Pubmed search
Skill pantheon-org/tekhne/skills/documentation/research/pubmed-search
Agents Skills
npx -y skills add pantheon-org/tekhne --skill pubmed-searchAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- 9 stars9 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
What its author says it does
Copied from the file, not written here
Search and analyze biomedical literature from PubMed using the free E-utilities API. Use when researching medical topics, discovering clinical papers, fetching article metadata by PMID, performing deep paper analysis, or downloading open-access PDFs from PubMed Central. Triggers: pubmed search, search biomedical literature, find medical papers, PMID lookup, pubmed metadata, clinical literature search, biomedical research, life sciences papers, PMC download, ncbi search.
SKILL.md
6.2 KB, as published. Nobody here has run it
PubMed Search
Search and analyze biomedical literature from PubMed using the free NCBI E-utilities API.
When to Use
- Discovering biomedical or clinical papers by keyword, author, journal, or date range
- Fetching structured metadata (title, authors, abstract, DOI) for a known PMID
- Performing deep analysis of a paper when only the abstract and metadata are available
- Downloading open-access full-text PDFs from PubMed Central (PMC)
- Building a candidate list before running
triage-paper
When Not to Use
- The paper is already known (DOI, URL) — go straight to
triage-paper - A
semantic-scholarMCP or PubTator MCP is configured — prefer the MCP; it returns structured data with no rate-limit risk - The search is for general academic literature — use
google-scholar-searchorsemantic-scholar-search - A candidate JSON already exists at
/tmp/<topic>-candidates.json— reuse it
Recommended MCP Server
When available, prefer the PubTator MCP server over this script:
{
"mcpServers": {
"pubtator": {
"type": "stdio",
"command": "uvx",
"args": ["pubtator-mcp-server"]
}
}
}
Mindset
Search is discovery, not analysis. The goal is a structured candidate list.
- Rate limits are a gotcha: without an API key the limit is 3 req/s; a pitfall is issuing bulk PMID fetches without a delay, causing silent failures or 429s. ALWAYS add a short delay between batch calls.
- MCP first: ALWAYS check whether a PubTator or PubMed MCP is configured before invoking the Python script. MCPs are faster, structured, and avoid rate-limit risk.
- Open access is not guaranteed: a pitfall is assuming all PMC articles can be downloaded. NEVER attempt to download a PDF without first confirming PMC availability and open-access status.
Workflow
1. Check MCP availability
ALWAYS check for a pubtator or pubmed MCP before running the script. If configured and reachable, prefer it.
2. Set up the environment (first run only)
See setup-and-troubleshooting.md for venv creation and dependency installation.
3. Run the search
Activate the venv, then choose the appropriate subcommand. You may optionally add --show-abstract to keyword searches for a richer preview.
# Basic keyword search
./scripts/pubmed_search.py search --keywords "CRISPR gene editing" --results 10
# Advanced: filter by author, journal, and date range
./scripts/pubmed_search.py search --term "cancer immunotherapy" --author "Smith" \
--journal "Nature" --start-date "2021" --end-date "2024" --results 20
# Fetch metadata for a known PMID
./scripts/pubmed_search.py metadata --pmid "33303479" --format json
# Deep paper analysis
./scripts/pubmed_search.py analyze --pmid "33303479" --output analysis.md
# Download open-access PDF
./scripts/pubmed_search.py download --pmid "33303479" --output-dir ./papers/
# Export candidate list to JSON
./scripts/pubmed_search.py search --keywords "Alzheimer disease biomarkers" \
--results 50 --format json --output /tmp/candidates.json
4. Handle rate-limit errors
If the script returns HTTP 429, wait 30 s and retry once:
sleep 30 && ./scripts/pubmed_search.py search --keywords "<topic>" --results 10
NEVER retry in a tight loop. If still failing, set PUBMED_API_KEY in the environment.
5. Present candidates and hand off
NEVER triage automatically — ALWAYS confirm with the user first:
Found N results. Would you like to triage any of these with
triage-paper?
Anti-Patterns
NEVER auto-triage search results
WHY: Discovery and triage are separate quality gates. Auto-triaging bypasses user review.
BAD Pass every result to triage-paper immediately. → GOOD Present the list; wait for the user to choose.
NEVER retry in a tight loop on 429
WHY: Repeated rapid retries worsen the block and extend the cooldown period.
BAD Loop search until it succeeds. → GOOD Retry once after 30 s; switch to MCP or API key on second failure.
NEVER assume all PMC articles are downloadable
WHY: Many PMC articles are not open access; the download command will fail or return a redirect link.
BAD Call download for every PMID. → GOOD Check PMC availability and open-access status before downloading.
NEVER skip the MCP availability check
WHY: The Python script is the fragile fallback. Skipping the check needlessly risks rate-limiting.
BAD Invoke the script without checking for a PubTator or PubMed MCP. → GOOD ALWAYS check MCP availability first; only fall back to the script if no MCP is configured.
NEVER hardcode API keys in scripts or task files
WHY: API keys committed to source are a production security risk and will be rotated or revoked.
BAD Set api_key = "abc123" inside the script. → GOOD ALWAYS use environment variables (PUBMED_API_KEY) or a .env file that is gitignored.
NEVER treat abstract-based analysis as a full-text review
WHY: PubMed metadata contains only the abstract. Deep analysis based solely on abstracts is incomplete and a pitfall for research quality.
BAD Mark a paper as "fully analysed" from analyze output alone. → GOOD Qualify analysis as "abstract-based" and recommend obtaining the full text for production use.
References
- Setup: setup-and-troubleshooting.md
- Script: pubmed_search.py
- Dependencies: requirements.txt
- Upstream: JackKuo666/pubmed-search-skill
- MCP alternative: PubTator-MCP-Server