.claude/skills/pediatric_drug_safety/SKILL.md
Pediatric Drug Safety Review - Evaluate pediatric drug safety: pediatric use info, child safety, dosage forms, and overdosage info from FDA. Use this skill for pediatric pharmacology tasks involving get pediatric use info by drug name get child safety info by drug name get dosage forms and strengths by drug name get overdosage info by drug name. Combines 4 tools from 1 SCP server(s).
npx skillsauth add SpectrAI-Initiative/InnoClaw pediatric_drug_safetyInstall 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.
Discipline: Pediatric Pharmacology | Tools Used: 4 | Servers: 1
Evaluate pediatric drug safety: pediatric use info, child safety, dosage forms, and overdosage info from FDA.
get_pediatric_use_info_by_drug_name from fda-drug-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrugget_child_safety_info_by_drug_name from fda-drug-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrugget_dosage_forms_and_strengths_by_drug_name from fda-drug-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrugget_overdosage_info_by_drug_name from fda-drug-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug{
"drug_name": "amoxicillin"
}
Note: Replace
sk-b04409a1-b32b-4511-9aeb-22980abdc05cwith your own SCP Hub API Key. You can obtain one from the SCP Platform.
import asyncio
import json
from contextlib import AsyncExitStack
from mcp import ClientSession
from mcp.client.streamable_http import streamablehttp_client
from mcp.client.sse import sse_client
SERVERS = {
"fda-drug-server": "https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug"
}
async def connect(url, stack):
transport = streamablehttp_client(url=url, headers={"SCP-HUB-API-KEY": "sk-b04409a1-b32b-4511-9aeb-22980abdc05c"})
read, write, _ = await stack.enter_async_context(transport)
ctx = ClientSession(read, write)
session = await stack.enter_async_context(ctx)
await session.initialize()
return session
def parse(result):
try:
if hasattr(result, 'content') and result.content:
c = result.content[0]
if hasattr(c, 'text'):
try: return json.loads(c.text)
except: return c.text
return str(result)
except: return str(result)
async def main():
async with AsyncExitStack() as stack:
# Connect to required servers
sessions = {}
sessions["fda-drug-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug", stack)
# Execute workflow steps
# Step 1: Get pediatric use info
result_1 = await sessions["fda-drug-server"].call_tool("get_pediatric_use_info_by_drug_name", arguments={})
data_1 = parse(result_1)
print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}")
# Step 2: Get child safety info
result_2 = await sessions["fda-drug-server"].call_tool("get_child_safety_info_by_drug_name", arguments={})
data_2 = parse(result_2)
print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")
# Step 3: Get dosage forms
result_3 = await sessions["fda-drug-server"].call_tool("get_dosage_forms_and_strengths_by_drug_name", arguments={})
data_3 = parse(result_3)
print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")
# Step 4: Get overdosage info
result_4 = await sessions["fda-drug-server"].call_tool("get_overdosage_info_by_drug_name", arguments={})
data_4 = parse(result_4)
print(f"Step 4 result: {json.dumps(data_4, indent=2, ensure_ascii=False)[:500]}")
# Cleanup
print("Workflow complete!")
if __name__ == "__main__":
asyncio.run(main())
tools
Use the local InnoClaw CLI to run app workflows and Deep Research sessions from the terminal. Trigger when the user wants command-line control over this repository instead of only using the web UI.
tools
SNP Functional Impact Analysis - Analyze SNP function: VEP prediction, variation details, phenotype association, and literature evidence. Use this skill for functional genomics tasks involving get vep id get variation get phenotype accession pubmed search. Combines 4 tools from 2 SCP server(s).
tools
SMILES Comprehensive Analysis - Comprehensive SMILES analysis: validate, convert name, compute all molecular descriptors, and predict ADMET. Use this skill for cheminformatics tasks involving is valid smiles ChemicalStructureAnalyzer calculate mol basic info pred molecule admet. Combines 4 tools from 3 SCP server(s).
tools
Convert SMILES strings to CAS registry numbers using material informatics tools to identify chemical substances.