Clinical Trials Database logo

Clinical Trials Database

OrganizationPopular
google-deepmind
clinical-trials-database

Query ClinicalTrials.gov via APIv2. Use when you want to search for trials by condition, drug, location, status, or phase; retrieve trial details by NCT ID; check eligibility/inclusion criteria; count trials across conditions or time periods; identify a sponsor's trial portfolio; find recruiting trials for patient matching.

Overview

Publishergoogle-deepmind
Repositoryscience-skills
Skill nameclinical-trials-database
Stars
3.1K
Forks
349
Bundled files
4
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by google-deepmind on GitHub. Read the source before you install it.

Installation

Install the Clinical Trials Database AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/google-deepmind/science-skills.git /tmp/science-skills
mkdir -p .claude/skills
cp -r /tmp/science-skills/skills/clinical_trials_database .claude/skills/clinical-trials-database
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Clinical Trials Database in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Clinical Trials Database on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Clinical Trials Database is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Clinical Trials Database

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/clinical_trials_database_LICENSE.txt does not already exist in the workspace root directory then (1) prominently notify the user to check the terms at https://clinicaltrials.gov/, then (2) create the file recording the notification text and timestamp.

Overview

Access worldwide clinical trial data from ClinicalTrials.gov via the REST API v2. The CLI script at scripts/clinical_trials_api.py wraps the API with dedicated flags for common filters (phase, age group, status, intervention, sponsor, etc.) so you rarely need to construct raw queries.

Core Rules

  • Use the Wrapper: ALWAYS execute the provided helper scripts to query the database rather than accessing the database directly. The scripts automatically enforce the required rate limit gracefully.
  • Always use --fields — trial JSON records can be very large; restrict to the data points you need.
  • Use --count-total first — check result volume before fetching all records.
  • Paginate large result sets — use --limit with --page-token to iterate.
  • Trust Search Filters: Do not manually re-filter results unless explicitly asked to verify detailed eligibility.
  • Notification: If this skill is used, ensure this is mentioned in the output.

Context Efficiency Warning

Trial JSON records can be very large. Always use the --fields parameter to restrict the response to only the data points you need. After writing to file, read only the fields you need rather than the entire file.

[!TIP] Use references/studies_schema.md to identify exact field paths for --fields.

Response Layout Summary

API responses contain a list of studies (usually in a studies[] array). Each study is split into protocolSection and optional resultsSection.

[!Tip] Use the shorthand aliases below with the --fields parameter to request specific data and keep responses small.

Top-Level Fields

  • totalCount — Total studies matching query (integer)
  • studies[] — Array of study objects
  • nextPageToken — cursor string for pagination

Common Study Fields (and shorthand alias)

  • Identification
    • protocolSection.identificationModule.nctId (NCTId) — Unique trial ID
    • protocolSection.identificationModule.briefTitle (BriefTitle) — Short title
  • Status
    • protocolSection.statusModule.overallStatus (OverallStatus) — Recruitment status
  • Description
    • protocolSection.descriptionModule.briefSummary (BriefSummary) — Short description
  • Arms & Interventions
    • protocolSection.armsInterventionsModule.interventions (ArmsInterventionsModule)
  • Eligibility
    • protocolSection.eligibilityModule.eligibilityCriteria (EligibilityCriteria) — Inclusion/Exclusion
    • protocolSection.eligibilityModule.stdAges (StdAge) — CHILD, ADULT, etc.

Consult references/studies_schema.md for full paths (Locations, Outcomes, Results) and common --fields recipes.

Commands

Search for studies

Use for: finding trials by disease, drug, phase, status, age group, or any combination of these filters.

bash
uv run scripts/clinical_trials_api.py search \
  --condition "<disease>" \
  --intervention "<drug_or_treatment>" \
  --status "<status>" \
  --phase "<phase>" \
  --age-group "<age_group>" \
  --study-type "<study_type>" \
  --sponsor "<sponsor_name>" \
  --has-results \
  --sort "<field>:<asc|desc>" \
  --fields "<fields>" \
  --limit <N> \
  --count-total \
  --page-token "<token>" \
  --output /tmp/search_results.json

All flags are optional and combine via AND logic.

