affaan-m/gget

gget CLIとPythonワークフロー: 迅速なゲノムデータベースクエリ、配列検索、BLASTスタイル検索、エンリッチメントチェック、再現可能なバイオインフォマティクス証拠ログを実行します。

gget とは?

このスキルは、gget CLIおよびPythonライブラリを使用して、迅速なゲノムデータベースクエリ、配列ルックアップ、BLASTスタイル検索、エンリッチメント解析、および再現可能なバイオインフォマティクス証拠ログを実行するためのワークフローを提供します。主にClaude Code、Cursor、CodexなどのAIエージェントプラットフォームで動作するように設計されており、コマンドラインインターフェースとPythonモジュールの両方をサポートしています。ggetを活用することで、研究者や開発者は、遺伝子アノテーション、相同性検索、遺伝子オントロジーエンリッチメント解析などの複雑なバイオインフォマティクスタスクを、スクリプトや会話型AIエージェントを通じて効率的に自動化できます。このワークフローは、科学的再現性を確保するために、クエリと結果を詳細なログに記録し、解析パイプラインの透明性と追跡可能性を向上させます。

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

Installed? Explore more 生産性&コラボレーション skills: steipete/gemini, steipete/gh-issues, steipete/skill-creator · View all 6 →

お気に入りのAIに質問する

このエージェントスキルを事前に読み込んだ状態で新しいチャットを開きます。

ドキュメント

gget は何をしますか?

gget CLIとPythonワークフロー: 迅速なゲノムデータベースクエリ、配列検索、BLASTスタイル検索、エンリッチメントチェック、再現可能なバイオインフォマティクス証拠ログを実行します。

はじめに

ggetは、公開ゲノムデータベースへの簡単なアクセスを可能にするコマンドラインツールおよびPythonライブラリです。このスキルは、AIエージェントがggetワークフローを使用して次のことを行うためのガイドを提供します:

  • 遺伝子アノテーションの検索
  • 配列のルックアップ
  • BLAST相同性検索
  • 遺伝子オントロジー(GO)エンリッチメント解析
  • すべての操作の再現可能なログの生成

対応プラットフォーム

  • Claude Code
  • Cursor
  • Codex
  • コマンドラインインターフェース
  • Python 3.7+

インストール

pip install gget

基本的な使用方法

遺伝子アノテーションの検索

gget search "BRCA1" -s human

配列のルックアップ

gget seq ENSG00000012048

BLAST検索

gget blast --sequence ATGC... --db refseq_rna

エンリッチメント解析

gget enrichment --genes BRCA1,TP53,EGFR --db GO_Biological_Process_2021

Pythonでのワークフロー例

import gget

# 遺伝子アノテーションの検索
annotation = gget.search("TP53", species="human")
print(annotation)

# 配列のルックアップ
sequence = gget.seq("ENSG00000141510")

# エンリッチメント解析
enrichment = gget.enrichment(["TP53", "BRCA1", "EGFR"], databases=["GO_Biological_Process_2021"])

ログ

ggetは、すべてのクエリと結果を詳細な証拠ログに記録し、研究の再現性を確保します。ログはデフォルトでgget_log.txtに保存されます。

参考リンク

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.

関連スキル