skills/43-wentorai-research-plugins/skills/domains/biomedical/medical-data-api/SKILL.md
Access FDA drug data and WHO global health statistics for research
npx skillsauth add brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research medical-data-apiInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
This skill covers two major open medical data APIs for academic research:
openFDA is the U.S. Food and Drug Administration's public API providing access to drug labeling (SPL), adverse event reports (FAERS), recalls, and NDC directory. The FAERS dataset contains over 722,000 reports for common drugs like aspirin, making it a primary pharmacovigilance resource.
WHO Global Health Observatory (GHO) is the WHO's OData v4 API serving over 2,000 health indicators across 194 member states -- life expectancy, mortality, disease burden, health system coverage, risk factors, and SDG targets. Returns structured JSON with numeric values, confidence intervals, and dimensional breakdowns by country, sex, and year.
Both APIs are free, require no authentication, and return JSON.
openFDA: No authentication required. An optional API key (free, via https://open.fda.gov/apis/authentication/) increases rate limits from 240/min to 120,000/day. Register at https://open.fda.gov/apis/ to obtain a key, then append &api_key=YOUR_KEY to requests.
WHO GHO: No authentication required. No API key needed. All endpoints are publicly accessible with no registration.
Search FDA-approved drug labeling data (Structured Product Labeling). Returns boxed warnings, indications, dosage, contraindications, and adverse reactions text.
GET https://api.fda.gov/drug/label.json| Parameter | Type | Required | Description | |-----------|--------|----------|-----------------------------------------------------| | search | string | No | Search query using openFDA query syntax | | limit | int | No | Number of results (default 1, max 1000) | | skip | int | No | Offset for pagination | | count | string | No | Count unique values of a field |
curl "https://api.fda.gov/drug/label.json?search=aspirin&limit=1"
meta.results.total (26,564 for "aspirin") and results array. Each result contains boxed_warning, indications_and_usage, dosage_and_administration, warnings, adverse_reactions, drug_interactions, and openfda cross-references (brand/generic names, manufacturer, NDC, pharmacologic class).Search the FDA Adverse Event Reporting System. Each record describes a safety report including patient demographics, suspect drugs, reported reactions, and outcomes.
GET https://api.fda.gov/drug/event.json| Parameter | Type | Required | Description |
|-----------|--------|----------|-----------------------------------------------------|
| search | string | No | Query (e.g., patient.drug.openfda.brand_name:"aspirin") |
| limit | int | No | Number of results (default 1, max 1000) |
| skip | int | No | Offset for pagination (max skip+limit = 26,000) |
| count | string | No | Count field values (e.g., patient.reaction.reactionmeddrapt.exact) |
curl 'https://api.fda.gov/drug/event.json?search=patient.drug.openfda.brand_name:"aspirin"&limit=1'
meta.results.total (722,607 for aspirin). Each result: safetyreportid, serious (1=yes, 2=no), primarysourcecountry, receivedate, nested patient with patientsex, reaction array (MedDRA terms + reactionoutcome), and drug array with drugcharacterization (1=suspect, 2=concomitant, 3=interacting), medicinalproduct, openfda cross-references.Retrieve data points for a specific health indicator with country, year, and sex dimensions.
GET https://ghoapi.azureedge.net/api/{IndicatorCode}| Parameter | Type | Required | Description |
|-----------|--------|----------|-------------------------------------------------------|
| $top | int | No | Limit number of records returned |
| $skip | int | No | Skip records for pagination |
| $filter | string | No | OData filter (e.g., SpatialDim eq 'USA' and TimeDim eq 2020) |
| $select | string | No | Select specific fields |
| $orderby | string | No | Sort results |
# Life expectancy at birth (WHOSIS_000001)
curl "https://ghoapi.azureedge.net/api/WHOSIS_000001?\$top=2"
value array. Each record: SpatialDim (ISO country, e.g., "BTN"), ParentLocation (WHO region), TimeDim (year), Dim1 (sex: "SEX_BTSX"/"SEX_MLE"/"SEX_FMLE"), Value ("67.8 [67.1-68.6]"), NumericValue (67.845665), Low/High confidence bounds.List available health indicators with their codes and names.