Flag reference:

  • --condition — Disease or condition to search for (e.g. "cystic fibrosis").
  • --intervention — Drug, device, or treatment name (e.g. "pembrolizumab").
  • --status — Recruitment status filter. Values: RECRUITING, COMPLETED, NOT_YET_RECRUITING, ACTIVE_NOT_RECRUITING, ENROLLING_BY_INVITATION, TERMINATED, SUSPENDED, WITHDRAWN.
  • --phase — Trial phase filter. Values: PHASE1, PHASE2, PHASE3, PHASE4, EARLY_PHASE1, NA.
  • --age-group — Patient age group filter. Values: CHILD (0–17), ADULT (18–64), OLDER_ADULT (65+).
  • --study-type — Type of study. Values: INTERVENTIONAL, OBSERVATIONAL, EXPANDED_ACCESS.
  • --sponsor — Lead sponsor or institution name (e.g. "National Cancer Institute").
  • --has-results — Boolean flag (no value needed). When present, filters for studies that have results available on ClinicalTrials.gov.
  • --sort — Sort order as FieldName:asc or FieldName:desc. Common fields: LastUpdatePostDate, EnrollmentCount, StudyFirstPostDate, StartDate.
  • --fields — Comma-separated list of JSON field names to include in the response. Use this to keep responses small (e.g. "NCTId,BriefTitle,OverallStatus,Phase"). See references/studies_schema.md for available field paths.
  • --limit — Maximum number of studies to return per request (1–1000, default 10).
  • --count-total — Boolean flag (no value needed). When present, the response includes a totalCount field showing the total number of matching studies across all pages.
  • --page-token — An opaque cursor string used to fetch the next page of results. Obtain this value from the nextPageToken field in a previous search response. Do not construct this string yourself; always copy it verbatim from the API response. See the Pagination section below.
  • --advanced — Raw Essie filter expression for structured queries beyond the dedicated flags (e.g. "AREA[LocationCountry]United States"). Combined with other flags via AND. See references/clinical_trials_api.md for syntax.
  • --output(Required) File path where the JSON response is written.

Example — actively recruiting Phase 3 pediatric cystic fibrosis trials:

bash
uv run scripts/clinical_trials_api.py search \
  --condition "cystic fibrosis" \
  --status RECRUITING \
  --phase PHASE3 \
  --age-group CHILD \
  --fields "NCTId,BriefTitle,OverallStatus,Phase" \
  --limit 10 \
  --output /tmp/cf_trials.json

Example — recruiting atezolizumab trials for esophageal cancer:

bash
uv run scripts/clinical_trials_api.py search \
  --condition "esophageal cancer" \
  --intervention "Atezolizumab" \
  --status RECRUITING \
  --fields "NCTId,BriefTitle,Phase" \
  --limit 10 \
  --output /tmp/atezolizumab_trials.json

Retrieve a study by NCT ID

Use for: fetching full details of a specific trial when you already have the NCT identifier.

bash
uv run scripts/clinical_trials_api.py get-study \
  <nct_id> [--fields "<fields>"] \
  --output /tmp/study.json

Returns a useful default set of fields if --fields is omitted: NCTId,BriefTitle,OverallStatus,Phase,BriefSummary, ConditionsModule,ArmsInterventionsModule,EligibilityModule

Structure of the default response:

json
{
  "protocolSection": {
    "identificationModule": {
      "nctId": "NCT00000000",
      "briefTitle": "Study Title"
    },
    "statusModule": {
      "overallStatus": "RECRUITING"
    },
    "descriptionModule": {
      "briefSummary": "This study is about..."
    },
    "conditionsModule": {
      "conditions": [ "Condition Name" ]
    },
    "armsInterventionsModule": {
      "interventions": [ { "type": "DRUG", "name": "Drug Name" } ]
    },
    "eligibilityModule": {
      "eligibilityCriteria": "Inclusion:\n- ...",
      "stdAges": [ "ADULT" ]
    }
  }
}

Get eligibility / inclusion criteria

Use for: pulling inclusion/exclusion rules, age ranges, and sex requirements for patient-matching tasks.

bash
uv run scripts/clinical_trials_api.py \
  get-eligibility <nct_id> \
  --output /tmp/eligibility.json

Shortcut that returns title and the full eligibility module (inclusion/exclusion criteria, age range, sex).

Example — inclusion criteria for NCT04886804:

bash
uv run scripts/clinical_trials_api.py \
  get-eligibility NCT04886804 \
  --output /tmp/eligibility_NCT04886804.json

Count matching studies

Use for: exploring the trial landscape — checking how many trials exist for a condition, phase, or status before fetching full records.

bash
uv run scripts/clinical_trials_api.py count \
  --condition "<disease>" \
  [--status "<status>"] [--phase "<phase>"] ... \
  --output /tmp/count.json

Returns only the total count of clinical trials matching the search criteria without fetching study records. Accepts the same filter flags as search.

