fda-database

davila7/claude-code-templates · updated Apr 8, 2026

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$npx skills add https://github.com/davila7/claude-code-templates --skill fda-database
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summary

Access comprehensive FDA regulatory data through openFDA, the FDA's initiative to provide open APIs for public datasets. Query information about drugs, medical devices, foods, animal/veterinary products, and substances using Python with standardized interfaces.

skill.md

FDA Database Access

Overview

Access comprehensive FDA regulatory data through openFDA, the FDA's initiative to provide open APIs for public datasets. Query information about drugs, medical devices, foods, animal/veterinary products, and substances using Python with standardized interfaces.

Key capabilities:

  • Query adverse events for drugs, devices, foods, and veterinary products
  • Access product labeling, approvals, and regulatory submissions
  • Monitor recalls and enforcement actions
  • Look up National Drug Codes (NDC) and substance identifiers (UNII)
  • Analyze device classifications and clearances (510k, PMA)
  • Track drug shortages and supply issues
  • Research chemical structures and substance relationships

When to Use This Skill

This skill should be used when working with:

  • Drug research: Safety profiles, adverse events, labeling, approvals, shortages
  • Medical device surveillance: Adverse events, recalls, 510(k) clearances, PMA approvals
  • Food safety: Recalls, allergen tracking, adverse events, dietary supplements
  • Veterinary medicine: Animal drug adverse events by species and breed
  • Chemical/substance data: UNII lookup, CAS number mapping, molecular structures
  • Regulatory analysis: Approval pathways, enforcement actions, compliance tracking
  • Pharmacovigilance: Post-market surveillance, safety signal detection
  • Scientific research: Drug interactions, comparative safety, epidemiological studies

Quick Start

1. Basic Setup

from scripts.fda_query import FDAQuery

# Initialize (API key optional but recommended)
fda = FDAQuery(api_key="YOUR_API_KEY")

# Query drug adverse events
events = fda.query_drug_events("aspirin", limit=100)

# Get drug labeling
label = fda.query_drug_label("Lipitor", brand=True)

# Search device recalls
recalls = fda.query("device", "enforcement",
                   search="classification:Class+I",
                   limit=50)

2. API Key Setup

While the API works without a key, registering provides higher rate limits:

  • Without key: 240 requests/min, 1,000/day
  • With key: 240 requests/min, 120,000/day

Register at: https://open.fda.gov/apis/authentication/

Set as environment variable:

export FDA_API_KEY="your_key_here"

3. Running Examples

# Run comprehensive examples
python scripts/fda_examples.py

# This demonstrates:
# - Drug safety profiles
# - Device surveillance
# - Food recall monitoring
# - Substance lookup
# - Comparative drug analysis
# - Veterinary drug analysis

FDA Database Categories

Drugs

Access 6 drug-related endpoints covering the full drug lifecycle from approval to post-market surveillance.

Endpoints:

  1. Adverse Events - Reports of side effects, errors, and therapeutic failures
  2. Product Labeling - Prescribing information, warnings, indications
  3. NDC Directory - National Drug Code product information
  4. Enforcement Reports - Drug recalls and safety actions
  5. Drugs@FDA - Historical approval data since 1939
  6. Drug Shortages - Current and resolved supply issues

Common use cases:

# Safety signal detection
fda.count_by_field("drug", "event",
                  search="patient.drug.medicinalproduct:metformin",
                  field="patient.reaction.reactionmeddrapt")

# Get prescribing information
label = fda.query_drug_label("Keytruda", brand=True)

# Check for recalls
recalls = fda.query_drug_recalls(drug_name="metformin")

# Monitor shortages
shortages = fda.query("drug", "drugshortages",
                     search="status:Currently+in+Shortage")

Reference: See references/drugs.md for detailed documentation

Devices

Access 9 device-related endpoints covering medical device safety, approvals, and registrations.

