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Agent skill

Gtars

by aipoch in aipoch/medical-research-skills

A high-performance Rust toolkit (with Python bindings and a CLI) for genomic interval analysis; use it when you need fast overlap queries, coverage track generation, genomic tokenization for ML…

MITAuto-check passedResearch & Science

Install Gtars

skills CLI
$ npx skills add aipoch/medical-research-skills --skill gtars -a claude-code

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

GitHub CLI
$ gh skill install aipoch/medical-research-skills 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/aipoch/medical-research-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/'scientific-skills/Data Analysis/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
2k
Token cost
~1.1k tokens
SKILL.md length
333 words
Files
8 (incl. references)
Skills in repo
578
Repo updated
First seen
Licence
MIT

At a glance

A high-performance Rust toolkit (with Python bindings and a CLI) for genomic interval analysis; use it when you need fast overlap queries, coverage track generation, genomic tokenization for ML…

  • You need fast overlap queries
  • SKILL.md covers When to Use, Key Features, Dependencies and Example Usage, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Coverage track generation

What it does

Gtars is an agent skill from aipoch/medical-research-skills. A high-performance Rust toolkit (with Python bindings and a CLI) for genomic interval analysis; use it when you need fast overlap queries, coverage track generation, genomic tokenization for ML, reference sequence verification, or fragment processing.

Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including reference files (for example `gtars_audit_result_v1.json`, `references/cli.md` and `references/coverage.md`).

It sits in Research & Science, covering Bioinformatics and Natural language processing. It works with Python and Rust. The repository describes itself as: Hundreds of agent skills for medical research, including protocol design, data analysis, evidence insights, and academic writing. The licence is MIT.

When your agent uses it

  • You need fast overlap queries
  • Coverage track generation
  • Genomic tokenization for ML
  • Reference sequence verification

Example prompts

  • “/gtars”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit 686e09d. 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

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python and bash).

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

  • Network

    No URLs in SKILL.md.

    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 loads about 1.1k tokens when it runs, and up to ~7.2k if it reads all its reference files. Until then it costs about 64 tokens; SKILL.md has 333 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~64
When it runs · the whole SKILL.md, loaded when a task matches
~1.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~7.2k

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 aipoch/medical-research-skills at commit 686e09d, republished under its MIT licence (© aipoch). 333 words, ~1,119 tokens.

Download SKILL.mdSave it as .claude/skills/gtars/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.
name
gtars
description
A high-performance Rust toolkit (with Python bindings and a CLI) for genomic interval analysis; use it when you need fast overlap queries, coverage track generation, genomic tokenization for ML, reference sequence verification, or fragment processing.
license
MIT
author
AIPOCH

Source: https://github.com/aipoch/medical-research-skills

When to Use

  • Overlap and set operations on genomic intervals (e.g., peak/promoter overlap, variant annotation, shared-feature detection).
  • Coverage track generation from interval-like inputs (e.g., ATAC-seq/ChIP-seq/RNA-seq coverage for visualization in genome browsers).
  • Machine-learning preprocessing where genomic regions must be converted into discrete tokens (e.g., Transformer-style models, geniml-style pipelines).
  • Reference sequence management and verification (e.g., subsequence retrieval, digest calculation aligned with GA4GH refget concepts).
  • Single-cell fragment workflows (e.g., splitting fragments by barcode/cluster, scoring fragments against reference region sets).

Key Features

  • Rust performance with low overhead; designed for large genomic datasets.
  • Python bindings for integration into analysis notebooks/pipelines.
  • CLI tooling for batch processing and shell workflows.
  • Fast overlap detection via IGD-style indexing and interval operations.
  • Coverage track generation (WIG/BigWig workflows via the uniwig functionality).
  • Genomic tokenizers for ML-ready representations of genomic regions.
  • Reference sequence utilities (FASTA-backed stores, subsequence retrieval, digesting).
  • Fragment processing and scoring for common single-cell genomics tasks.

Additional module-specific guidance may be available in: references/overlap.md, references/coverage.md, references/tokenizers.md, references/refget.md, references/python-api.md, and references/cli.md.