GET https://ghoapi.azureedge.net/api/Indicatorcurl "https://ghoapi.azureedge.net/api/Indicator?\$top=5"
IndicatorCode (e.g., "Adult_curr_e-cig"), IndicatorName (e.g., "Prevalence of current e-cigarette use among adults (%)"), Language ("EN"). Over 2,000 indicators spanning mortality, morbidity, health systems, and risk factors.openFDA:
skip + limit cannot exceed 26,000 (use search + sort for deeper access)WHO GHO:
Count reactions by MedDRA term to identify safety signals:
import requests
resp = requests.get("https://api.fda.gov/drug/event.json", params={
"search": 'patient.drug.openfda.brand_name:"aspirin"',
"count": "patient.reaction.reactionmeddrapt.exact",
"limit": 10
})
for r in resp.json()["results"]:
print(f" {r['term']}: {r['count']} reports")
Compare health indicators across countries and time periods:
import requests
resp = requests.get("https://ghoapi.azureedge.net/api/WHOSIS_000001", params={
"$filter": "SpatialDim eq 'JPN' and Dim1 eq 'SEX_BTSX'",
"$orderby": "TimeDim desc",
"$top": 10
})
for row in resp.json()["value"]:
print(f" Japan {row['TimeDim']}: {row['Value']}")
Compare safety language across drug labels for regulatory research:
import requests
for drug in ["ibuprofen", "naproxen", "celecoxib"]:
resp = requests.get("https://api.fda.gov/drug/label.json",
params={"search": f'openfda.generic_name:"{drug}"', "limit": 1})
results = resp.json().get("results", [])
if results:
warning = results[0].get("boxed_warning", ["None"])[0][:200]
print(f"{drug.upper()}: {warning}...\n")
| Code | Indicator | |------|-----------| | WHOSIS_000001 | Life expectancy at birth | | NCDMORT3070 | NCD mortality (30-70 years) | | MDG_0000000001 | Under-five mortality rate | | WHS4_100 | Physicians per 10,000 population | | NCD_BMI_30A | Prevalence of obesity (BMI >= 30) | | SA_0000001688 | Alcohol per capita consumption | | TOBACCO_0000000262 | Tobacco smoking prevalence |
development
Conduct rigorous thematic analysis (TA) of qualitative data following Braun and Clarke's (2006) six-phase framework. Use whenever the user mentions 'thematic analysis', 'TA', 'Braun and Clarke', 'qualitative coding', 'identifying themes', or asks for help analysing interviews, focus groups, open-ended survey responses, or transcripts to identify patterns. Also trigger for questions about inductive vs theoretical coding, semantic vs latent themes, essentialist vs constructionist epistemology, building a thematic map, or writing up a qualitative findings section. Covers all six phases, the four upfront analytic decisions, the 15-point quality checklist, and the five common pitfalls. Produces a Word document write-up and an annotated thematic map. Does NOT cover IPA, grounded theory, discourse analysis, conversation analysis, or narrative analysis — use a different method for those.
development
Guide users through writing a systematic literature review (SLR) following the PRISMA 2020 framework. Use this skill whenever the user mentions 'systematic review', 'systematic literature review', 'SLR', 'PRISMA', 'PRISMA 2020', 'PRISMA flow diagram', 'PRISMA checklist', or asks for help writing, structuring, or auditing a literature review that follows reporting guidelines. Also trigger when the user asks about inclusion/exclusion criteria for a review, search strategies for databases like Scopus/WoS/PubMed, study selection processes, risk of bias assessment, or narrative synthesis for a review paper. This skill covers the full PRISMA 2020 checklist (27 items), produces a Word document manuscript in strict journal article format, generates an annotated PRISMA flow diagram, and enforces APA 7th Edition referencing throughout. It does NOT cover meta-analysis or statistical pooling. By Chuah Kee Man.
testing
Performs placebo-in-time sensitivity analysis with hierarchical null model and optional Bayesian assurance. Use when checking model robustness, verifying lack of pre-intervention effects, or estimating study power.
data-ai
Fit, summarize, plot, and interpret a chosen CausalPy experiment. Use after the causal method has been selected, including when configuring PyMC/sklearn models and scale-aware custom priors.