Expert knowledge for Microsoft Planetary Computer Pro development including troubleshooting, decision making, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using GeoCatalog/STAC APIs, configuring collections/tiles, securing access, integrating QGIS/ArcGIS, or troubleshooting ingestion, and other Microsoft Planetary Computer Pro related development tasks. Not for Azure Maps (use azure-maps), Azure Open Datasets (use azure-open-datasets), Azure Data Explorer (use azure-data-explorer).
npx skills add https://github.com/MicrosoftDocs/Agent-Skills --skill azure-planetary-computer-pro
This skill provides expert guidance for Microsoft Planetary Computer Pro. Covers troubleshooting, decision making, limits & quotas, 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-L40 | Diagnosing and resolving Planetary Computer Pro GeoCatalog ingestion failures, including error code meanings, common causes, and step-by-step remediation guidance. |
| Decision Making | L41-L45 | Guidance on selecting how to access Planetary Computer Pro data, including connection options, integrations with tools/services, and choosing the best method for your workflow. |
| Limits & Quotas | L46-L50 | Supported file formats, data types, and size/usage limits for datasets and computations in Planetary Computer Pro, including quotas that affect how you process and store data. |
| Security | L51-L62 | Configuring secure access to Planetary Computer Pro/GeoCatalog using Entra ID, RBAC, managed identities, cross-tenant auth, API Management, and SAS tokens for collections. |
| Configuration | L63-L77 | Configuring Planetary Computer Pro collections: visualization, render and tile settings, colormaps, mosaics, queryables, ingestion sources/credentials, and US Gov cloud endpoints. |
| Integrations & Coding Patterns | L78-L91 | Using GeoCatalog/STAC with code and tools: ingest and bulk-load data, build apps, create collections/items, and integrate with QGIS, ArcGIS Pro, Azure Batch, and other geospatial clients. |
| Deployment | L92-L96 | Deploying and safely deleting Planetary Computer GeoCatalog resources, including deployment steps, best practices, and cleanup to avoid data loss or orphaned assets. |
| Topic | URL |
|-------|-----|
| Reference ingestion error codes for Planetary Computer Pro GeoCatalogs | https://learn.microsoft.com/en-us/azure/planetary-computer/error-codes-ingestion |
| Troubleshoot data ingestion issues in Planetary Computer Pro | https://learn.microsoft.com/en-us/azure/planetary-computer/troubleshooting-ingestion |
| Topic | URL |
|-------|-----|
| Choose connection methods and integrations for Planetary Computer Pro data | https://learn.microsoft.com/en-us/azure/planetary-computer/build-applications-with-planetary-computer-pro |
| Topic | URL |
|-------|-----|
| Use supported data types in Planetary Computer Pro | https://learn.microsoft.com/en-us/azure/planetary-computer/supported-data-types |
| Topic | URL |
|-------|-----|
| Configure application authentication to Planetary Computer Pro with Entra ID | https://learn.microsoft.com/en-us/azure/planetary-computer/application-authentication |
| Use managed identities with Planetary Computer GeoCatalog | https://learn.microsoft.com/en-us/azure/planetary-computer/assign-managed-identity-geocatalog-resource |
| Authorize cross-tenant partner applications to access Planetary Computer Pro GeoCatalogs | https://learn.microsoft.com/en-us/azure/planetary-computer/authorize-cross-tenant-partner-applications |
| Configure cross-tenant app access to Planetary Computer Pro | https://learn.microsoft.com/en-us/azure/planetary-computer/configure-cross-tenant-application |
| Secure GeoCatalog access via Azure API Management | https://learn.microsoft.com/en-us/azure/planetary-computer/create-api-proxy-geocatalog |
| Generate collection-level SAS tokens for GeoCatalog assets | https://learn.microsoft.com/en-us/azure/planetary-computer/get-collection-sas-token |
