skills/skill-collections/SciGraph-SCP-Skills/scp-marineexpert/SKILL.md
Use when you need to connect to the SciGraph SCP server for MarineExpert (marine expert management knowledge graph integrating experts/publications/institutions/collaborations/fields from multi-source marine info) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
npx skillsauth add zjunlp/Skills scp-marineexpertInstall 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.
MarineExpert is a domain-specific knowledge graph for marine expert management. It integrates fragmented marine-science expert information from multiple heterogeneous sources (official marine information websites, Baidu Encyclopedia, public reports, social media, scholarly literature) into structured knowledge.
It supports efficient management, discovery, and visualization of marine experts, their publications, research institutions, collaborations, and research fields, enabling marine think-tank and talent-management use cases.
https://scp.intern-ai.org.cn/api/v1/mcp/37/SciGraphSCP-HUB-API-KEY: {API-KEY}pip install mcp
{
"mcpServers": {
"SciGraph": {
"type": "streamableHttp",
"description": "这是一款面向科学研究的统一知识查询服务,集成了化学、生物等多个学科领域的知识图谱数据,支持跨学科知识检索、实体关系查询、领域知识问答等操作",
"url": "https://scp.intern-ai.org.cn/api/v1/mcp/37/SciGraph",
"headers": {
"SCP-HUB-API-KEY": "{API-KEY}"
}
}
}
}
Execute a Cypher query and return JSON results.
Arguments:
cypher (string, required)kg_name (string|null, optional, default null)limit (int, optional, default 100)Example arguments (MarineExpert):
{
"cypher": "MATCH (e:Experiment:MarineExpert) RETURN e.id as experiment_id",
"kg_name": "MarineExpert",
"limit": 5
}
Return graph statistics.
Example arguments:
{ "kg_name": "MarineExpert" }
Return entity details.
Example arguments:
{ "entity_identifier": "experiment_1", "kg_name": "MarineExpert" }
Return the full workflow of an experiment.
Example arguments:
{ "experiment_id": "experiment_1" }
import asyncio
import json
from mcp.client.streamable_http import streamablehttp_client
from mcp.client.session import ClientSession
SERVER_URL = "https://scp.intern-ai.org.cn/api/v1/mcp/37/SciGraph"
async def main():
transport = streamablehttp_client(
url=SERVER_URL,
headers={"SCP-HUB-API-KEY": "sk-xxx"},
)
read, write, get_session_id = await transport.__aenter__()
session_ctx = ClientSession(read, write)
session = await session_ctx.__aenter__()
await session.initialize()
# Example: stats for MarineExpert
result = await session.call_tool(
"get_kg_statistics",
arguments={"kg_name": "MarineExpert"},
)
data = json.loads(result.content[0].text)
print(data)
await session_ctx.__aexit__(None, None, None)
await transport.__aexit__(None, None, None)
if __name__ == "__main__":
asyncio.run(main())
For the full scraped page text, read:
references/source.mddevelopment
Expert AWS solution architecture for startups focusing on serverless, scalable, and cost-effective cloud infrastructure with modern DevOps practices and infrastructure-as-code
tools
AWS development with infrastructure automation and cloud architecture patterns
development
Specialized skill for building production-ready serverless applications on AWS. Covers Lambda functions, API Gateway, DynamoDB, SQS/SNS event-driven patterns, SAM/CDK deployment, and cold start optimization.
development
Terraform infrastructure specialist for Vertex AI ADK Agent Engine production deployments. Provisions Agent Engine runtime, Code Execution Sandbox, Memory Bank, VPC-SC, IAM, and secure multi-agent infrastructure. Triggers: "deploy adk terraform", "agent engine infrastructure", "adk production deployment", "vpc-sc agent engine"