skills/codex/application-integration/SKILL.md
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: application-integration description: Google Cloud Application Integration and Integration Connectors for enterprise system connectivity. Use when connecting AI agents to SAP, Oracle, Salesforce, ServiceNow, Snowflake, or other enterprise systems via pre-built GCP connectors. --- # Google Cloud Application Integration Connect AI agents to enterprise systems using Google Cloud's 100+ pre-built Integration Connectors for SAP, Ora
npx skillsauth add frank-luongt/faos-skills-marketplace skills/codex/application-integrationInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Connect AI agents to enterprise systems using Google Cloud's 100+ pre-built Integration Connectors for SAP, Oracle, Salesforce, ServiceNow, Snowflake, and more.
| Category | Connectors | |---|---| | ERP | SAP ERP (RFC, BAPI), SAP S/4HANA (OData), Oracle ERP Cloud | | CRM | Salesforce, HubSpot, Dynamics 365 | | ITSM | ServiceNow | | Databases | Oracle DB, MySQL, PostgreSQL, SQL Server, MongoDB, Cloud SQL, AlloyDB, Spanner | | Data Platforms | Snowflake, BigQuery | | Messaging | Pub/Sub, Kafka, RabbitMQ | | Storage | Cloud Storage, S3, SFTP, FTP | | SaaS | Zendesk, Workday, Slack | | Protocols | REST, SOAP, gRPC, GraphQL |
# Integration connector configuration for SAP S/4HANA
connection:
connector: sap
connection_type: OData
properties:
sap_client: "100"
odata_service_url: "https://sap-s4.example.com/sap/opu/odata/sap/API_SALES_ORDER_SRV"
authentication:
type: basic
username_secret: "projects/my-project/secrets/sap-user/versions/latest"
password_secret: "projects/my-project/secrets/sap-password/versions/latest"
connection:
connector: oracle_db
properties:
host: "oracle-db.example.com"
port: 1521
service_name: "ORCL"
authentication:
type: basic
username_secret: "projects/my-project/secrets/oracle-user/versions/latest"
password_secret: "projects/my-project/secrets/oracle-password/versions/latest"
# Create an OpenAPI spec wrapping Integration Connector for use as Agent Builder tool
openapi_spec = {
"openapi": "3.0.0",
"info": {"title": "SAP Sales Orders", "version": "1.0.0"},
"servers": [{"url": "https://integrations.googleapis.com/v1/projects/MY_PROJECT/locations/us-central1/integrations/sap-sales-orders"}],
"paths": {
"/execute": {
"post": {
"operationId": "getSalesOrders",
"summary": "Get SAP sales orders by customer",
"requestBody": {
"content": {
"application/json": {
"schema": {
"type": "object",
"properties": {
"customer_id": {"type": "string", "description": "SAP customer number"},
"date_from": {"type": "string", "description": "Start date (YYYY-MM-DD)"},
},
}
}
}
},
}
}
},
}
SAP Sales Order Created
--> Cloud Pub/Sub (via connector event trigger)
--> Cloud Function
--> Vertex AI Gemini (analyze order, check for anomalies)
--> ServiceNow (create incident if anomaly detected, via connector)
development
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: databricks-mlflow-evaluation --- # MLflow 3 GenAI Evaluation ## Before Writing Any Code 1. **Read GOTCHAS.md** - 15+ common mistakes that cause failures 2. **Read CRITICAL-interfaces.md** - Exact API signatures and data schemas ## End-to-End Workflows Follow these workflows based on your goal. Each step indicates which reference files to read. ### Workflow 1: First-Time Evaluation Setup For users new to MLflow GenAI evalu
development
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: databricks-lakebase-provisioned --- # Lakebase Provisioned Patterns and best practices for using Lakebase Provisioned (Databricks managed PostgreSQL) for OLTP workloads. ## When to Use Use this skill when: - Building applications that need a PostgreSQL database for transactional workloads - Adding persistent state to Databricks Apps - Implementing reverse ETL from Delta Lake to an operational database - Storing chat/agent m
tools
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: databricks-jobs --- # Databricks Lakeflow Jobs ## Overview Databricks Jobs orchestrate data workflows with multi-task DAGs, flexible triggers, and comprehensive monitoring. Jobs support diverse task types and can be managed via Python SDK, CLI, or Asset Bundles. ## Reference Files | Use Case | Reference File | | ----------------------
development
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: databricks-genie --- # Databricks Genie Create and query Databricks Genie Spaces - natural language interfaces for SQL-based data exploration. ## Overview Genie Spaces allow users to ask natural language questions about structured data in Unity Catalog. The system translates questions into SQL queries, executes them on a SQL warehouse, and presents results conversationally. ## When to Use This Skill Use this skill when: -