Endpoints:

  1. Adverse Events - Device malfunctions, injuries, deaths
  2. 510(k) Clearances - Premarket notifications
  3. Classification - Device categories and risk classes
  4. Enforcement Reports - Device recalls
  5. Recalls - Detailed recall information
  6. PMA - Premarket approval data for Class III devices
  7. Registrations & Listings - Manufacturing facility data
  8. UDI - Unique Device Identification database
  9. COVID-19 Serology - Antibody test performance data

Common use cases:

# Monitor device safety
events = fda.query_device_events("pacemaker", limit=100)

# Look up device classification
classification = fda.query_device_classification("DQY")

# Find 510(k) clearances
clearances = fda.query_device_510k(applicant="Medtronic")

# Search by UDI
device_info = fda.query("device", "udi",
                       search="identifiers.id:00884838003019")

Reference: See references/devices.md for detailed documentation

Foods

Access 2 food-related endpoints for safety monitoring and recalls.

Endpoints:

  1. Adverse Events - Food, dietary supplement, and cosmetic events
  2. Enforcement Reports - Food product recalls

Common use cases:

# Monitor allergen recalls
recalls = fda.query_food_recalls(reason="undeclared peanut")

# Track dietary supplement events
events = fda.query_food_events(
    industry="Dietary Supplements")

# Find contamination recalls
listeria = fda.query_food_recalls(
    reason="listeria",
    classification="I")

Reference: See references/foods.md for detailed documentation

Animal & Veterinary

Access veterinary drug adverse event data with species-specific information.

Endpoint:

  1. Adverse Events - Animal drug side effects by species, breed, and product

Common use cases:

# Species-specific events
dog_events = fda.query_animal_events(
    species="Dog",
    drug_name="flea collar")

# Breed predisposition analysis
breed_query = fda.query("animalandveterinary", "event",
    search="reaction.veddra_term_name:*seizure*+AND+"
           "animal.breed.breed_component:*Labrador*")

Reference: See references/animal_veterinary.md for detailed documentation

Substances & Other

Access molecular-level substance data with UNII codes, chemical structures, and relationships.

Endpoints:

  1. Substance Data - UNII, CAS, chemical structures, relationships
  2. NSDE - Historical substance data (legacy)

Common use cases:

# UNII to CAS mapping
substance = fda.query_substance_by_unii("R16CO5Y76E")

# Search by name
results = fda.query_substance_by_name("acetaminophen")

# Get chemical structure
structure = fda.query("other", "substance",
    search="names.name:ibuprofen+AND+substanceClass:chemical")

Reference: See references/other.md for detailed documentation

Common Query Patterns

Pattern 1: Safety Profile Analysis

Create comprehensive safety profiles combining multiple data sources:

def drug_safety_profile(fda, drug_name):
    """Generate complete safety profile."""

    # 1. Total adverse events
    events = fda.query_drug_events(drug_name, limit=1)
    total = events["meta"]["results"]["total"]

    # 2. Most common reactions
    reactions = fda.count_by_field(
        "drug", "event",
        search=f"patient.drug.medicinalproduct:*{drug_name}*",
        field="patient.reaction.reactionmeddrapt",
        exact=True
    )

    # 3. Serious events
    serious = fda.query("drug", "event",
        search=f"patient.drug.medicinalproduct:*{drug_name}*+AND+serious:1",
        limit=1)

    # 4. Recent recalls
    recalls = fda.query_drug_recalls(drug_name=drug_name)

    return {
        "total_events": total,
        "top_reactions": reactions["results"][:10],
        "serious_events": serious["meta"]["results"]["total"],
        "recalls"
how to use fda-database

How to use fda-database on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add fda-database
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/davila7/claude-code-templates --skill fda-database

The skills CLI fetches fda-database from GitHub repository davila7/claude-code-templates and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/fda-database

Reload or restart Cursor to activate fda-database. Access the skill through slash commands (e.g., /fda-database) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.855 reviews
  • Sofia Thompson· Dec 28, 2024

    Solid pick for teams standardizing on skills: fda-database is focused, and the summary matches what you get after install.

  • William Park· Dec 24, 2024

    Keeps context tight: fda-database is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Ren Mehta· Dec 8, 2024

    We added fda-database from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Nikhil Brown· Dec 4, 2024

    fda-database fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Charlotte Ramirez· Dec 4, 2024

    fda-database has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Amelia Iyer· Nov 23, 2024

    Keeps context tight: fda-database is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Ava Robinson· Nov 19, 2024

    fda-database is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Ava Park· Nov 15, 2024

    fda-database has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Amelia Menon· Oct 14, 2024

    fda-database is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Ava Verma· Oct 10, 2024

    Keeps context tight: fda-database is the kind of skill you can hand to a new teammate without a long onboarding doc.

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