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Agent skill

Biopython Entrez

by aipoch in aipoch/medical-research-skills

Use Bio.Entrez to access NCBI databases (e.g., PubMed/GenBank) for searching, fetching summaries, and downloading records when your workflow needs to call the NCBI E-utilities API over the network.

MITAuto-check passedResearch & Science

Install Biopython Entrez

skills CLI
$ npx skills add aipoch/medical-research-skills --skill biopython-entrez -a claude-code

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

GitHub CLI
$ gh skill install aipoch/medical-research-skills biopython-entrez --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/aipoch/medical-research-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/'scientific-skills/Evidence Insight/biopython-entrez' .claude/skills/biopython-entrez && 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
biopython-entrez
GitHub stars
2k
Token cost
~1.5k tokens
SKILL.md length
416 words
Files
4 (incl. references)
Skills in repo
567
Repo updated
First seen
Licence
MIT

At a glance

Use Bio.Entrez to access NCBI databases (e.g., PubMed/GenBank) for searching, fetching summaries, and downloading records when your workflow needs to call the NCBI E-utilities API over the network.

  • Tasks that involve Bioinformatics
  • SKILL.md covers When to Use, Key Features, Dependencies and Example Usage, plus 1 more section
  • Calls python
  • Tasks that involve Academic paper search

What it does

Biopython Entrez is an agent skill from aipoch/medical-research-skills. Use Bio.Entrez to access NCBI databases (e.g., PubMed/GenBank) for searching, fetching summaries, and downloading records when your workflow needs to call the NCBI E-utilities API over the network.

Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `biopython-entrez_audit_result_v1.json`, `config/task_config.json` and `references/databases.md`).

It sits in Research & Science, covering Bioinformatics and Academic paper search. It works with NCBI, Biopython and PubMed. The repository describes itself as: Hundreds of agent skills for medical research, including protocol design, data analysis, evidence insights, and academic writing. The licence is MIT.

When your agent uses it

  • Tasks that involve Bioinformatics
  • Tasks that involve Academic paper search

Example prompts

  • “/biopython-entrez”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit 686e09d. 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

    Shell commands in SKILL.md call:

    • python

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

  • Network

    No URLs in SKILL.md.

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

  • Credentials

    Names no API keys, tokens, secrets or passwords.

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

Context cost

Biopython Entrez loads about 1.5k tokens when it runs, and up to ~4.5k if it reads all its reference files. Until then it costs about 54 tokens; SKILL.md has 416 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~54
When it runs · the whole SKILL.md, loaded when a task matches
~1.5k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~4.5k

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 aipoch/medical-research-skills at commit 686e09d, republished under its MIT licence (© aipoch). 416 words, ~1,513 tokens.

Download SKILL.mdSave it as .claude/skills/biopython-entrez/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
biopython-entrez
description
Use Bio.Entrez to access NCBI databases (e.g., PubMed/GenBank) for searching, fetching summaries, and downloading records when your workflow needs to call the NCBI E-utilities API over the network.
license
MIT
author
AIPOCH

Source: https://github.com/aipoch/medical-research-skills

When to Use

  • You need to search PubMed for articles by keyword, author, journal, or date range and then retrieve metadata or abstracts.
  • You want to download GenBank records (e.g., nucleotide/protein sequences) in batch given accession IDs or search queries.
  • You need to convert identifiers or discover related records across NCBI databases (e.g., PubMed ↔ PMC, Gene ↔ Protein) via cross-links.
  • You must retrieve lightweight summaries (titles, IDs, basic metadata) before deciding which full records to fetch.
  • You are integrating NCBI E-utilities into an automated pipeline and need API key usage and rate-limit-aware requests.

Key Features

  • Supports core NCBI E-utilities via Bio.Entrez: esearch, efetch, esummary, elink.
  • Query-based searching and ID list retrieval for downstream batch operations.
  • Batch downloading of records in common formats (e.g., GenBank, FASTA, XML).
  • API key configuration and rate-limit-friendly request patterns.
  • XML response parsing using Biopython’s Entrez parsers for structured results.
  • Standardized configuration and invocation conventions:
    • Write runtime configuration to config/task_config.json.
    • Invoke tasks via python scripts/<task_name>.py.
    • Avoid stacking many CLI -- parameters; prefer config files.
    • Use explicit UTF-8 encoding for file I/O and ensure_ascii=False for JSON output.