| Configure RBAC access for Planetary Computer GeoCatalog | https://learn.microsoft.com/en-us/azure/planetary-computer/manage-access |
| Configure managed identity credentials for Planetary Computer Pro ingestion | https://learn.microsoft.com/en-us/azure/planetary-computer/set-up-ingestion-credentials-managed-identity |
| Topic | URL |
|-------|-----|
| Configure Planetary Computer Pro collections for Explorer visualization | https://learn.microsoft.com/en-us/azure/planetary-computer/collection-configuration-concept |
| Configure collection visualization settings in Planetary Computer Pro portal | https://learn.microsoft.com/en-us/azure/planetary-computer/configure-collection-web-interface |
| Apply sample render configurations for Planetary Computer Pro data visualization | https://learn.microsoft.com/en-us/azure/planetary-computer/data-visualization-samples |
| Configure ingestion sources for Planetary Computer Pro GeoCatalogs | https://learn.microsoft.com/en-us/azure/planetary-computer/ingestion-source |
| Configure mosaic options for Planetary Computer Pro collections | https://learn.microsoft.com/en-us/azure/planetary-computer/mosaic-configurations-for-collections |
| Configure queryables for custom search filters in Planetary Computer Pro | https://learn.microsoft.com/en-us/azure/planetary-computer/queryables-for-explorer-custom-search-filter |
| Configure render settings in Planetary Computer Pro | https://learn.microsoft.com/en-us/azure/planetary-computer/render-configuration |
| Configure SAS-based ingestion credentials for GeoCatalog | https://learn.microsoft.com/en-us/azure/planetary-computer/set-up-ingestion-credentials-sas-tokens |
| Use supported colormaps in Planetary Computer Pro render configurations | https://learn.microsoft.com/en-us/azure/planetary-computer/supported-colormaps |
| Configure tile settings for Planetary Computer Pro STAC collections | https://learn.microsoft.com/en-us/azure/planetary-computer/tile-settings |
| Configure US Government cloud endpoints for Planetary Computer Pro | https://learn.microsoft.com/en-us/azure/planetary-computer/us-government-cloud-support |
| Topic | URL |
|-------|-----|
| Ingest STAC items into GeoCatalog collections | https://learn.microsoft.com/en-us/azure/planetary-computer/add-stac-item-to-collection |
| Use Planetary Computer Pro APIs for STAC data | https://learn.microsoft.com/en-us/azure/planetary-computer/api-tutorial |
| Use Planetary Computer Pro GeoCatalog with Azure Batch | https://learn.microsoft.com/en-us/azure/planetary-computer/azure-batch |
| Build web apps using GeoCatalog STAC APIs and tiles | https://learn.microsoft.com/en-us/azure/planetary-computer/build-web-application |
| Bulk ingest geospatial data with GeoCatalog API | https://learn.microsoft.com/en-us/azure/planetary-computer/bulk-ingestion-api |
| Configure QGIS to connect to Planetary Computer Pro STAC collections | https://learn.microsoft.com/en-us/azure/planetary-computer/configure-qgis |
| Connect ArcGIS Pro to Planetary Computer GeoCatalog | https://learn.microsoft.com/en-us/azure/planetary-computer/create-connection-arc-gis-pro |
| Create STAC collections in GeoCatalog with Python | https://learn.microsoft.com/en-us/azure/planetary-computer/create-stac-collection |
| Create STAC items for Planetary Computer Pro raster assets | https://learn.microsoft.com/en-us/azure/planetary-computer/create-stac-item |
| Integrate third-party geospatial applications with Planetary Computer Pro | https://learn.microsoft.com/en-us/azure/planetary-computer/working-with-partner-applications |
| Topic | URL |
|-------|-----|
| Delete Planetary Computer GeoCatalog resources safely | https://learn.microsoft.com/en-us/azure/planetary-computer/delete-geocatalog-resource |
| Deploy Planetary Computer GeoCatalog resources | https://learn.microsoft.com/en-us/azure/planetary-computer/deploy-geocatalog-resource |
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-planetary-computer-pro 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.