Gtars Genomic Interval Toolkit safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Gtars Genomic Interval Toolkit use?

Gtars Genomic Interval Toolkit is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Gtars Genomic Interval Toolkit use?

About 1.9k tokens (SKILL.md is roughly 7.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 5.6k tokens, read only when the agent opens those files.

What are the alternatives to Gtars Genomic Interval Toolkit?

Skills that share tags, products or a category with Gtars Genomic Interval Toolkit: Bio Temporal Genomics Temporal Grn (GPTomics/bioSkills, 1.2k stars), Gtars (K-Dense-AI/scientific-agent-skills, 48k stars), Gtars (aipoch/medical-research-skills, 1.9k stars) and Experiment Lab (Citrus-bit/Anaxa, 120 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Gtars Genomic Interval Toolkit?

davila7 (a GitHub user) maintains it in davila7/claude-code-templates, which has 32,512 GitHub stars. The repository holds 479 skills in this directory. The repository was last updated on October 10, 2026.

Source: davila7/claude-code-templates on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.