Included skill
Azure Energy Data Services
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).
Source: /skills/azure-energy-data-services/SKILL.md
Azure Energy Data Services Skill
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.
How to Use This Skill
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](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:
- Preferred: Use
mcp_microsoftdocs:microsoft_docs_fetch with query string from=learn-agent-skill. Returns Markdown.
- Fallback: Use
fetch_webpage with query string from=learn-agent-skill&accept=text/markdown. Returns Markdown.
Category Index
| 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 |
Troubleshooting
Decision Making
Architecture & Design Patterns
Security
Configuration
Integrations & Coding Patterns
Deployment