Agent skill

Fda Database

by aipoch in aipoch/medical-research-skills

Query the openFDA API to retrieve FDA regulatory datasets (drugs, devices, adverse events, recalls, submissions, UNII) when you need programmatic safety/regulatory evidence for analysis or research.

MITAuto-check passedBackend & APIs

Install Fda Database

skills CLI

$ npx skills add aipoch/medical-research-skills --skill fda-database -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install aipoch/medical-research-skills fda-database --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/aipoch/medical-research-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/'scientific-skills/Evidence Insight/fda-database' .claude/skills/fda-database && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "fda-database" agent skill from https://github.com/aipoch/medical-research-skills/tree/main/scientific-skills/Evidence%20Insight/fda-database into .claude/skills/fda-database/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fda-database", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/aipoch/medical-research-skills/tree/main/scientific-skills/Evidence%20Insight/fda-database

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add aipoch/medical-research-skills --skill fda-database -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install aipoch/medical-research-skills fda-database --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/aipoch/medical-research-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/'scientific-skills/Evidence Insight/fda-database' .agents/skills/fda-database && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "fda-database" agent skill from https://github.com/aipoch/medical-research-skills/tree/main/scientific-skills/Evidence%20Insight/fda-database into .agents/skills/fda-database/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fda-database", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add aipoch/medical-research-skills --skill fda-database -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install aipoch/medical-research-skills fda-database --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/aipoch/medical-research-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/'scientific-skills/Evidence Insight/fda-database' .cursor/skills/fda-database && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "fda-database" agent skill from https://github.com/aipoch/medical-research-skills/tree/main/scientific-skills/Evidence%20Insight/fda-database into .cursor/skills/fda-database/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fda-database", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/aipoch/medical-research-skills.git --path 'scientific-skills/Evidence Insight/fda-database'

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add aipoch/medical-research-skills --skill fda-database -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install aipoch/medical-research-skills fda-database --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/aipoch/medical-research-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/'scientific-skills/Evidence Insight/fda-database' .gemini/skills/fda-database && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "fda-database" agent skill from https://github.com/aipoch/medical-research-skills/tree/main/scientific-skills/Evidence%20Insight/fda-database into .gemini/skills/fda-database/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fda-database", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install aipoch/medical-research-skills fda-database

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add aipoch/medical-research-skills --skill fda-database -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/aipoch/medical-research-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/'scientific-skills/Evidence Insight/fda-database' .github/skills/fda-database && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "fda-database" agent skill from https://github.com/aipoch/medical-research-skills/tree/main/scientific-skills/Evidence%20Insight/fda-database into .github/skills/fda-database/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fda-database", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add aipoch/medical-research-skills --skill fda-database -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install aipoch/medical-research-skills fda-database --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/aipoch/medical-research-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/'scientific-skills/Evidence Insight/fda-database' .opencode/skills/fda-database && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "fda-database" agent skill from https://github.com/aipoch/medical-research-skills/tree/main/scientific-skills/Evidence%20Insight/fda-database into .opencode/skills/fda-database/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fda-database", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: fda-database
GitHub stars: 2k
Token cost: ~2k tokens
SKILL.md length: 459 words
Files: 22 (incl. scripts, references)
Skills in repo: 567
Repo updated: Sep 17, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Query the openFDA API to retrieve FDA regulatory datasets (drugs, devices, adverse events, recalls, submissions, UNII) when you need programmatic safety/regulatory evidence for analysis or research.

Works in 3 steps: Set an API key (optional, recommended) → Run a complete script → Run the repository examples (if provided)
Backend & APIs work in your project
SKILL.md covers When to Use, Key Features, Dependencies and Example Usage, plus 1 more section
Calls python; needs FDA_API_KEY

What it does

Fda Database is an agent skill from aipoch/medical-research-skills. Query the openFDA API to retrieve FDA regulatory datasets (drugs, devices, adverse events, recalls, submissions, UNII) when you need programmatic safety/regulatory evidence for analysis or research.

Its SKILL.md is about 2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 23 other files, including scripts and reference files (for example `fda-database_audit_result_v1.json`, `fda_cache/1cf8286b186012b387cd775ea2e0cc6d.json` and `fda_cache/5c7c1e6be77f7a9025bf3c1cc4a027e2.json`).

