bio-applied-bio-data-formats

bio-applied-bio-data-formats is a skill for Claude Code, Codex from Pavel-Kravchenko/Bioinformatics. It costs 67 tokens per session (3,012 once invoked), scanned A, original, no licence file.

A Python toolkit for reading and writing common genomics file formats, which store DNA sequences, sequencing reads, alignments, variants, and genomic regions. It supports FASTA, FASTQ, SAM/BAM, VCF, BED, and GFF/GTF files.

In plain words
What is it for?
Use it to build custom genomics format parsers, convert or inspect sequencing files, decode alignment fields, and debug off-by-one coordinate errors.
Why use it?
It helps avoid parser work and catches coordinate mistakes, especially the difference between zero-based and one-based positions. It can also explain alignment details such as SAM flags and CIGAR strings.

Skill for Claude CodeCodex

Which agent this was written for is unclear — body not stored (licence); the path alone says nothing.

Good fit Use it to build custom genomics format parsers, convert or inspect sequencing files, decode alignment fields, and debug off-by-one coordinate errors.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/pavel-kravchenko/bioinformatics/bio-applied-bio-data-formats
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add Pavel-Kravchenko/Bioinformatics --skill bio-applied-bio-data-formats
Clone the repo
git clone --depth 1 https://github.com/Pavel-Kravchenko/Bioinformatics

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for bio-applied-bio-data-formats

README.md
[![agentmods](https://agentmods.dev/badge/skills/pavel-kravchenko/bioinformatics/bio-applied-bio-data-formats/github.svg)](https://agentmods.dev/skills/pavel-kravchenko/bioinformatics/bio-applied-bio-data-formats)
Your own site
<a href="https://agentmods.dev/skills/pavel-kravchenko/bioinformatics/bio-applied-bio-data-formats"><img src="https://agentmods.dev/badge/skills/pavel-kravchenko/bioinformatics/bio-applied-bio-data-formats/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for bio-applied-bio-data-formats

Your own site · 80×15
<a href="https://agentmods.dev/skills/pavel-kravchenko/bioinformatics/bio-applied-bio-data-formats"><img src="https://agentmods.dev/badge/skills/pavel-kravchenko/bioinformatics/bio-applied-bio-data-formats.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 67 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,012 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe.
Origin unknown No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00067 $0.03012
Opus 5 $0.00034 $0.01506
Sonnet 5 $0.00013 $0.00602
Haiku 4.5 $0.00007 $0.00301

Measured 11d ago against content hash 3eb13bcd8be9, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

bio-applied-bio-data-formats scanned grade A with 1 finding 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 11d 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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

In practice, prefer `pysam.FastaFile(path).fetch(chrom, start, end)` — this hand-rolled version exists to show what the index actually encodes.
Skills/bio-applied-bio-data-formats/SKILL.md · 257 lines

The source is not reproduced here

No licence file

A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.

Read it on GitHub

Changes

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.

  1. 11d ago First seen · 257 lines · 67 tokens per session scan A 3eb13bcd8be9

Subscribe to this mod's changes

bio-applied-bio-data-formats is a skill published in the GitHub repository Pavel-Kravchenko/Bioinformatics (5 stars, last pushed 2mo ago), with no licence file. It adds 67 tokens to every session and 3,012 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Related

Other skills, from other repositories

astropy

Core Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.

K-Dense-AI/scientific-agent-skills · 56 tokens

cobrapy

Constraint-based metabolic modeling (COBRA). FBA, FVA, gene knockouts, flux sampling, SBML models, for systems biology and metabolic engineering analysis.

K-Dense-AI/scientific-agent-skills · 38 tokens

matlab

Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.

K-Dense-AI/scientific-agent-skills · 42 tokens

bioservices

Unified Python interface to 40+ bioinformatics services. Use when querying multiple databases (UniProt, KEGG, ChEMBL, Reactome) in a single workflow with consistent API. Best for cross-database analysis, ID mapping across services. For quick single-database lookups use gget; for sequence/file manipulation use…

K-Dense-AI/scientific-agent-skills · 73 tokens

pennylane

Hardware-agnostic quantum ML framework with automatic differentiation. Use when training quantum circuits via gradients, building hybrid quantum-classical models, or needing device portability across IBM/Google/Rigetti/IonQ. Best for variational algorithms (VQE, QAOA), quantum neural networks, and integration with…

K-Dense-AI/scientific-agent-skills · 98 tokens

biopython

Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use…

synthetic-sciences/openscience · 76 tokens