Communitygithub.com

gget

gget CLI 與 Python 工作流程,提供快速基因組資料庫查詢、序列比對、BLAST 搜尋、富集分析及可重現的生物資訊證據記錄。

gget 是什麼?

gget 是一個高效能的生物資訊工具,提供命令列介面(CLI)與 Python 套件,讓使用者能快速查詢各類基因組參考資料庫,無需建置完整的本地管線。此技能支援多種常用模組,包括搜尋 Ensembl ID、擷取基因與轉錄體中繼資料、取得核苷酸或胺基酸序列、執行 BLAST 或 BLAT 比對、取得參考基因組下載連結,以及進行蛋白質結構、訊號路徑、疾病關聯等專業查詢。gget 特別適合需要快速取得第一手證據的場景,例如在正式使用 Biopython、Snakemake 或 Nextflow 等大型工具前,先進行初步探索與記錄。支援平台包含任何可執行 Python 及 CLI 的環境,並強調可重現性,鼓勵使用者記錄查詢參數、資料庫版本與環境設定,以確保科學輸出的可靠性。

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

在你喜歡的 AI 中提問

開啟一個已預先載入此 Agent Skill 的新對話。

說明文件

gget 是做什麼的?

當任務需要透過 gget CLI 或 Python 套件快速查詢基因組參考資料庫時,請使用此技能。

使用時機

  • 尋找 Ensembl ID、基因中繼資料、轉錄體細節或序列。
  • 執行快速 BLAST 或 BLAT 查詢,無需建置完整的本地管線。
  • 從 Ensembl 取得參考基因組下載連結與註解。
  • 透過單一介面查詢蛋白質結構、訊號路徑、癌症、表現或疾病關聯模組。
  • 在轉向 Biopython、Snakemake、Nextflow、BLAST+ 或特定資料庫用戶端等較重型工具前,建立可重現的初步證據記錄。

當任務需要法規級別的臨床解讀、高產量生產管線,或對資料庫版本與本地索引有精細控制需求時,請改用專屬工作流程而非 gget。

安裝

請使用乾淨的 Python 環境。

python -m venv .venv
. .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install --upgrade gget
gget --help

若 uv 可用:

uv venv
. .venv/bin/activate
uv pip install gget

在依賴舊環境前,請先升級 gget 並重新檢查模組文件。gget 查詢的上游資料庫會隨時間變更。

基本模式

CLI 格式:

gget <module> [arguments] [options]

Python 格式:

import gget

result = gget.search(["BRCA1"], species="human")
print(result)

常見工作流程:

  1. 確定所需的物種、組裝版本、基因 ID 類型及資料庫。
  2. 查看目前模組文件的參數說明。
  3. 先執行小型查詢。
  4. 使用明確的檔案名稱與日期儲存輸出結果。
  5. 記錄模組名稱、版本、參數與資料庫假設。

常用模組

請使用最新的上游文件確認確切參數。以下為常見的優先選擇模組:

  • gget search:從搜尋詞尋找 Ensembl ID。
  • gget info:取得 Ensembl、UniProt 或相關 ID 的中繼資料。
  • gget seq:擷取核苷酸或胺基酸序列。
  • gget ref:取得參考基因組下載連結。
  • gget blast:執行快速 BLAST 查詢。
  • gget blat:針對支援的基因組組裝找出序列位置。
  • gget muscle:執行多重序列比對。
  • gget diamond:對參考序列執行本地序列比對。
  • gget alphafold 和 gget pdb:檢視蛋白質結構參考資料。
  • gget enrichr、gget opentargets、gget archs4、gget bgee、gget cbio 和 gget cosmic:探索富集分析、目標、表現、癌症及疾病關聯資料。

請勿假設每個模組都支援所有 Python 版本或依賴套件集合。某些選用的科學依賴套件,其版本支援範圍比核心套件更窄。

快速範例

尋找基因:

gget search -s human brca1 dna repair -o brca1-search.json

取得基因中繼資料:

gget info ENSG00000012048 -o brca1-info.json

取得序列:

gget seq ENSG00000012048 -o brca1-seq.fa

執行小型 BLAST 查詢:

gget blast "MEEPQSDPSVEPPLSQETFSDLWKLLPEN" -l 10 -o blast-results.json

Python 範例:

import gget

genes = gget.search(["BRCA1", "DNA repair"], species="human")
info = gget.info(["ENSG00000012048"])
sequence = gget.seq("ENSG00000012048")

重現性記錄

為科學輸出結果加入足夠的中繼資料,以便重現查詢。

| 日期 | gget 版本 | 模組 | 查詢 | 物種/組裝 | 輸出 | 備註 |
| --- | --- | --- | --- | --- | --- | --- |
| 2026-05-11 | `gget --version` | search | `BRCA1 DNA repair` | human | `brca1-search.json` | 執行前已檢查文件 |

同時記錄:

  • Python 版本與環境管理工具。
  • 透過 gget setup 安裝的任何選用依賴套件。
  • 查詢回傳的資料庫特定識別碼。
  • 輸出格式為 JSON、CSV、FASTA 或 DataFrame 匯出。
  • 任何因升級 gget 而解決的失敗情況。

審查清單

  • 是否已升級或驗證安裝的 gget 版本?
  • 使用參數前是否已檢查最新上游模組文件?
  • 物種或組裝版本是否明確指定?
  • 識別碼是否完全保留(包括 Ensembl/UniProt 前綴)?
  • 結果是否標註為資料庫輸出而非臨床解讀?
  • 從已儲存的命令或 Python 片段能否重現查詢?
  • 選用依賴套件是否安裝在隔離環境中?

參考資料

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/gget

gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs.

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.

相關技能