Search by location / geography

Use for: narrowing trials to a specific country, state, or city.

Use --advanced with AREA[LocationCountry] or AREA[LocationCity] to restrict results by geography:

bash
uv run scripts/clinical_trials_api.py search \
  --condition "cystic fibrosis" \
  --status RECRUITING \
  --advanced "AREA[LocationCity]New York" \
  --fields "NCTId,BriefTitle" \
  --limit 20 \
  --output /tmp/nyc_cf_trials.json

Search by sponsor / organization

Use for: identifying a sponsor's or institution's trial portfolio.

Use --sponsor to find trials run by a specific institution or company:

bash
uv run scripts/clinical_trials_api.py search \
  --sponsor "National Cancer Institute" \
  --fields "NCTId,BriefTitle,LeadSponsorName" \
  --limit 20 \
  --output /tmp/nci_trials.json

Combined multi-criteria search

Use for: complex queries that layer multiple filters (condition and drug and phase and geography and sponsor, etc.).

All flags combine via AND, so you can layer conditions, interventions, status, phase, geography, and sponsor in a single query:

bash
uv run scripts/clinical_trials_api.py search \
  --condition "pancreatic cancer" \
  --intervention "immunotherapy" \
  --status RECRUITING \
  --phase PHASE3 \
  --advanced "AREA[LocationCountry]United States" \
  --fields "NCTId,BriefTitle,Phase,LeadSponsorName" \
  --limit 20 \
  --output /tmp/panc_trials.json

Raw API query (escape hatch)

Use for: uncommon endpoints or parameter combinations not covered by the dedicated flags.

bash
uv run scripts/clinical_trials_api.py raw-query \
  --endpoint <path> \
  --params '<json_dict>' \
  --output /tmp/raw_result.json

Pagination

When results exceed --limit, the response includes a nextPageToken. Pass it with --page-token to fetch the next page:

bash
uv run scripts/clinical_trials_api.py search \
  --condition "breast cancer" \
  --status RECRUITING \
  --limit 50 --count-total \
  --output /tmp/breast_cancer_p1.json

uv run scripts/clinical_trials_api.py search \
  --condition "breast cancer" \
  --status RECRUITING \
  --limit 50 --page-token "CAo=" \
  --output /tmp/breast_cancer_p2.json

Advanced Querying

For complex filtering beyond the dedicated flags, use --advanced with an Essie expression.

What is an Essie Expression? Essie is the search engine powering ClinicalTrials.gov. An Essie expression is a structured query that targets specific fields (e.g., country, phase) rather than doing general keyword searches.

  • AREA[Field]Value: Targets a specific field.
    • AREA[LocationCountry]United States
    • AREA[Phase]PHASE3
  • Boolean operators: Combine with AND, OR, NOT.
  • RANGE[min, max]: For numeric/date fields (e.g. RANGE[500, MAX]).

See references/clinical_trials_api.md for syntax and available fields.

It is combined with other flags via AND:

bash
uv run scripts/clinical_trials_api.py search \
  --condition "diabetes" \
  --advanced "AREA[LocationCountry]United States \
    AND AREA[EnrollmentCount]RANGE[500, MAX]" \
  --fields "NCTId,BriefTitle,EnrollmentCount" \
  --output /tmp/diabetes_us_large.json

References

  • API parameters, enum values, and Essie syntax: references/clinical_trials_api.md
  • JSON field paths and --fields recipes: references/studies_schema.md

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Clinical Trials Database AI skill do?

Query ClinicalTrials.gov via APIv2. Use when you want to search for trials by condition, drug, location, status, or phase; retrieve trial details by NCT ID; check eligibility/inclusion criteria; count trials across conditions or time periods; identify a sponsor's trial portfolio; find recruiting trials for patient matching.

Why use Clinical Trials Database on TypingMind?

Because you install it once and use it with any model. Clinical Trials Database is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Clinical Trials Database in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/google-deepmind/science-skills/tree/main/skills/clinical_trials_database. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Clinical Trials Database?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Clinical Trials Database?

As many as you like. As long as a model supports skills, you can use Clinical Trials Database with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Clinical Trials Database AI skill free?

Yes. It is published on GitHub by google-deepmind under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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