Dependencies

  • biopython>=1.80

Example Usage

The following example is a complete, runnable script that:

  1. searches PubMed, 2) retrieves summaries for the top results, and 3) writes output to JSON.

1) Create config/task_config.json:

json
{
  "email": "your-email@example.com",
  "api_key": "",
  "db": "pubmed",
  "term": "CRISPR Cas9 2020[PDAT]",
  "retmax": 5,
  "out_json": "outputs/pubmed_summaries.json"
}

2) Create scripts/pubmed_summaries.py:

python
import json
import os
import time
from typing import Any, Dict, List

from Bio import Entrez


def load_config(path: str) -> Dict[str, Any]:
    with open(path, "r", encoding="utf-8") as f:
        return json.load(f)


def ensure_parent_dir(path: str) -> None:
    parent = os.path.dirname(path)
    if parent:
        os.makedirs(parent, exist_ok=True)


def main() -> None:
    cfg = load_config("config/task_config.json")

    Entrez.email = cfg["email"]
    api_key = cfg.get("api_key") or ""
    if api_key:
        Entrez.api_key = api_key

    db = cfg.get("db", "pubmed")
    term = cfg["term"]
    retmax = int(cfg.get("retmax", 20))
    out_json = cfg.get("out_json", "outputs/pubmed_summaries.json")

    # 1) ESearch: get IDs
    with Entrez.esearch(db=db, term=term, retmax=retmax, usehistory="n") as handle:
        search_result = Entrez.read(handle)

    id_list: List[str] = search_result.get("IdList", [])
    if not id_list:
        ensure_parent_dir(out_json)
        with open(out_json, "w", encoding="utf-8") as f:
            json.dump({"query": term, "count": 0, "items": []}, f, ensure_ascii=False, indent=2)
        return

    # Be polite with NCBI: small delay (especially without API key)
    time.sleep(0.34 if api_key else 0.5)

    # 2) ESummary: get summaries for IDs
    with Entrez.esummary(db=db, id=",".join(id_list), retmode="xml") as handle:
        summary_result = Entrez.read(handle)

    items = []
    for docsum in summary_result:
        items.append({
            "id": str(docsum.get("Id", "")),
            "title": str(docsum.get("Title", "")),
            "pubdate": str(docsum.get("PubDate", "")),
            "source": str(docsum.get("Source", "")),
            "authors": [str(a.get("Name", "")) for a in docsum.get("AuthorList", [])],
        })

    payload = {
        "query": term,
        "count": len(items),
        "items": items,
    }

    ensure_parent_dir(out_json)
    with open(out_json, "w", encoding="utf-8") as f:
        json.dump(payload, f, ensure_ascii=False, indent=2)


if __name__ == "__main__":
    main()

3) Run:

bash
python scripts/pubmed_summaries.py
Show full SKILL.md (200 more words)Show less

Implementation Details

  • Core E-utilities mapping

    • ESearch: builds a query against an NCBI database and returns matching IDs (and optionally WebEnv/QueryKey for history-based batching).
    • ESummary: returns lightweight document summaries for a list of IDs.
    • EFetch: downloads full records (e.g., GenBank/FASTA/XML) for IDs; choose rettype/retmode based on the target database.
    • ELink: discovers cross-database relationships (e.g., PubMed → PMC, Gene → Protein).

  • Batching strategy

    • Prefer ESearch to obtain IDs, then call ESummary/EFetch in chunks (e.g., 100–500 IDs per request depending on payload size).
    • For large jobs, consider usehistory="y" in ESearch and then fetch via WebEnv/QueryKey to avoid very long ID lists.

  • Rate limiting and API key

    • NCBI enforces request limits; using an API key increases allowed throughput.
    • Implement a small delay between requests and retry on transient network errors (HTTP 429/5xx) with backoff.

