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pubmed-database

直接使用PubMed和NCBI E-utilities搜索工作流,用于生物医学文献检索、MeSH查询、PMID查找、引文获取和基于API的文献监控。当任务需要来自PubMed的生物医学文献而非通用网络搜索时使用。

pubmed-database 是什么?

该技能专门用于执行PubMed和NCBI E-utilities的搜索工作流,适用于需要从生物医学文献数据库(如MEDLINE)中获取信息的场景。它支持构建包含MeSH术语、字段标签、日期和文章类型的高级查询,并能直接查找PMID、获取摘要和元数据。技能提供完整的Python和Shell示例,指导如何使用NCBI E-utilities API进行可重复的检索、获取相关引文和链接资源。它还强调使用环境变量管理API密钥、遵守NCBI使用策略和速率限制。主要支持的平台包括Claude Code、Cursor等基于技能的AI代理平台。该技能显著提升了生物医学文献检索的效率和准确性,特别适合系统综述、文献综述和循证医学研究。

兼容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/scientific-db-pubmed-database

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打开一个已预加载此 Agent Skill 的新对话。

文档

PubMed 数据库

当任务需要来自PubMed的生物医学文献而非通用网络搜索时,请使用此技能。

何时使用

  • 搜索MEDLINE或生命科学文献。
  • 使用MeSH术语、字段标签、日期或文章类型构建PubMed查询。
  • 查找PMID、摘要、出版物元数据或相关引文。
  • 运行需要可重复搜索字符串的系统综述搜索轮次。
  • 直接通过Python、Shell或其他HTTP客户端使用NCBI E-utilities。

查询构建

从研究问题开始,将其拆分为概念,然后使用布尔运算符组合概念。

concept_1 AND concept_2 AND filter
synonym_a OR synonym_b
NOT exclusion_term

有用的PubMed字段标签:

  • [ti]:标题
  • [ab]:摘要
  • [tiab]:标题或摘要
  • [au]:作者
  • [ta]:期刊标题缩写
  • [mh]:MeSH术语
  • [majr]:主要MeSH主题
  • [pt]:出版物类型
  • [dp]:发布日期
  • [la]:语言

示例:

diabetes mellitus[mh] AND treatment[tiab] AND systematic review[pt] AND 2023:2026[dp]
(metformin[nm] OR insulin[nm]) AND diabetes mellitus, type 2[mh] AND randomized controlled trial[pt]
smith ja[au] AND cancer[tiab] AND 2026[dp] AND english[la]

MeSH和副标题

当概念具有稳定的受控词汇术语时,优先使用MeSH。当主题较新或术语多样时,将MeSH与标题/摘要术语结合使用。

正确的副标题语法是将副标题放在字段标签之前:

diabetes mellitus, type 2/drug therapy[mh]
cardiovascular diseases/prevention & control[mh]

仅当主题必须是论文核心内容时使用[majr]。它可以提高精确度,但可能遗漏相关文献。

筛选条件

出版物类型:

  • clinical trial[pt]
  • meta-analysis[pt]
  • randomized controlled trial[pt]
  • review[pt]
  • systematic review[pt]
  • guideline[pt]

日期筛选:

2026[dp]
2020:2026[dp]
2026/03/15[dp]

可用性筛选:

free full text[sb]
hasabstract[text]

E-utilities工作流

NCBI E-utilities支持可重复的API工作流:

  1. esearch.fcgi:搜索并返回PMID。
  2. esummary.fcgi:返回轻量级文章元数据。
  3. efetch.fcgi:以XML、MEDLINE或文本格式获取摘要或完整记录。
  4. elink.fcgi:查找相关文章和链接资源。

在生产脚本中使用电子邮件和API密钥。将API密钥存储在环境变量中,切勿放在提交的文件或命令历史中。

import os
import time
import requests

BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils"


def esearch(query: str, retmax: int = 20) -> list[str]:
    params = {
        "db": "pubmed",
        "term": query,
        "retmode": "json",
        "retmax": retmax,
        "tool": "ecc-pubmed-search",
        "email": os.environ.get("NCBI_EMAIL", ""),
    }
    api_key = os.environ.get("NCBI_API_KEY")
    if api_key:
        params["api_key"] = api_key

    response = requests.get(f"{BASE}/esearch.fcgi", params=params, timeout=30)
    response.raise_for_status()
    time.sleep(0.35)
    return response.json()["esearchresult"]["idlist"]


pmids = esearch("hypertension[mh] AND randomized controlled trial[pt] AND 2024:2026[dp]")
print(pmids)

对于批处理,优先使用NCBI历史服务器参数(usehistory=y、WebEnv、query_key),而不是通过URL传递非常长的PMID列表。

输出规范

对于每个搜索轮次,记录:

  • 确切的搜索字符串
  • 搜索的数据库
  • 搜索日期
  • 使用的筛选条件
  • 结果数量
  • 导出格式
  • 任何手动排除项

示例:

| 数据库 | 搜索日期 | 查询 | 筛选条件 | 结果数 |
| --- | --- | --- | --- | ---: |
| PubMed | 2026-05-11 | `sickle cell disease[mh] AND CRISPR[tiab]` | 2020:2026[dp], English | 42 |

审查清单

  • 字段标签是否为有效的PubMed标签?
  • 对于较新的主题,MeSH术语是否与自由文本同义词配对?
  • 日期范围是否明确且适当?
  • 搜索日志是否包含足够的细节以便重复查询?
  • API密钥是否从环境变量加载?
  • HTTP代码是否调用了raise_for_status()或以其他方式处理了非200响应后再解析?
  • 是否遵守了速率限制?

参考文献

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/content-engine

Create platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across platforms.

affaan-m/ECC

Multi-platform content distribution across X, LinkedIn, Threads, and Bluesky. Adapts content per platform using content-engine patterns. Never posts identical content cross-platform. Use when the user wants to distribute content across social platforms.

affaan-m/fal-ai-media

Unified media generation via fal.ai MCP — image, video, and audio. Covers text-to-image (Nano Banana), text/image-to-video (Seedance, Kling, Veo 3), text-to-speech (CSM-1B), and video-to-audio (ThinkSound). Use when the user wants to generate images, videos, or audio with AI.

affaan-m/manim-video

日本語翻訳:このファイルは manim-video 用の日本語翻訳が必要です

affaan-m/remotion-video-creation

Remotion のベストプラクティス - React で動画を作成する。3D、アニメーション、音声、字幕、チャート、トランジションなどをカバーするドメイン固有の29のルール。

affaan-m/video-editing

AI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.

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.

agent-sort

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

ai-first-engineering

Engineering operating model for teams where AI agents generate a large share of implementation output. Use when setting team process, review gates, or ownership rules for a codebase largely written by agents.

ai-regression-testing

Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code. Use when adding regression coverage to AI-assisted code, or when the same model both wrote and reviewed a change.

android-clean-architecture

Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns. Use when structuring modules, layers, or data flow in an Android or KMP project.

angular-developer

Generates Angular code and provides architectural guidance. Trigger when creating projects, components, or services, or for best practices on reactivity (signals, linkedSignal, resource), forms, dependency injection, routing, SSR, accessibility (ARIA), animations, styling (component styles, Tailwind CSS), testing, or CLI tooling.

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