skills/skill-collections/SciGraph-SCP-Skills/scp-yasascore/SKILL.md
Use when you need to connect to the SciGraph SCP server for YaSAScore (reaction knowledge graph for compound synthesis accessibility; USPTO/Pistachio reactions as directed reactant→product edges) 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-yasascoreInstall 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.
YaSAScore Reaction Knowledge Graph is built from chemical reaction data to support prediction of compound synthesis accessibility (SA). It integrates reactions from USPTO and Pistachio into a directed molecular network: compounds are nodes and reactions define directed edges from reactants to products.
The graph supports shortest-path-based estimation of synthetic complexity and is used as a foundation for training/benchmarking ML models such as CMPNN and SYBA-2.
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 (YaSAScore):
{
"cypher": "MATCH (e:Experiment:YaSAScore) RETURN e.id as experiment_id",
"kg_name": "YaSAScore",
"limit": 5
}
Return graph statistics.
Example arguments:
{ "kg_name": "YaSAScore" }
Return entity details.
Example arguments:
{ "entity_identifier": "experiment_1", "kg_name": "YaSAScore" }
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 YaSAScore
result = await session.call_tool(
"get_kg_statistics",
arguments={"kg_name": "YaSAScore"},
)
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())
Li, B., & Chen, H. (2022). Prediction of compound synthesis accessibility based on reaction knowledge graph. Molecules, 27(3), 1039. https://doi.org/10.3390/molecules27031039
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"