  • Parsing

    • Use Entrez.read(handle) for structured parsing of XML responses into Python objects.
    • For raw text formats (e.g., FASTA), use handle.read() and write to disk with encoding="utf-8" where applicable.

  • Configuration and I/O conventions

    • Store runtime parameters in config/task_config.json as an intermediate artifact.
    • Avoid complex CLI flags; keep scripts callable as python scripts/<task_name>.py.
    • Always specify encoding="utf-8" for file I/O and use ensure_ascii=False for JSON outputs.

  • Reference

    • See references/databases.md for database notes and selection guidance.

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

Files

SKILL.md and 3 other files (references) in scientific-skills/Evidence Insight/biopython-entrez of aipoch/medical-research-skills.

  • SKILL.md
  • biopython-entrez_audit_result_v1.json
  • config/task_config.json
  • references/databases.md

Open the folder on GitHubat commit 686e09d

Compare with similar skills

Biopython Entrez 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.

Biopython Entrez compared with similar skills
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Bio Entrez LinkGPTomics/bioSkills1.2k2 repos~3.8kAutomated safety check: PassMIT
Ena Databasejaechang-hits/SciAgent-Skills3701 repos~5.3kAutomated safety check: PassCustom licence
Biopythondavila7/claude-code-templates32k13 repos~3.4kAutomated safety check: PassMIT
BiopythonK-Dense-AI/scientific-agent-skills48k1 repos~4.3kAutomated safety check: NotesMIT

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Works with

Categories

Questions about Biopython Entrez

What does Biopython Entrez do?

Use Bio.Entrez to access NCBI databases (e.g., PubMed/GenBank) for searching, fetching summaries, and downloading records when your workflow needs to call the NCBI E-utilities API over the network. Biopython Entrez is an agent skill from aipoch/medical-research-skills., PubMed/GenBank) for searching, fetching summaries, and downloading records when your workflow needs to call the NCBI E-utilities API over the network.

When should I use Biopython Entrez?

Biopython Entrez fits situations like: tasks that involve Bioinformatics; tasks that involve Academic paper search.

How do I install Biopython Entrez in Claude Code?

Run `npx skills add aipoch/medical-research-skills --skill biopython-entrez -a claude-code`. Or copy the skill folder (scientific-skills/Evidence Insight/biopython-entrez in aipoch/medical-research-skills) into .claude/skills/biopython-entrez in your project. Claude Code loads it when a task matches its description.

How do I install Biopython Entrez in Codex?

Run `npx skills add aipoch/medical-research-skills --skill biopython-entrez -a codex`. Or copy the skill folder (scientific-skills/Evidence Insight/biopython-entrez in aipoch/medical-research-skills) into .agents/skills/biopython-entrez in your project. Codex loads it when a task matches its description.

Can I use Biopython Entrez 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 aipoch/medical-research-skills --skill biopython-entrez -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/biopython-entrez, .gemini/skills/biopython-entrez, .github/skills/biopython-entrez and .opencode/skills/biopython-entrez in your project.

What does Biopython Entrez need to run?

Going by SKILL.md and its folder, Biopython Entrez needs the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Biopython Entrez access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Biopython Entrez 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 Biopython Entrez use?

Biopython Entrez is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Biopython Entrez use?

About 1.5k tokens (SKILL.md is roughly 6.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 3k tokens, read only when the agent opens those files.

What are the alternatives to Biopython Entrez?

Skills that share tags, products or a category with Biopython Entrez: Bio Entrez Fetch (GPTomics/bioSkills, 1.2k stars), Bio Entrez Link (GPTomics/bioSkills, 1.2k stars), Ena Database (jaechang-hits/SciAgent-Skills, 370 stars) and Biopython (davila7/claude-code-templates, 32k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Biopython Entrez?

aipoch (a GitHub organization) maintains it in aipoch/medical-research-skills, which has 1,974 GitHub stars. The repository holds 567 skills in this directory. The repository was last updated on September 17, 2026.

Source: aipoch/medical-research-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.