Pubmed Database

PubMed API

Prerequisites

1. uv: Read the uv skill and follow its Setup instructions to ensure

uv is installed and on PATH.

1. User Notification: If LICENSE_NOTIFICATION.txt does not already exist in

this skill directory then (1) prominently notify the user to check the terms at https://pubmed.ncbi.nlm.nih.gov/disclaimer/ and https://www.ncbi.nlm.nih.gov/home/about/policies/ and to always check the license of the papers retrieved by the skill for any restrictions, then (2) create the file recording the notification text and timestamp.

1. .env file: Make sure the .env file exists in your home directory.

Create one if it does not exist.

1. NCBI_API_KEY (optional): Raises the NCBI E-utilities rate limit from 3

to 10 requests/second. The skill works without it, but a key is recommended if the user plans many queries or encounters a 429 error. The user can obtain one for free by registering at https://www.ncbi.nlm.nih.gov/account/settings/

1. USER_EMAIL (optional but recommended): Identifies the caller to NCBI

(recommended by their Terms of Use).

If the variables are missing from .env, do NOT ask the user to paste them into the chat (this would leak keys into the agent's context). Instead, give the user these commands — substituting ENV_FILE with the resolved literal path to the .env file:

printf "Enter NCBI API key (typing hidden): " && read -s key && echo && echo "NCBI_API_KEY=$key" >> "ENV_FILE" && echo "Saved."

printf "Enter contact email: " && read email && echo "USER_EMAIL=$email" >> "ENV_FILE" && echo "Saved."

The scripts load credentials automatically via dotenv. NEVER read, print, or inspect the .env file or its variables (e.g. no cat, grep, echo, printenv, or os.environ.get on keys). Credentials must stay out of the agent's context.

This skill provides CLI access to the NCBI PubMed and PubMed Central APIs via scripts/pubmed_api.py — a single CLI with 10 functions covering search, fetch, linking, full text, spelling, discovery, citation matching, and caching.

Core Rules

• API Use: Always use the provided wrapper scripts/pubmed_api.py which

manages rate limits automatically and prevents API abuse. Setting the NCBI_API_KEY environment variable raises the rate limit from 3 to 10 requests/second. Querying the API any other way (e.g. via curl, wget, or hand-written code) is strictly forbidden.

• JSON Processing: Use jq to filter and transform JSON output (or python

equivalents if jq is not available) to prevent hallucinations and context overflow.

• Temporary Files: To avoid polluting the working directory with JSON

files, use a temporary directory inside the current directory. When running multiple agents or tasks in parallel, ensure each uses a unique subdirectory name (e.g., tmp_$TASK_ID/) to avoid file collisions.

• Notification: If this skill is used, ensure this is mentioned in the

output AND list the URLs of all papers that were used in producing the output.

Structure of the skill folder

• SKILL.md - This file
• scripts/pubmed_api.py - The skill CLI
• references/ - Directory with detailed function specifications
• advanced-linking.md
• advanced-search.md
• bulk-workflows.md
• citation-matching.md
• cross-database-linking.md
• fetch-and-resolve.md
• search-and-discovery.md
• utilities.md

CLI Usage

uv run scripts/pubmed_api.py    [--flag value ...]

• Positional Arguments: Arguments are positional; list arguments are

passed as comma-separated strings without spaces (e.g. "35113657,31234568").

• Flag Options: Optional arguments can be passed as --flag value instead

of positional args.

• Output Handling: On success, JSON is written to output_file. On error,

the process exits with a non-zero code and no output file is written.

Example Usage

uv run scripts/pubmed_api.py ./search_results.json search_pubmed "BRCA1" --max_results 5
cat ./search_results.json | jq '.[]' -r
uv run scripts/pubmed_api.py ./abstracts.json fetch_article_abstracts "35113657"
cat ./abstracts.json | jq '.[0].title' -r

Essential Recipes

Join PMIDs for the next call (most common chaining pattern):

cat ./search_results.json | jq -r 'join(",")'

Slim abstracts to essential fields and truncate long abstracts:

cat ./abstracts.json | jq '[.[] | {pmid, title, snippet: (.abstract // "")[:500]}]'

Filter by keyword (null-safe):

cat ./abstracts.json | jq '[.[] | select((.title // "") | contains("Review"))]'

Context Management & Accuracy

When processing larger result sets (>10 abstracts):

1. Filter Early: Use jq to verify keywords in abstracts before reading

the full JSON into context.

1. Slimming: Extract only title and abstract fields unless explicitly

instructed otherwise. Author lists and metadata contribute to noise.

1. Bulk Operations (N > 10): Avoid fetching or processing IDs one-by-one.

The API and History Server are designed for bulk retrieval. Fetch all data in a single turn and use shell pipelines to slim the results before reading into context. This prevents turn exhaustion and context overflow.

1. Grounding: Never use internal knowledge to provide specific identifiers

(PMIDs, CIDs, Gene IDs) if no results are found. Report the tool's output accurately to ensure results are grounded in the current database state.

1. Search Termination: When asked to find papers that may not exist, limit

exploration to 3–5 high-quality, varied search queries. If no results match after these attempts, conclude that no papers meet the criteria rather than continuing to iterate — unless explicitly instructed to be thorough.

Functions

> ⚠️ MANDATORY: You MUST read the linked reference file for a function > group before calling any function in that group. The tables below only > describe what each function does — not how to call it. Argument names, > argument order, flags, and output schemas are only documented in the > reference files. Do NOT guess or infer arguments from function names. If > you call a function without first reading its reference, you will produce > incorrect invocations.

[Search](references/search-and-discovery.md)

• search_pubmed: Find PMIDs matching a free-text or structured NCBI query.
• global_database_discovery: Count how many records match a query across

every NCBI database.

[Fetch & Resolve](references/fetch-and-resolve.md)

• fetch_article_abstracts: Retrieve metadata and abstracts for a batch of

PMIDs.

• get_full_text_pmc: Retrieve open-access full text from PMC.
• fetch_database_summary: Resolve opaque UIDs from any NCBI database into

human-readable metadata.

[Cross-Database Linking](references/cross-database-linking.md)

• find_linked_biological_data: Find records in other NCBI databases linked

to a source record.

• discover_available_links: List all available ELink linknames for a given

record.

[Bulk Workflows](references/bulk-workflows.md)

When working with more than ~10 PMIDs, avoid processing IDs one-by-one. Upload them to the NCBI History Server via cache_results_history to get a session handle (webenv + query_key), then pass that handle to fetch_article_abstracts or find_linked_biological_data for a single bulk call. Chain with jq shell pipelines to slim results before reading into context. This prevents turn exhaustion and context overflow. See the reference for complete workflow recipes (search→fetch, cross-db exploration, citation resolution, and bulk retrieval with data slimming).

• cache_results_history: Upload PMIDs to the NCBI History Server for bulk

retrieval.

[Utilities](references/utilities.md)

• verify_medical_spelling: Spell-check biomedical terms before searching.
• match_raw_citations: Resolve incomplete bibliographic citations to PMIDs.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: google-deepmind
• Source: google-deepmind/science-skills
• License: Apache-2.0
• Homepage: https://antigravity.google/use-cases/science

Install and usage instructions live in the source repository linked above.