affaan-m/gget

Flujo de trabajo en CLI y Python para consultas rápidas en bases de datos genómicas, búsqueda de secuencias, búsquedas tipo BLAST, verificaciones de enriquecimiento y registros de evidencia bioinformática reproducibles.

¿Qué es gget?

El skill 'affaan-m/gget' proporciona un flujo de trabajo integrado para realizar consultas genómicas rápidas directamente desde la línea de comandos o scripts en Python. Es útil para investigadores y desarrolladores bioinformáticos que necesitan buscar secuencias, realizar búsquedas tipo BLAST, verificar enriquecimiento funcional y mantener un registro de evidencia reproducible. Soporta plataformas como Claude Code y se integra con herramientas de terminal como Cursor o Codex, permitiendo automatizar consultas en bases de datos como NCBI, Ensembly UniProt. Su funcionamiento se basa en el módulo Python 'gget' de L. Luebbert, ofreciendo funciones como 'blast', 'enrichr', 'search', 'find' y otras, generando salidas en formatos de tabla o JSON que pueden ser documentadas en archivos markdown para trazabilidad.

Compatible con~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/everything-claude-code/tree/main/skills/gget

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Documentación

¿Qué hace gget?

Flujo de trabajo en CLI y Python para consultas rápidas en bases de datos genómicas, búsqueda de secuencias, búsquedas tipo BLAST, verificaciones de enriquecimiento y registros de evidencia bioinformática reproducibles.

Uso

CLI

gget tool [opciones] [argumento]

Python

import gget

gget.tool(argumento, opciones)

Herramientas disponibles

HerramientaDescripción
blastRealiza búsquedas BLAST de una secuencia o acceso contra bases de datos de nucleótidos o proteínas.
enrichrRealiza análisis de enriquecimiento de conjuntos de genes.
searchBusca en bases de datos genómicas (NCBI, Ensembl, UniProt).
findEncuentra genes por nombre o descripción.
muscleAlinea secuencias músculo.
seqObtiene secuencias a partir de accesos.
infoObtiene información sobre un gen o proteína.
genesObtiene genes asociados con una enfermedad.
convertConvierte identificadores de genes entre bases de datos.

Ejemplos

CLI

# Buscar secuencia usando BLAST
gget blast "ATGCTAGCTAGCTA"

# Buscar enriquecimiento de genes
gget enrichr --genes CASP8 TP53 BRCA1

# Buscar un gen en NCBI
gget search -db ncbi -q TP53

Python

import gget

# Búsqueda BLAST
resultado = gget.blast("ATGCTAGCTAGCTA")
print(resultado)

# Enriquecimiento
resultado = gget.enrichr(["CASP8", "TP53", "BRCA1"], database="KEGG_2021_Human")
print(resultado)

# Búsqueda
resultado = gget.search("TP53", db="ncbi")
print(resultado)

Variables de entorno

No se requieren variables de entorno adicionales. gget usa las configuraciones predeterminadas del sistema. Opcionalmente, se pueden configurar para entornos de red:

export GGET_CACHE_DIR=/ruta/a/cache
export GGET_TIMEOUT=30

Registro de evidencia

Para un flujo reproducible, se recomienda redirigir la salida a un archivo markdown:

gget blast "ATGCTAGCTAGCTA" >> evidencia_blast.md

Plataformas compatibles

Este skill está diseñado para funcionar con:

  • Claude Code
  • Cursor AI
  • Codex CLI

Instalación

pip install gget

O clonar e instalar desde GitHub:

git clone https://github.com/affaan-m/gget.git
cd gget
pip install -r requirements.txt

Licencia

Este proyecto está licenciado bajo la licencia MIT. Ver el archivo LICENSE para más detalles.

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/claude-api

Anthropic Claude API patterns for Python and TypeScript. Covers Messages API, streaming, tool use, vision, extended thinking, batches, prompt caching, and Claude Agent SDK. Use when building applications with the Claude API or Anthropic SDKs.

affaan-m/everything-claude-code

End-to-end marketing campaign planning and execution. Covers audience research, positioning, campaign angle definition, landing page copy, email sequences, social posts, ad copy, short-form video scripts, and content calendars. Use as the orchestration layer for multi-channel product launches. Use when planning or executing a multi-channel product launch, or producing landing page, email, social, or ad copy.

affaan-m/everything-claude-code

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/everything-claude-code-conventions

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/frontend-design

Create distinctive, production-grade frontend interfaces with high design quality. Use when the user asks to build web components, pages, or applications and the visual direction matters as much as the code quality.

affaan-m/literature-review

Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.

affaan-m/motion-ui

Production-ready UI motion system for React/Next.js. Use when implementing animations, transitions, or motion patterns.

affaan-m/project-guidelines-example

Example project-specific skill template based on a real production application.

affaan-m/pubmed-database

Direct PubMed and NCBI E-utilities search workflows for biomedical literature, MeSH queries, PMID lookup, citation retrieval, and API-backed literature monitoring.

affaan-m/scholar-evaluation

Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.

affaan-m/uspto-database

USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

agent-self-evaluation

Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

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