Dependencies

  • Python package: gtars (version not specified in the source document)
  • Rust toolchain (for CLI install): cargo (version not specified)
  • Rust crate: gtars = "0.1" (as shown in the example)

Example Usage

Python: overlap analysis workflow (runnable)
python
import gtars

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

# Find overlaps (peaks that overlap promoters)
overlapping_peaks = peaks.filter_overlapping(promoters)

# Export results
overlapping_peaks.to_bed("peaks_in_promoters.bed")
CLI: generate coverage tracks (runnable)
bash
# Generate WIG coverage at a given resolution
gtars uniwig generate --input atac_fragments.bed --output coverage.wig --resolution 10

# Generate BigWig coverage for genome browser visualization
gtars uniwig generate --input atac_fragments.bed --output coverage.bw --format bigwig
Python: ML tokenization (runnable)
python
import gtars
from gtars.tokenizers import TreeTokenizer

# Load regions and build a tokenizer from BED
regions = gtars.RegionSet.from_bed("training_peaks.bed")
tokenizer = TreeTokenizer.from_bed_file("training_peaks.bed")

# Tokenize each region into a discrete representation
tokens = [tokenizer.tokenize(r.chromosome, r.start, r.end) for r in regions]

print(tokens[:5])

Implementation Details

  • Interval overlap & indexing: Overlap queries are designed around an IGD-like index to accelerate repeated interval queries (build once, query many). Typical parameters are chromosome, start, end; results are overlapping intervals or derived set operations.
  • Coverage generation (uniwig): Produces coverage tracks from interval/fragments input. Common knobs include output format (e.g., WIG vs BigWig) and resolution/binning for track granularity.
  • Tokenization: Tokenizers (e.g., TreeTokenizer) map genomic coordinates to discrete tokens suitable for ML pipelines. Token vocabularies are commonly derived from a BED-defined training region universe.
  • Reference sequence store: FASTA-backed reference access supports subsequence retrieval and digesting/verification workflows aligned with refget-style usage.
  • Fragment workflows: Fragment splitting and scoring operate on fragment-like inputs (often TSV/BED-style) and can be used for barcode/cluster partitioning and enrichment-style scoring against reference region sets.

© aipoch, 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 7 other files (references) in scientific-skills/Data Analysis/gtars of aipoch/medical-research-skills.

  • SKILL.md
  • gtars_audit_result_v1.json
  • 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 686e09d

Compare with similar skills

Gtars next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Gtars compared with similar skills
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Gtarsjaechang-hits/SciAgent-Skills371—~4.2kAutomated safety check: PassMIT
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Alphagenome Single Variant Analysisgoogle-deepmind/science-skills3.2k2 repos~3kAutomated safety check: NotesApache-2.0
13C Metabolic Flux AnalysisK-Dense-AI/scientific-agent-skills48k1 repos~3.2kAutomated safety check: PassMIT

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Works with

Categories

Questions about Gtars

What does Gtars do?

A high-performance Rust toolkit (with Python bindings and a CLI) for genomic interval analysis; use it when you need fast overlap queries, coverage track generation, genomic tokenization for ML…. Gtars is an agent skill from aipoch/medical-research-skills. A high-performance Rust toolkit (with Python bindings and a CLI) for genomic interval analysis; use it when you need fast overlap queries, coverage track generation, genomic tokenization for ML, reference sequence verification, or fragment processing.

When should I use Gtars?

Gtars fits situations like: you need fast overlap queries; coverage track generation; genomic tokenization for ML; reference sequence verification.

How do I install Gtars in Claude Code?

Run `npx skills add aipoch/medical-research-skills --skill gtars -a claude-code`. Or copy the skill folder (scientific-skills/Data Analysis/gtars in aipoch/medical-research-skills) into .claude/skills/gtars in your project. Claude Code loads it when a task matches its description.

How do I install Gtars in Codex?

Run `npx skills add aipoch/medical-research-skills --skill gtars -a codex`. Or copy the skill folder (scientific-skills/Data Analysis/gtars in aipoch/medical-research-skills) into .agents/skills/gtars in your project. Codex loads it when a task matches its description.

Can I use Gtars 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 aipoch/medical-research-skills --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 need to run?

SKILL.md names no scripts, command-line tools or credentials: Gtars is instructions for the agent only. Our summary lists: Python 3.

Does Gtars access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Gtars 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 use?

Gtars is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Gtars use?

About 1.1k tokens (SKILL.md is roughly 4.5k 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 6.1k tokens, read only when the agent opens those files.

What are the alternatives to Gtars?

Skills that share tags, products or a category with Gtars: Gtars (K-Dense-AI/scientific-agent-skills, 48k stars), Gtars (jaechang-hits/SciAgent-Skills, 371 stars), Gtars Genomic Interval Toolkit (davila7/claude-code-templates, 32k stars) and Alphagenome Single Variant Analysis (google-deepmind/science-skills, 3.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Gtars?

aipoch (a GitHub organization) maintains it in aipoch/medical-research-skills, which has 1,978 GitHub stars. The repository holds 578 skills in this directory. The repository was last updated on September 17, 2026.

Source: aipoch/medical-research-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.