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Agent skill

Scientific DB Pubmed Database

by affaan-m in affaan-m/ECC

生物医学文献、MeSH クエリ、PMID 検索、引用取得、および API を利用した文献モニタリングのための PubMed および NCBI E-utilities の直接検索ワークフロー。

MITAuto-check passedResearch & Science

Install Scientific DB Pubmed Database

skills CLI
$ npx skills add affaan-m/ECC --skill scientific-db-pubmed-database -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install affaan-m/ECC scientific-db-pubmed-database --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .claude/skills && cp -r skills-src/docs/ja-JP/skills/scientific-db-pubmed-database .claude/skills/scientific-db-pubmed-database && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
scientific-db-pubmed-database
GitHub stars
276k
Token cost
~908 tokens
SKILL.md length
144 words
Files
1
Skills in repo
683
Repo updated
First seen
Licence
MIT

At a glance

生物医学文献、MeSH クエリ、PMID 検索、引用取得、および API を利用した文献モニタリングのための PubMed および NCBI E-utilities の直接検索ワークフロー。

  • Works in 4 steps: esearch.fcgi: 検索して PMID を返す。 → esummary.fcgi: 軽量な記事メタデータを返す。 → efetch.fcgi:… → …
  • Tasks that involve Academic paper search
  • SKILL.md covers 使用するタイミング, クエリの構築, MeSH とサブヘッディング and フィルター, plus 4 more sections
  • Reaches eutils.ncbi.nlm.nih.gov; needs NCBI_API_KEY

What it does

Scientific DB Pubmed Database is an agent skill from affaan-m/ECC. 生物医学文献、MeSH クエリ、PMID 検索、引用取得、および API を利用した文献モニタリングのための PubMed および NCBI E-utilities の直接検索ワークフロー。

Its SKILL.md is about 910 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Research & Science, covering Academic paper search. It works with PubMed and NCBI. The repository describes itself as: The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond. The licence is MIT.

When your agent uses it

  • Tasks that involve Academic paper search

Example prompts

  • “/scientific-db-pubmed-database”

Requirements

  • Python 3
  • A credential in NCBI_API_KEY

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. esearch.fcgi: 検索して PMID を返す。
  2. esummary.fcgi: 軽量な記事メタデータを返す。
  3. efetch.fcgi: XML、MEDLINE、またはテキストでアブストラクトまたはフルレコードを取得。
  4. elink.fcgi: 関連記事とリンクされたリソースを検索。

What it can do on your machine

Read from SKILL.md and the folder at commit 4eb71d9. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python and markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • eutils.ncbi.nlm.nih.gov

    Also links to:

    • pubmed.ncbi.nlm.nih.gov
    • ncbi.nlm.nih.gov
    • support.nlm.nih.gov

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • NCBI_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Scientific DB Pubmed Database loads about 908 tokens when it runs. Until then it costs about 31 tokens; SKILL.md has 144 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~31
When it runs · the whole SKILL.md, loaded when a task matches
~908

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from affaan-m/ECC at commit 4eb71d9, republished under its MIT licence (© affaan-m). 144 words, ~908 tokens.

Download SKILL.mdSave it as .claude/skills/scientific-db-pubmed-database/SKILL.md (or your agent's skills folder).
name
scientific-db-pubmed-database
description
生物医学文献、MeSH クエリ、PMID 検索、引用取得、および API を利用した文献モニタリングのための PubMed および NCBI E-utilities の直接検索ワークフロー。
origin
community

PubMed Database

一般的なウェブ検索ではなく PubMed から生物医学文献が必要なタスクにこのスキルを使用します。

使用するタイミング

  • MEDLINE または生命科学文献の検索。
  • MeSH 用語、フィールドタグ、日付、または文献種別を使った PubMed クエリの構築。
  • PMID、アブストラクト、出版メタデータ、または関連引用の検索。
  • 再現可能な検索文字列が必要なシステマティックレビューの検索パスの実行。
  • Python、シェル、または別の HTTP クライアントから直接 NCBI E-utilities を使用。

クエリの構築

研究質問から始め、概念に分割し、ブール演算子で概念を組み合わせます。

text
concept_1 AND concept_2 AND filter
synonym_a OR synonym_b
NOT exclusion_term

有用な PubMed フィールドタグ:

  • [ti]: タイトル
  • [ab]: アブストラクト
  • [tiab]: タイトルまたはアブストラクト
  • [au]: 著者
  • [ta]: 雑誌タイトル略語
  • [mh]: MeSH 用語
  • [majr]: 主要 MeSH トピック
  • [pt]: 出版種別
  • [dp]: 出版日
  • [la]: 言語

例:

text
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 とサブヘッディング

概念が安定した統制語彙用語を持つ場合は MeSH を優先します。トピックが新しいまたは用語が多様な場合は MeSH とタイトル/アブストラクト用語を組み合わせます。

正しいサブヘッディング構文では、サブヘッディングをフィールドタグの前に置きます:

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

[majr] は論文の中心的なトピックである必要がある場合にのみ使用します。精度は向上しますが、関連する研究を見逃す可能性があります。

フィルター

出版種別:

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

日付フィルター:

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

利用可能性フィルター:

text
free full text[sb]
hasabstract[text]

E-utilities ワークフロー

NCBI E-utilities は再現可能な API ワークフローをサポートします:

  1. esearch.fcgi: 検索して PMID を返す。
  2. esummary.fcgi: 軽量な記事メタデータを返す。
  3. efetch.fcgi: XML、MEDLINE、またはテキストでアブストラクトまたはフルレコードを取得。
  4. elink.fcgi: 関連記事とリンクされたリソースを検索。

