Expert knowledge for Azure Energy Data Services development including troubleshooting, decision making, architecture & design patterns, security, configuration, integrations & coding patterns, and deployment. Use when configuring ADME metrics/partitioning, choosing tiers, securing auth/ACLs, deploying AKS geospatial, or fixing ingestion logs, and other Azure Energy Data Services related development tasks. Not for Azure Data Explorer (use azure-data-explorer), Azure Synapse Analytics (use azure-synapse-analytics), Azure Data Factory (use azure-data-factory), Azure Databricks (use azure-databricks).
npx skills add https://github.com/MicrosoftDocs/Agent-Skills --skill azure-energy-data-services
This skill provides expert guidance for Azure Energy Data Services. Covers troubleshooting, decision making, architecture & design patterns, security, configuration, integrations & coding patterns, and deployment. It combines local quick-reference content with remote documentation fetching capabilities.
> IMPORTANT for Agent: Use the Category Index below to locate relevant sections. For categories with line ranges (e.g., L35-L120), use read_file with the specified lines. For categories with file links (e.g., security.md), use read_file on the linked reference file
> IMPORTANT for Agent: If metadata.generated_at is more than 3 months old, suggest the user pull the latest version from the repository. If mcp_microsoftdocs tools are not available, suggest the user install it: Installation Guide
This skill requires network access to fetch documentation content:
mcp_microsoftdocs:microsoft_docs_fetch with query string from=learn-agent-skill. Returns Markdown.fetch_webpage with query string from=learn-agent-skill&accept=text/markdown. Returns Markdown.| Category | Lines | Description |
|----------|-------|-------------|
| Troubleshooting | L35-L39 | Diagnosing and fixing manifest ingestion failures in Azure Data Manager for Energy using Airflow logs, including log analysis steps and common error patterns. |
| Decision Making | L40-L45 | Guidance on choosing ADME deployment tiers (Developer vs Standard) and checking which OSDU data/compute services and capabilities are available in each tier. |
| Architecture & Design Patterns | L46-L50 | Guidance on architecting resilient ADME deployments in Azure Energy Data Services, including zone redundancy, disaster recovery strategies, and high-availability design patterns. |
| Security | L51-L65 | Securing Azure Data Manager for Energy: auth tokens, ACLs, encryption, legal tags, user/group entitlements, managed identities, private endpoints, and API Management access control. |
| Configuration | L66-L74 | Configuring ADME operations: monitoring metrics, data partitioning, CORS, audit logging, and milestone upgrade settings for secure, scalable data management. |
| Integrations & Coding Patterns | L75-L95 | Patterns and examples for integrating Azure Energy Data Services with analytics platforms, external data sources, DDMS APIs, logs/monitoring, and large file workflows. |
| Deployment | L96-L99 | Guides for deploying Azure Energy Data Services components, including Geospatial Consumption Zone on AKS and the OSDU Admin UI for Azure Data Manager for Energy administration |
| Topic | URL |
|-------|-----|
| Troubleshoot manifest ingestion in Azure Data Manager for Energy using Airflow logs | https://learn.microsoft.com/en-us/azure/energy-data-services/troubleshoot-manifest-ingestion |
| Topic | URL |
|-------|-----|
| Choose between Developer and Standard ADME tiers | https://learn.microsoft.com/en-us/azure/energy-data-services/concepts-tier-details |
| Determine which OSDU services are available on ADME | https://learn.microsoft.com/en-us/azure/energy-data-services/osdu-services-on-adme |
| Topic | URL |
|-------|-----|
| Design resilient ADME deployments with zones and DR | https://learn.microsoft.com/en-us/azure/energy-data-services/reliability-energy-data-services |
| Topic | URL |
|-------|-----|
| Use Customer Lockbox to control ADME support access | https://learn.microsoft.com/en-us/azure/energy-data-services/how-to-create-lockbox |
| Enable Analytics Consumption Zone with managed identity and storage | https://learn.microsoft.com/en-us/azure/energy-data-services/how-to-enable-analytics-consumption-zone |
| Enable legal tags for restricted origin data in Azure Energy | https://learn.microsoft.com/en-us/azure/energy-data-services/how-to-enable-legal-tags-restricted-country-of-origin |
| Generate auth and refresh tokens for Azure Data Manager for Energy | https://learn.microsoft.com/en-us/azure/energy-data-services/how-to-generate-auth-token |
| Configure and update ACLs on ADME data records | https://learn.microsoft.com/en-us/azure/energy-data-services/how-to-manage-acls |
