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.
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AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionfda-databaseExecute the skills CLI command in your project's root directory to begin installation:
Fetches fda-database from davila7/claude-code-templates and configures it for Cursor.
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Confirm successful installation by checking the skill directory location:
Restart Cursor to activate fda-database. Access via /fda-database in your agent's command palette.
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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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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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:
This skill should be used when working with:
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)
While the API works without a key, registering provides higher rate limits:
Register at: https://open.fda.gov/apis/authentication/
Set as environment variable:
export FDA_API_KEY="your_key_here"
# 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
Access 6 drug-related endpoints covering the full drug lifecycle from approval to post-market surveillance.
Endpoints:
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
Access 9 device-related endpoints covering medical device safety, approvals, and registrations.
Endpoints:
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
Access 2 food-related endpoints for safety monitoring and recalls.
Endpoints:
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
Access veterinary drug adverse event data with species-specific information.
Endpoint:
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
Access molecular-level substance data with UNII codes, chemical structures, and relationships.
Endpoints:
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
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"✓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
Steps
- 1Install product management skill
- 2Start with user story generation for known feature
- 3Progress to competitive analysis: research 2-3 competitors
- 4Use for roadmap prioritization: apply RICE/ICE scoring
- 5Draft stakeholder communications and refine based on feedback
- 6Build template library for recurring PM tasks
- 7Share 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
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
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4.8★★★★★55 reviews- SSofia Thompson★★★★★Dec 28, 2024
Solid pick for teams standardizing on skills: fda-database is focused, and the summary matches what you get after install.
- WWilliam 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.
- RRen Mehta★★★★★Dec 8, 2024
We added fda-database from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- NNikhil Brown★★★★★Dec 4, 2024
fda-database fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- CCharlotte Ramirez★★★★★Dec 4, 2024
fda-database has been reliable in day-to-day use. Documentation quality is above average for community skills.
- AAmelia 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.
- AAva Robinson★★★★★Nov 19, 2024
fda-database is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- AAva Park★★★★★Nov 15, 2024
fda-database has been reliable in day-to-day use. Documentation quality is above average for community skills.
- AAmelia Menon★★★★★Oct 14, 2024
fda-database is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- AAva 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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