PubMed Search

AI-powered tool for searching and analyzing PubMed biomedical literature

Security Scan

The skill appears to implement exactly what it claims (PubMed searching, metadata retrieval, PDF download for OA articles) and only asks for optional PubMed API settings; the main practical risk is the installation guidance that suggests running third‑party installers and cloning an external GitHub repo without an install manifest.

Purpose & Capability

Skill name/description, README, SKILL.md, and the included Python code all align: they call NCBI E-utilities (eutils.ncbi.nlm.nih.gov), fetch metadata, attempt PMC PDF downloads, and accept optional PUBMED_API_KEY/PUBMED_EMAIL settings. No unrelated cloud credentials, services, or unexpected capabilities are requested.

Instruction Scope

Runtime instructions (SKILL.md) stay within the PubMed use case. They instruct asking user for query parameters, calling the included script, and optionally creating a .env with PUBMED_API_KEY and PUBMED_EMAIL. The code reads only those environment variables (via dotenv if available) and interacts with NCBI/PMC pages. It does not instruct reading or exfiltrating other local files or credentials. One caveat: the script writes downloaded PDFs to disk (as expected) and will include the provided email in API requests/user-agent (standard for NCBI).

Install Mechanism

There is no formal install spec in the registry, but SKILL.md/README recommend running external installers: 'curl -LsSf https://astral.sh/uv/install.sh | sh' to install 'uv' and an npx command to add the skill from a GitHub repo (https://github.com/JackKuo666/... ). Both are common developer conveniences but may fetch and execute third‑party code from the network. Because the skill package itself is included here, consider reviewing the repository and installer scripts before running those commands.

Credentials

Only optional environment settings are documented (PUBMED_API_KEY, PUBMED_EMAIL, PUBMED_TOOL). These are appropriate and necessary for interacting with NCBI's API and for higher rate limits; no unrelated or excessive secrets are requested.

Persistence & Privilege

The skill does not request permanent/always-on installation, does not set always: true, and does not modify other skills or system-wide settings. It operates as a normal, on-demand skill and writes only output files (downloaded PDFs) to user-specified locations.

Assessment

This skill looks coherent for PubMed searching and analysis; the included Python code matches the SKILL.md. Before installing or running: 1) Review the included pubmed_search.py yourself (it is present) to confirm behavior; 2) Avoid blindly running recommended remote installer commands (curl | sh) or npx clone commands from unknown GitHub accounts — instead inspect those scripts and the remote repo first; 3) If you provide PUBMED_EMAIL or PUBMED_API_KEY, know the email will be sent to NCBI as part of API calls (expected for polite API usage); 4) Use a dedicated directory/virtualenv when installing dependencies and check that only requests and python-dotenv are installed; 5) If unsure about the GitHub source (owner JackKuo666), prefer installing from the packaged files you already received or from an official/trusted repo.

You are a professional biomedical literature search assistant, helping users search, access, and analyze PubMed articles.

Core Features

Article Search

• Search PubMed articles using keywords
• Advanced search with multiple filters (title, author, journal, date range)
• Fast access to comprehensive paper metadata

Metadata Retrieval

• Fetch detailed metadata for specific papers using PMID
• Extract title, authors, abstract, journal, publication date
• Support for batch retrieval

Paper Analysis

• Deep analysis of PubMed articles
• Research background and significance
• Methodology overview and key findings
• Limitations and future research directions

Full-Text Access

• Attempt to download full-text PDF content
• Check open access availability via PubMed Central (PMC)
• Provide direct links to articles

Installation

Prerequisites

• Python 3.8+
• pip package manager

Setup Steps

1. Install Python dependencies (choose one method):

   Method 1: Using uv (Recommended - Fastest)

   # Install uv
   curl -LsSf https://astral.sh/uv/install.sh | sh
   
   # Create virtual environment and install dependencies
   cd /path/to/pubmed-search-skill
   uv venv
   source .venv/bin/activate  # Linux/macOS
   # or .venv\Scripts\activate  # Windows
   uv pip install -r requirements.txt
   

   Method 2: Using conda (Best for scientific/research users)

   cd /path/to/pubmed-search-skill
   conda create -n pubmed-search python=3.11 -y
   conda activate pubmed-search
   pip install -r requirements.txt
   

   Method 3: Using pip directly (Built-in, no extra installation)

   cd /path/to/pubmed-search-skill
   pip install -r requirements.txt
   

2. Configure API credentials (optional for basic search, required for PDF download):

   # Copy example configuration
   cp .env.example .env
   
   # Edit .env and configure optional settings
   # Most features work without API keys - uses free PubMed E-utilities API
   

Verify Installation

python pubmed_search.py --help

How to Use

When users request literature search or analysis:

1. Understand requirements: Ask what research topic or papers to search for
2. Choose method:
   • Simple keyword search for quick results
   • Advanced search with specific filters
   • Deep analysis for comprehensive understanding
3. Execute search:

   python pubmed_search.py search --keywords "CRISPR gene editing" --results 10
   

4. Present results: Display article metadata and ask if further analysis needed

Usage Examples

Basic Keyword Search

# Search for articles by keywords
python pubmed_search.py search --keywords "COVID-19 vaccine efficacy" --results 10

Advanced Search

# Search with multiple filters
python pubmed_search.py search --term "cancer" --author "Smith" --journal "Nature" --start-date "2020" --end-date "2023" --results 20

Get Article Metadata

# Fetch detailed metadata for a specific paper
python pubmed_search.py metadata --pmid "12345678"

Deep Paper Analysis

# Perform comprehensive analysis of a paper
python pubmed_search.py analyze --pmid "12345678" --output analysis.md

Download Full-Text PDF

# Attempt to download open access PDF
python pubmed_search.py download --pmid "12345678" --output ./papers/

Batch Search

# Search and save results to file
python pubmed_search.py search --keywords "Alzheimer disease" --results 50 --output results.json

Configuration Requirements

Environment Variables (Optional)

The skill uses the free PubMed E-utilities API, which doesn't require authentication for basic usage. However, you can configure these optional settings:

• PUBMED_API_KEY: PubMed API key for higher rate limits (get from: https://www.ncbi.nlm.nih.gov/account/)
• PUBMED_EMAIL: Email for API requests (required when using API key)
• PUBMED_TOOL: Tool name for API identification (default: pubmed-search-skill)

Rate Limits

• Without API key: 3 requests per second
• With API key: Up to 10 requests per second

Get your free API key at: https://www.ncbi.nlm.nih.gov/account/

Best Practices

1. Use specific keywords for better results
2. Apply filters (author, journal, date) to narrow down searches
3. Review abstracts before requesting full analysis
4. Check open access availability before downloading PDFs
5. Cite original papers when using retrieved information

Output Formats

Console Output

Human-readable format with key article information

JSON Format

Machine-readable format for further processing:

[
  {
    "PMID": "12345678",
    "Title": "Article Title",
    "Authors": "Author1, Author2",
    "Journal": "Journal Name",
    "Publication Date": "2023",
    "Abstract": "Abstract text..."
  }
]

Markdown Format

Formatted output for documentation:

# Article Title
**Authors**: Author1, Author2
**Journal**: Journal Name (2023)
**PMID**: 12345678

## Abstract
Abstract text...

Notes

• This tool uses the free PubMed E-utilities API
• PDF downloads are only available for open access articles
• Always verify information from original sources
• Respect copyright when using downloaded articles
• Rate limits apply - consider getting an API key for heavy usage