gget 是做什么的？
gget CLI 和 Python 工作流，用于快速基因组数据库查询、序列查找、BLAST 样搜索、富集检查以及可重复的生物信息学证据日志。
gget CLI 和 Python 工作流，用于快速基因组数据库查询、序列查找、BLAST 样搜索、富集检查以及可重复的生物信息学证据日志。
该技能提供对 gget 的简化访问，gget 是一个命令行界面 (CLI) 和 Python 库，专为快速、可重复的生物信息学查询而设计。它使 AI 代理能够直接执行常见基因组操作，例如从 Ensembl 获取序列通过基因 ID、使用标准数据库运行 BLAST 样搜索以及执行基因本体 (GO) 富集分析。该技能的主要优势在于其能够无缝生成“glog”——一种结构化的 JSON 日志，以机器可读的格式记录每个查询、结果和参数，确保完全的可重复性。该技能支持多种平台，包括用户本地环境、GitHub Codespaces 和 Google Colab，使其适合被整合到基于终端的 AI 代理（如 Claude Code 或 Cursor）中。通过抽象底层的 API 调用和数据处理，它让 AI 代理无需人工干预即可执行复杂的生物信息学任务，从而简化了研究验证和自动化流程。
npx skills add https://github.com/affaan-m/everything-claude-code/tree/main/skills/ggetInstalled? Explore more 生产力与协作 skills: steipete/gemini, steipete/gh-issues, steipete/skill-creator · View all 6 →
gget CLI 和 Python 工作流，用于快速基因组数据库查询、序列查找、BLAST 样搜索、富集检查以及可重复的生物信息学证据日志。
This repo contains 20 individual skills — each has its own dedicated page.
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.
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.
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.
Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.
Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.
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.
Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.
Production-ready UI motion system for React/Next.js. Use when implementing animations, transitions, or motion patterns.
Example project-specific skill template based on a real production application.
Direct PubMed and NCBI E-utilities search workflows for biomedical literature, MeSH queries, PMID lookup, citation retrieval, and API-backed literature monitoring.
Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.
USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.
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.
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.
Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent
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.
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.
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.
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.
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.
Gemini CLI one-shot prompts, summaries, generation, skills, hooks, MCP, or Gemma routing.
Fetch GitHub issues, select candidates, spawn background fix agents, open PRs, and optionally process PR review comments.
Create, edit, audit, tidy, validate, or restructure AgentSkills and SKILL.md files.
Search, install, update, sync, or publish agent skills with the ClawHub CLI and registry.
Delegate coding work to Codex, Claude Code, or OpenCode as background workers; not simple edits or read-only code lookup.
List, configure, authenticate, call, and inspect MCP servers/tools with mcporter over HTTP or stdio.