Agent skill · Testing & QA

polars-bio

High-performance genomic interval operations and bioinformatics file I/O on Polars DataFrames. Overlap, nearest, merge, coverage, complement, subtract for BED/VCF/BAM/GFF intervals. Streaming, cloud-native, faster bioframe alternative.

K-Dense-AIgithub.com/K-Dense-AIGitHub ↗
claude-codecan modify filesMIT
Install
npx skills add K-Dense-AI/scientific-agent-skills --skill polars-bio --agent claude-code

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

Facts
Files in the skill folder: 7
SKILL.md size: 15 KB
Bundled scripts: none
Version: 1.0
Allowed tools: ReadWriteEditBash
Requires: Requires Python 3.11–3.14 and polars-bio (uv pip install). Cloud I/O uses standard AWS/GCS/Azure SDK env vars when…
Path: skills/polars-bio/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 32,619
Language: Python
Read our review of the source →

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

From the SKILL.md

# polars-bio ## Overview polars-bio is a high-performance Python library for genomic interval operations and bioinformatics file I/O, built on Polars, Apache Arrow, and Apache DataFusion. It provides a familiar DataFrame-centric API for interval arithmetic (overlap, nearest, merge, coverage, complement, subtract) and reading/writing common bioinformatics formats (BED, VCF, BAM, CRAM, GFF/GTF, FASTA, FASTQ). Key value propositions: - **6-38x faster** than bioframe on real-world genomic benchmarks - **Streaming/out-of-core** support for large genomes via DataFusion - **Cloud-native** file I/O (S3, GCS, Azure) with predicate pushdown - **Two API styles**: functional (`pb.overlap(df1, df2)`) and method-chaining (`df1.lazy().pb.overlap(df2)`) - **SQL interface** for genomic data via DataFusion SQL engine ## When to Use This Skill Use this skill when: - Performing genomic interval operations (overlap, nearest, merge, coverage, complement, subtract) - Reading/writing bioinformatics file formats (BED, VCF, BAM, CRAM, GFF/GTF, FASTA, FASTQ) - Processing large genomic datasets that don't fit in memory (streaming mode) - Running SQL queries on genomic data files - Migrating from bioframe to a

What's inside
Steps it walks through
  1. Overview
  2. When to Use This Skill
  3. Quick Start
  4. Installation
  5. Basic Overlap Example
  6. Reading a BED File
  7. Core Capabilities
  8. 1. Genomic Interval Operations
  9. 2. Bioinformatics File I/O
  10. 3. SQL Data Processing
  11. 4. Pileup Operations
  12. Key Concepts
  13. Coordinate Systems
  14. Two API Styles
Ships with 6 files
  • references/bioframe_migration.md
  • references/configuration.md
  • references/file_io.md
  • references/interval_operations.md
  • references/pileup_operations.md
  • references/sql_processing.md
Commands it runs
uv pip install "polars-bio==0.31.0"
uv pip install "polars-bio[pandas]==0.31.0"
More from scientific-agent-skills
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About this skill
What does the polars-bio skill do?

High-performance genomic interval operations and bioinformatics file I/O on Polars DataFrames. Overlap, nearest, merge, coverage, complement, subtract for BED/VCF/BAM/GFF intervals. Streaming, cloud-native, faster bioframe alternative.

How do I install it?

Run `npx skills add K-Dense-AI/scientific-agent-skills --skill polars-bio --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 K-Dense-AI/scientific-agent-skills, a repository with 32,619 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.

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