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⏱ Pasting from scattered docs→Backend — one installTrial and error, again→#authentication ready to use

Gget

CLI/Python toolkit for rapid bioinformatics queries. Preferred for quick BLAST searches. Access to 20+ databases: gene info (Ensembl/UniProt), AlphaFold, ARCHS4, Enrichr, OpenTargets, COSMIC, genome downloads. For advanced BLAST/batch processing, use biopython. For multi-database integration, use bioservices.

⚙️ Backend✦Premium script✦7 languages

WHAT YOU BECOME

Use Cases

Perfect for these scenarios

✦ 01🛠️

Create New Skills

Build Claude Code skills from scratch with proper structure and triggers

✦ 02🔧

Debug Skill Activation

Troubleshoot why skills aren't triggering or behaving unexpectedly

✦ 03📝

Modify skill-rules.json

Update skill configuration files with correct syntax and best practices

✦ 04🎭

Implement Progressive Disclosure

Design skills that reveal features gradually based on user context

MEASURED GAIN

Why Choose This Skill

Proven benefits and measurable impact

5x

Faster Skill Development

Reduce skill creation time with structured templates and validation

90%

Fewer Activation Errors

Eliminate common mistakes in trigger patterns and hook implementations

3x

Improved Debugging Speed

Quickly identify and fix skill configuration issues with guided analysis

WHAT YOU GET

What’s Inside

Files, tags and the three-step install

Download or copy the skill file from the source repository.
Put the skill file into Claude's skills directory — ~/.claude/skills/
Restart Claude or run the reload command, and the skill loads automatically.
$ cp K-Dense-AI__claude-scientific-skills/scientific-skills/gget/Skill.md ~/.claude/skills/

Tip: Read the documentation and the code before first use, so you know what it does and which permissions it needs.

Related tags

#authentication#data-analysis#database#documentation#git#python

Technical information

Author
K-Dense-AI
Category
Backend
File size
24.43 KB
Source repository
K-Dense-AI__claude-scientific-skills
License
MIT
Metadata
Includes YAML metadata
View GitHub source

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This skill should be used when working with annotated data matrices in Python, particularly for single-cell genomics analysis, managing experimental measurements with metadata, or handling large-scale biological datasets. Use when tasks involve AnnData objects, h5ad files, single-cell RNA-seq data, or integration with scanpy/scverse tools.

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Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.

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