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pubmed-database — Claude Code skill: search PubMed literature

Gives your agent a CLI into PubMed and PubMed Central: search papers, pull abstracts and full text, link publications to biological databases.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓
npx skills add google-deepmind/science-skills --skill pubmed-database -a claude-code
npx skills add google-deepmind/science-skills --skill pubmed_database -a claude-code
Open on GitHub

What pubmed-database does

The skill wraps NCBI E-utilities and the PMC BioC API in a single CLI, scripts/pubmed_api.py, with ten functions. Search by free-text or structured query, fetch abstracts for a batch of PMIDs, get full text from PMC, link papers to gene, protein, nucleotide and PubChem records, verify medical spelling, match raw citations, and cache result sets for bulk work. The agent runs it through uv run and pipes JSON through jq, which keeps author lists and metadata out of context.

You need uv on PATH and a .env file in your home directory before the first call. NCBI_API_KEY is optional and raises the E-utilities rate limit from 3 to 10 requests per second; without it the skill still runs, but a long series of queries can hit a 429. USER_EMAIL is also optional and just identifies the caller to NCBI.

Calling the API any other way, including curl or hand-written code, is off the table — the wrapper handles rate limits for you. On first use the skill asks you to check the PubMed and NCBI terms and drops a marker file under .licenses/. For bulk retrieval, read the reference file for a function group before calling it: argument order and flags live there, and guessing from function names will produce bad invocations.

What it helps with

  • find papers on a topic and get back PMIDs
  • pull abstracts for a batch of identifiers
  • link a paper to a specific gene or compound
  • match a raw citation to a PubMed record
  • check medical spelling before running a search

Example prompts for the agent

  • Search PubMed for BRCA1 papers, show 5 results and list the titles
  • Fetch abstracts for PMID 35113657 and keep only title and abstract
  • Link this paper to PubChem and Gene records

How to install pubmed-database

1Skills CLI (Vercel)

npx skills add google-deepmind/science-skills --skill pubmed-database -a claude-code
npx skills add google-deepmind/science-skills --skill pubmed_database -a claude-code

2Manually: put the files in place

~/.claude/skills/pubmed_database/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/pubmed_database/

Step by step, with commands for each platform: How to install skills in Claude Code: five ways

Questions about pubmed-database

How do I install pubmed-database in Claude?

Run npx skills add google-deepmind/science-skills --skill pubmed-database -a claude-code, then npx skills add google-deepmind/science-skills --skill pubmed_database -a claude-code. The skill file ends up at ~/.claude/skills/pubmed_database/SKILL.md.

What does pubmed-database do?

It searches PubMed for scientific literature, fetches abstracts and PMC full text, and links papers to gene, protein, nucleotide and PubChem databases. It also verifies medical spelling and matches raw citations.

Do I need an NCBI API key for this skill?

No, it works without one. NCBI_API_KEY raises the rate limit from 3 to 10 requests per second, so it is worth registering if you plan many queries or keep hitting a 429.

Source text

SKILL.md · Apache-2.0
---
name: pubmed-database
description: >-
  Search PubMed for scientific literature, including published clinical trials.
  Fetch abstracts and full text. Link published research to biological databases
  (gene, protein, nucleotide, PubChem) to discover associations between papers
  and specific compounds or genes. Verify medical spelling, match raw citations,
  and cache result sets for bulk processing. Interfaces NCBI E-utilities and PMC
  BioC APIs.
---

# PubMed API

## Prerequisites

1.  **`uv`**: Read the `uv` skill and follow its Setup instructions to ensure
    `uv` is installed and on PATH.
2.  **User Notification**: If .licenses/pubmed_database_LICENSE.txt does not
    already exist in the workspace root 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.
3.  **`.env` file**: Make sure the `.env` file exists in your home directory.
    Create one if it does not exist.
4.  **`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. You can register
    for a key for free at https://www.ncbi.nlm.nih.gov/account/settings/. You
    **MUST** use the safe credentials protocol in the `credentials` skill to
    check for and request this key if this skill looks relevant to the user's
    request.
5.  **`USER_EMAIL`** (optional): Identifies the caller to NCBI (recommended by
    their Terms of Use). You **MUST** use the safe credentials protocol in the
    `credentials` skill to check for and request this credential if this skill
    looks relevant to the user's request.

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

```bash
uv run scripts/pubmed_api.py <output_file> <function_name> <required_args> [--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

```bash
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):**

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

**Slim abstracts to essential fields and truncate long abstracts:**

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

**Filter by keyword (null-safe):**

```bash
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.
2.  **Slimming**: Extract only `title` and `abstract` fields unless explicitly
    instructed otherwise. Author lists and metadata contribute to noise.
3.  **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.
4.  **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.
5.  **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.

© google-deepmind · Apache-2.0 · google-deepmind/science-skills

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