| Configure data security and encryption for Azure Data Manager for Energy | https://learn.microsoft.com/en-us/azure/energy-data-services/how-to-manage-data-security-and-encryption |
| Create and manage legal tags for ADME data | https://learn.microsoft.com/en-us/azure/energy-data-services/how-to-manage-legal-tags |
| Manage users and OSDU group entitlements in ADME | https://learn.microsoft.com/en-us/azure/energy-data-services/how-to-manage-users |
| Secure Azure Data Manager for Energy APIs with API Management | https://learn.microsoft.com/en-us/azure/energy-data-services/how-to-secure-apis |
| Create private endpoints for ADME with Azure Private Link | https://learn.microsoft.com/en-us/azure/energy-data-services/how-to-set-up-private-links |
| Configure managed identities for Azure Data Manager for Energy | https://learn.microsoft.com/en-us/azure/energy-data-services/how-to-use-managed-identity |
| Topic | URL |
|-------|-----|
| Reference monitoring metrics for Data Manager for Energy | https://learn.microsoft.com/en-us/azure/energy-data-services/concepts-monitor-data-reference |
| Add and manage data partitions in ADME | https://learn.microsoft.com/en-us/azure/energy-data-services/how-to-add-more-data-partitions |
| Configure CORS policies for Azure Data Manager for Energy | https://learn.microsoft.com/en-us/azure/energy-data-services/how-to-enable-cors |
| Configure and use audit logs in Azure Data Manager for Energy | https://learn.microsoft.com/en-us/azure/energy-data-services/how-to-manage-audit-logs |
| Configure milestone upgrade settings for Azure Data Manager for Energy | https://learn.microsoft.com/en-us/azure/energy-data-services/how-to-manage-upgrade-settings |
| Topic | URL |
|-------|-----|
| Integrate Azure Energy ACZ with Azure Databricks | https://learn.microsoft.com/en-us/azure/energy-data-services/how-to-connect-analytics-consumption-zone-to-databricks |
| Connect Azure Energy ACZ data to Microsoft Fabric | https://learn.microsoft.com/en-us/azure/energy-data-services/how-to-connect-analytics-consumption-zone-to-fabric |
| Enable External Data Services and Key Vault access | https://learn.microsoft.com/en-us/azure/energy-data-services/how-to-enable-external-data-services |
| Integrate Airflow task logs from ADME with Azure Monitor | https://learn.microsoft.com/en-us/azure/energy-data-services/how-to-integrate-airflow-logs-with-azure-monitor |
| Send ADME Elasticsearch logs to Azure Monitor | https://learn.microsoft.com/en-us/azure/energy-data-services/how-to-integrate-elastic-logs-with-azure-monitor |
| Export OSDU service logs from ADME to Azure Monitor | https://learn.microsoft.com/en-us/azure/energy-data-services/how-to-integrate-osdu-service-logs-with-azure-monitor |
| Register external data sources with ADME EDS | https://learn.microsoft.com/en-us/azure/energy-data-services/how-to-register-external-data-services |
| Upload large files via Azure Data Manager for Energy File service API | https://learn.microsoft.com/en-us/azure/energy-data-services/how-to-upload-large-files-using-file-service |
| Call Analytics Consumption Zone management APIs with cURL | https://learn.microsoft.com/en-us/azure/energy-data-services/tutorial-analytics-consumption-zone-apis |
| Work with Petrel data via Petrel DDMS APIs | https://learn.microsoft.com/en-us/azure/energy-data-services/tutorial-petrel-ddms |
| Read reservoir data using Reservoir DDMS REST APIs | https://learn.microsoft.com/en-us/azure/energy-data-services/tutorial-reservoir-ddms-apis |
| Use Reservoir DDMS websocket endpoints for data | https://learn.microsoft.com/en-us/azure/energy-data-services/tutorial-reservoir-ddms-websocket |
| Call RAFS DDMS APIs for rock and fluid samples | https://learn.microsoft.com/en-us/azure/energy-data-services/tutorial-rock-and-fluid-samples-ddms |
| Call Seismic DDMS APIs with cURL | https://learn.microsoft.com/en-us/azure/energy-data-services/tutorial-seismic-ddms |
| Use sdutil CLI to interact with Seismic Store | https://learn.microsoft.com/en-us/azure/energy-data-services/tutorial-seismic-ddms-sdutil |
| Manage well records with Well Delivery DDMS APIs | https://learn.microsoft.com/en-us/azure/energy-data-services/tutorial-well-delivery-ddms |
| Use Wellbore DDMS APIs for well data | https://learn.microsoft.com/en-us/azure/energy-data-services/tutorial-wellbore-ddms |
| Topic | URL |
|-------|-----|
| Deploy Geospatial Consumption Zone on AKS with Azure Data Manager for Energy | https://learn.microsoft.com/en-us/azure/energy-data-services/how-to-deploy-gcz |
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take microsoftdocs/azure-energy-data-services from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.