It sits in Backend & APIs. The repository describes itself as: Hundreds of agent skills for medical research, including protocol design, data analysis, evidence insights, and academic writing. The licence is MIT.

When your agent uses it

Backend & APIs work in your project

Example prompts

“/fda-database”

Requirements

Python 3
A credential in FDA_API_KEY

Workflow steps

3 steps, taken from the step headings in SKILL.md.

Set an API key (optional, recommended)
Run a complete script
Run the repository examples (if provided)

What it can do on your machine

Read from SKILL.md and the folder at commit 686e09d. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 1 file in scripts/, which the agent can run.
Shell commands in SKILL.md call:
- python
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names these keys or tokens, usually read from environment variables:
- FDA_API_KEY
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Fda Database loads about 2k tokens when it runs, and up to ~24k if it reads all its reference files. Until then it costs about 53 tokens; SKILL.md has 459 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~53
When it runs · the whole SKILL.md, loaded when a task matches: ~2k
With references · SKILL.md plus every file in references/, read only if the agent opens them: ~24k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from aipoch/medical-research-skills at commit 686e09d, republished under its MIT licence (© aipoch). 459 words, ~1,964 tokens.

Download SKILL.mdSave it as .claude/skills/fda-database/SKILL.md (or your agent's skills folder). This skill also uses 21 other files; get the full folder from GitHub.

name: fda-database
description: Query the openFDA API to retrieve FDA regulatory datasets (drugs, devices, adverse events, recalls, submissions, UNII) when you need programmatic safety/regulatory evidence for analysis or research.
license: MIT
author: AIPOCH

Source: https://github.com/aipoch/medical-research-skills

When to Use

Pharmacovigilance / safety signal screening when you need adverse event counts, common reactions, or serious-event rates for a drug.
Medical device regulatory research when you need 510(k)/PMA context, device classification, UDI lookups, or device adverse events/recalls.
Recall and enforcement monitoring when you need to track Class I/II/III recalls across drugs, devices, or foods.
Substance identity resolution when you need UNII/CAS/name-based lookups and basic substance relationship/structure retrieval.
Veterinary safety analysis when you need animal adverse events filtered by species/breed and product.

Key Features

Unified Python interface (FDAQuery) for multiple openFDA domains (drug, device, food, animalandveterinary, other).
Convenience helpers for common tasks:
- Drug events, labels, recalls, shortages
- Device events, classification, 510(k), PMA, UDI
- Food events and recalls
- Animal/veterinary adverse events
- Substance (UNII/name) lookups
Supports openFDA query patterns:
- Fielded search strings, date ranges, wildcards
- Aggregations via count_by_field(...) (with .exact support)
- Pagination via skip/limit and bulk retrieval via query_all(...)
Operational safeguards:
- Optional API key support for higher daily limits
- Built-in caching (TTL) and rate limiting (as implemented in scripts/fda_query.py)
- Basic error handling patterns

Additional endpoint notes and query syntax are typically documented in: references/api_basics.md, references/drugs.md, references/devices.md, references/foods.md, references/animal_veterinary.md, references/other.md.

Dependencies

Python 3.9+
openFDA API access (public)
Optional: openFDA API key (recommended for higher daily quota)

Package-level dependencies (e.g., requests) are defined by the repository implementation in scripts/fda_query.py. If you maintain this skill, pin them in requirements.txt (for example, requests==2.31.0) to ensure reproducibility.

Example Usage

The following example is designed to be runnable in a repository that contains scripts/fda_query.py and the FDAQuery class.

1) Set an API key (optional, recommended)

bash

export FDA_API_KEY="your_key_here"

2) Run a complete script

python

import os
from datetime import datetime, timedelta

from scripts.fda_query import FDAQuery


def drug_safety_profile(fda: FDAQuery, drug_name: str):
    # Total adverse events (meta.total)
    events = fda.query_drug_events(drug_name, limit=1)
    total = events.get("meta", {}).get("results", {}).get("total", 0)

    # Top reactions (aggregation)
    reactions = fda.count_by_field(
        "drug",
        "event",
        search=f"patient.drug.medicinalproduct:*{drug_name}*",
        field="patient.reaction.reactionmeddrapt",
        exact=True,
    )
    top_reactions = reactions.get("results", [])[:10]

