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pubmed-database

直接使用 PubMed 和 NCBI E-utilities 搜尋生物醫學文獻、MeSH 查詢、PMID 查詢、引用檢索及 API 支援的文獻監控。當任務需要來自 PubMed 的生物醫學文獻而非一般網路搜尋時使用。

pubmed-database 是什麼?

此技能專為需要直接存取 PubMed 與 NCBI E-utilities API 的生物醫學文獻搜尋任務而設計。其主要功能包括使用 MeSH 詞彙、欄位標籤、布林運算子與日期範圍建立精確的 PubMed 查詢,以及透過 PMID 查找摘要與引用資料。技能支援系統性文獻回顧的可重複搜尋策略,並提供完整的 E-utilities 工作流程(esearch、esummary、efetch、elink),確保可程式化且可重現的操作。輸出紀律方面,它要求記錄每次搜尋的詳細資訊,包括查詢字串、資料庫、日期與篩選器,以便稽核與重現。該技能可應用於 Python、Shell 或其他 HTTP 用戶端,並建議使用 API 金鑰與電子郵件以符合生產環境的最佳實踐。

相容平台~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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說明文件

PubMed 資料庫

當任務需要來自 PubMed 的生物醫學文獻而非一般網路搜尋時,請使用此技能。

使用時機

  • 搜尋 MEDLINE 或生命科學文獻。
  • 使用 MeSH 詞彙、欄位標籤、日期或文章類型建立 PubMed 查詢。
  • 查詢 PMID、摘要、出版品元資料或相關引用。
  • 執行需要可重複搜尋字串的系統性文獻回顧搜尋。
  • 直接從 Python、Shell 或其他 HTTP 用戶端使用 NCBI E-utilities。

查詢建構

從研究問題開始,將其拆解為概念,然後使用布林運算子組合這些概念。

概念_1 AND 概念_2 AND 篩選器
同義詞_a OR 同義詞_b
NOT 排除詞

有用的 PubMed 欄位標籤:

  • [ti]:標題
  • [ab]:摘要
  • [tiab]:標題或摘要
  • [au]:作者
  • [ta]:期刊名稱縮寫
  • [mh]:MeSH 詞彙
  • [majr]:主要 MeSH 主題
  • [pt]:出版類型
  • [dp]:出版日期
  • [la]:語言

範例:

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 與副標題

當概念有穩定的控制詞彙時,優先使用 MeSH。當主題較新或詞彙不一致時,將 MeSH 與標題/摘要詞彙結合使用。

正確的副標題語法將副標題置於欄位標籤之前:

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

僅在主題必須是論文核心時使用 [majr]。這能提高精確度,但可能會遺漏相關文獻。

篩選器

出版類型:

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

日期篩選器:

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

可用性篩選器:

free full text[sb]
hasabstract[text]

E-utilities 工作流程

NCBI E-utilities 支援可重複的 API 工作流程:

  1. esearch.fcgi:搜尋並回傳 PMID。
  2. esummary.fcgi:回傳輕量級文章元資料。
  3. efetch.fcgi:以 XML、MEDLINE 或純文字格式擷取摘要或完整記錄。
  4. elink.fcgi:尋找相關文章與連結資源。

在生產腳本中使用電子郵件與 API 金鑰。將 API 金鑰儲存在環境變數中,切勿儲存在已提交的檔案或命令歷史中。

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)

對於大量資料,建議使用 NCBI 歷史伺服器參數(usehistory=y、WebEnv、query_key),而不是透過 URL 傳遞非常長的 PMID 列表。

輸出紀律

每次搜尋時,請記錄:

  • 確切的搜尋字串
  • 搜尋的資料庫
  • 搜尋日期
  • 使用的篩選器
  • 結果數量
  • 匯出格式
  • 任何手動排除項目

範例:

| 資料庫 | 搜尋日期 | 查詢 | 篩選器 | 結果 |
| --- | --- | --- | --- | ---: |
| PubMed | 2026-05-11 | `sickle cell disease[mh] AND CRISPR[tiab]` | 2020:2026[dp], English | 42 |

審查核對清單

  • 欄位標籤是否為有效的 PubMed 標籤?
  • MeSH 詞彙是否已搭配新興主題的自由文字同義詞?
  • 日期範圍是否明確且適當?
  • 搜尋日誌是否包含足夠的詳細資訊以重現查詢?
  • API 金鑰是否從環境變數載入?
  • HTTP 程式碼是否在解析前呼叫 raise_for_status() 或以其他方式處理非 200 的回應?
  • 是否遵守速率限制?

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/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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