本番スクリプトにはメールアドレスと API キーを使用します。API キーは環境変数に保存し、コミットされたファイルやコマンド履歴には絶対に入れないでください。

python
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)

バッチの場合、非常に長い PMID リストを URL に渡す代わりに、NCBI ヒストリーサーバーパラメーター（usehistory=y、WebEnv、query_key）を優先します。

出力の記録

各検索パスについて以下を記録します:

  • 正確な検索文字列
  • 検索したデータベース
  • 検索日
  • 使用したフィルター
  • 結果件数
  • エクスポート形式
  • 手動除外

例:

markdown
| データベース | 検索日 | クエリ | フィルター | 結果 |
| --- | --- | --- | --- | ---: |
| PubMed | 2026-05-11 | `sickle cell disease[mh] AND CRISPR[tiab]` | 2020:2026[dp], English | 42 |

レビューチェックリスト

  • フィールドタグは有効な PubMed タグか？
  • 新しいトピックについて MeSH 用語は自由テキストの同義語とペアになっているか？
  • 日付範囲は明示的で適切か？
  • 検索ログにクエリを再現するのに十分な詳細が含まれているか？
  • API キーは環境から読み込まれているか？
  • HTTP コードは解析前に raise_for_status() を呼び出しているか、または 200 以外のレスポンスを処理しているか？
  • レート制限は守られているか？

参考文献

© affaan-m, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in docs/ja-JP/skills/scientific-db-pubmed-database of affaan-m/ECC.

Open the folder on GitHubat commit 4eb71d9

Compare with similar skills

Scientific DB Pubmed Database next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Scientific DB Pubmed Database compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Scientific DB Pubmed Database this skillaffaan-m/ECC276k—~908Automated safety check: PassMIT
PubMed REST API Searchdavila7/claude-code-templates33k14 repos~3.9kAutomated safety check: PassMIT
Pubmed Databasegoogle-deepmind/science-skills3.2k2 repos~2.1kAutomated safety check: NotesApache-2.0
Ncbi Sequence Fetchgoogle-deepmind/science-skills3.2k1 repos~2.3kAutomated safety check: NotesApache-2.0
Journal Skillsaipoch/medical-research-skills1.9k—~1.7kAutomated safety check: PassMIT
Pubmed Databasejaechang-hits/SciAgent-Skills3741 repos~4.4kAutomated safety check: PassCC-BY-4.0

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Works with

Categories

Questions about Scientific DB Pubmed Database

What does Scientific DB Pubmed Database do?

生物医学文献、MeSH クエリ、PMID 検索、引用取得、および API を利用した文献モニタリングのための PubMed および NCBI E-utilities の直接検索ワークフロー。. Scientific DB Pubmed Database is an agent skill from affaan-m/ECC.

When should I use Scientific DB Pubmed Database?

Scientific DB Pubmed Database fits situations like: tasks that involve Academic paper search.

How do I install Scientific DB Pubmed Database in Claude Code?

Run `npx skills add affaan-m/ECC --skill scientific-db-pubmed-database -a claude-code`. Or copy the skill folder (docs/ja-JP/skills/scientific-db-pubmed-database in affaan-m/ECC) into .claude/skills/scientific-db-pubmed-database in your project. Claude Code loads it when a task matches its description.

How do I install Scientific DB Pubmed Database in Codex?

Run `npx skills add affaan-m/ECC --skill scientific-db-pubmed-database -a codex`. Or copy the skill folder (docs/ja-JP/skills/scientific-db-pubmed-database in affaan-m/ECC) into .agents/skills/scientific-db-pubmed-database in your project. Codex loads it when a task matches its description.

Can I use Scientific DB Pubmed Database in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add affaan-m/ECC --skill scientific-db-pubmed-database -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/scientific-db-pubmed-database, .gemini/skills/scientific-db-pubmed-database, .github/skills/scientific-db-pubmed-database and .opencode/skills/scientific-db-pubmed-database in your project.

What does Scientific DB Pubmed Database need to run?

Going by SKILL.md and its folder, Scientific DB Pubmed Database needs credentials named NCBI_API_KEY. Our summary lists: Python 3; A credential in NCBI_API_KEY.

Does Scientific DB Pubmed Database access the network?

SKILL.md names 4 domains. In commands or code: eutils.ncbi.nlm.nih.gov; the agent is likely to contact it when it follows the instructions. As links in the text: pubmed.ncbi.nlm.nih.gov, ncbi.nlm.nih.gov and support.nlm.nih.gov. This is read from the text; nothing was executed.

Is Scientific DB Pubmed Database safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Scientific DB Pubmed Database use?

Scientific DB Pubmed Database is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Scientific DB Pubmed Database use?

About 908 tokens (SKILL.md is roughly 3.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Scientific DB Pubmed Database?

Skills that share tags, products or a category with Scientific DB Pubmed Database: PubMed REST API Search (davila7/claude-code-templates, 33k stars), Pubmed Database (google-deepmind/science-skills, 3.2k stars), Ncbi Sequence Fetch (google-deepmind/science-skills, 3.2k stars) and Journal Skills (aipoch/medical-research-skills, 1.9k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Scientific DB Pubmed Database?

affaan-m (a GitHub user) maintains it in affaan-m/ECC, which has 276,111 GitHub stars. The repository holds 683 skills in this directory. The repository was last updated on October 10, 2026.

Source: affaan-m/ECC on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.