    # Serious events
    serious = fda.query(
        "drug",
        "event",
        search=f"patient.drug.medicinalproduct:*{drug_name}*+AND+serious:1",
        limit=1,
    )
    serious_total = serious.get("meta", {}).get("results", {}).get("total", 0)

    # Recent recalls
    recalls = fda.query_drug_recalls(drug_name=drug_name)
    recall_results = recalls.get("results", [])

    return {
        "drug": drug_name,
        "total_events": total,
        "serious_events": serious_total,
        "serious_rate_pct": (serious_total / total * 100.0) if total else 0.0,
        "top_reactions": top_reactions,
        "recalls_sample": recall_results[:5],
    }


def monthly_event_trend(fda: FDAQuery, drug_name: str, months: int = 6):
    trends = []
    for i in range(months):
        end = datetime.now() - timedelta(days=30 * i)
        start = end - timedelta(days=30)
        date_range = f"[{start.strftime('%Y%m%d')}+TO+{end.strftime('%Y%m%d')}]"

        search = (
            f"patient.drug.medicinalproduct:*{drug_name}*"
            f"+AND+receivedate:{date_range}"
        )
        result = fda.query("drug", "event", search=search, limit=1)
        count = result.get("meta", {}).get("results", {}).get("total", 0)

        trends.append({"month": start.strftime("%Y-%m"), "events": count})

    return list(reversed(trends))


def main():
    fda = FDAQuery(api_key=os.getenv("FDA_API_KEY"))

    # Drug: safety profile + trend
    profile = drug_safety_profile(fda, "aspirin")
    trend = monthly_event_trend(fda, "aspirin", months=6)

    # Device: quick cross-database lookup
    device_lookup = {
        "adverse_events": fda.query_device_events("pacemaker", limit=10),
        "classification": fda.query_device_classification("DQY"),
        "510k": fda.query_device_510k(applicant="Medtronic"),
        "udi": fda.query("device", "udi", search="brand_name:*pacemaker*", limit=5),
    }

    # Food: recall monitoring
    food_recalls = fda.query_food_recalls(reason="undeclared peanut", limit=10)

    # Substance: UNII lookup
    substance = fda.query_substance_by_unii("R16CO5Y76E")

    print({"drug_profile": profile, "drug_trend": trend})
    print({"device_lookup_keys": list(device_lookup.keys())})
    print({"food_recalls_count": len(food_recalls.get("results", []))})
    print({"substance_keys": list(substance.keys())})


if __name__ == "__main__":
    main()

3) Run the repository examples (if provided)

bash

python scripts/fda_examples.py

Show full SKILL.md (183 more words)Show less

Implementation Details

API domains and endpoints

This skill is a thin client over openFDA endpoints, typically accessed as:

Drugs: drug/event, drug/label, drug/ndc, drug/enforcement, drug/drugsfda, drug/drugshortages
Devices: device/event, device/510k, device/classification, device/enforcement, device/recall, device/pma, device/registrationlisting, device/udi, device/covid19serology
Foods: food/event, food/enforcement
Animal/Veterinary: animalandveterinary/event
Other/Substances: other/substance, other/nsde

Exact helper method names (e.g., query_drug_events, query_device_510k) are implemented in scripts/fda_query.py.

Query construction

Searches are passed as openFDA query strings (Lucene-like), e.g.:
- Field match: patient.drug.medicinalproduct:aspirin
- Wildcards: *aspirin* (use sparingly)
- Boolean: A+AND+B
- Date range: receivedate:[20240101+TO+20241231]
Pagination uses:
- limit (page size)
- skip (offset)
Aggregations use count_by_field(domain, endpoint, search, field, exact=True):
- When exact=True, the implementation typically appends .exact to the aggregation field to avoid tokenization issues.

Rate limits and authentication

openFDA supports unauthenticated access with lower daily quotas; an API key increases the daily request limit.
The client is expected to:
- Attach the API key when provided
- Apply rate limiting and retries (per FDAQuery implementation)

Result handling and robustness

Responses generally follow:

json

{
  "meta": { "results": { "skip": 0, "limit": 100, "total": 12345 } },
  "results": []
}

Always guard for:
- Missing results
- Empty result sets
- error objects returned by the API

Caching

If enabled in FDAQuery, caching reduces repeated calls for identical queries.
Typical parameters (implementation-dependent):
- use_cache=True
- cache_ttl=<seconds>

Files

SKILL.md and 21 other files (scripts, references) in scientific-skills/Evidence Insight/fda-database of aipoch/medical-research-skills.

