megahit-assembler

MEGAHIT metagenomic assembly skill for reconstructing genomes from short reads

509 stars

bya5c-ai

View on GitHub Installation ↓

Best use case

megahit-assembler is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

MEGAHIT metagenomic assembly skill for reconstructing genomes from short reads

Teams using megahit-assembler should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

You only need a quick one-off answer and do not need a reusable workflow.
You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/megahit-assembler/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/domains/science/bioinformatics/skills/megahit-assembler/SKILL.md"

Manual Installation

Download SKILL.md from GitHub
Place it in .claude/skills/megahit-assembler/SKILL.md inside your project
Restart your AI agent — it will auto-discover the skill

How megahit-assembler Compares

Feature / Agent	megahit-assembler	Standard Approach
Platform Support	Not specified	Limited / Varies
Context Awareness	High	Baseline
Installation Complexity	Unknown	N/A

Frequently Asked Questions

What does this skill do?

MEGAHIT metagenomic assembly skill for reconstructing genomes from short reads

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# MEGAHIT Assembler Skill

## Purpose
Enable MEGAHIT metagenomic assembly for reconstructing genomes from short reads.

## Capabilities
- Memory-efficient assembly
- Multiple k-mer strategies
- Contig quality assessment
- Large dataset handling
- Iterative assembly refinement
- Assembly graph analysis

## Usage Guidelines
- Select k-mer strategy based on coverage
- Assess contig quality metrics
- Handle large datasets efficiently
- Consider iterative refinement
- Bin contigs for MAG recovery
- Document assembly parameters

## Dependencies
- MEGAHIT
- metaSPAdes
- IDBA-UD

## Process Integration
- Shotgun Metagenomics Pipeline (shotgun-metagenomics)

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