SKILL.md
fda-database_audit_result_v1.json
fda_cache/1cf8286b186012b387cd775ea2e0cc6d.json
fda_cache/5c7c1e6be77f7a9025bf3c1cc4a027e2.json
fda_cache/6f0b440698c62cc6b2d485cfb639cf3c.json
fda_cache/78a895fc30a8f656ac9cae080c9aa6be.json
fda_cache/859253e4be22288a2b4be7028ee5481a.json
fda_cache/8e21e949fd31613c165c2f1f7c67b26c.json
fda_cache/95ad9e9bdfc1787b38c0bbd0873c7d69.json
fda_cache/abc08fa551365fd6037fbf1613cccd7e.json
fda_cache/d9ad69e7135faccb5ed17764b6255d0d.json
fda_cache/eccae2c6594ad6cea5a198a4dcc0b68c.json
fda_cache/fa6d3bcf5c52ce1c6631e69d6f73b47f.json
references/animal_veterinary.md
references/api_basics.md
references/devices.md
references/drugs.md
references/foods.md
references/other.md
… and 3 more

Open the folder on GitHubat commit 686e09d

Compare with similar skills

Fda Database next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Questions about Fda Database

What does Fda Database do?

Query the openFDA API to retrieve FDA regulatory datasets (drugs, devices, adverse events, recalls, submissions, UNII) when you need programmatic safety/regulatory evidence for analysis or research. Fda Database is an agent skill from aipoch/medical-research-skills. Query the openFDA API to retrieve FDA regulatory datasets (drugs, devices, adverse events, recalls, submissions, UNII) when you need programmatic safety/regulatory evidence for analysis or research.

When should I use Fda Database?

Fda Database fits situations like: backend & APIs work in your project.

How do I install Fda Database in Claude Code?

Run `npx skills add aipoch/medical-research-skills --skill fda-database -a claude-code`. Or copy the skill folder (scientific-skills/Evidence Insight/fda-database in aipoch/medical-research-skills) into .claude/skills/fda-database in your project. Claude Code loads it when a task matches its description.

How do I install Fda Database in Codex?

Run `npx skills add aipoch/medical-research-skills --skill fda-database -a codex`. Or copy the skill folder (scientific-skills/Evidence Insight/fda-database in aipoch/medical-research-skills) into .agents/skills/fda-database in your project. Codex loads it when a task matches its description.

Can I use Fda Database in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add aipoch/medical-research-skills --skill fda-database -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/fda-database, .gemini/skills/fda-database, .github/skills/fda-database and .opencode/skills/fda-database in your project.

What does Fda Database need to run?

Going by SKILL.md and its folder, Fda Database needs the command-line tools its instructions call (python) and credentials named FDA_API_KEY. Our summary lists: Python 3; A credential in FDA_API_KEY.

Does Fda Database access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Fda Database safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Fda Database use?

Fda Database is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Fda Database use?

About 2k tokens (SKILL.md is roughly 7.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 23k tokens, read only when the agent opens those files.

What are the alternatives to Fda Database?

Skills that share tags, products or a category with Fda Database: Configuring Horizon (coollabsio/coolify, 63k stars), Nestjs Best Practices (rolling-scopes/rsschool-app, 10k stars), Sub2API Admin (Wei-Shaw/sub2api, 43k stars) and Firecrawl Build Onboarding (firecrawl/firecrawl, 190k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Fda Database?

aipoch (a GitHub organization) maintains it in aipoch/medical-research-skills, which has 1,974 GitHub stars. The repository holds 567 skills in this directory. The repository was last updated on September 17, 2026.

Source: aipoch/medical-research-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Fda Database

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

When to Use

Key Features

Dependencies

Example Usage

1) Set an API key (optional, recommended)

2) Run a complete script

3) Run the repository examples (if provided)

Implementation Details

API domains and endpoints

Query construction

Rate limits and authentication

Result handling and robustness

Caching

Files

Compare with similar skills

Similar skills

More from aipoch/medical-research-skills

Related

Questions about Fda Database