[Skill] deployment context, aks, azure, koji - Resolve Koji AKS deployment context -- resource group, cluster name, subscription, Log Analytics workspace, and monitoring resource names from deployment_summary.yaml when available, or discover them dynamically from Azure.
npx skills add https://github.com/microsoft/azurelinux --skill skill-deployment-context
Always resolve context before running queries. Never assume resource names.
deployment_summary.yaml (if available)This file is typically generated by the Koji deployment pipeline (e.g., as an Azure DevOps pipeline artifact) and stored at the repository root as ./deployment_summary.yaml. It should contain resource_group, aks_cluster_name, subscription_id, and monitoring resource names/IDs.
az commands to discover the values and generate it for them.Run: az account show --query id -o tsv
Ask the user for the resource group or AKS cluster name.
az monitor log-analytics workspace list --resource-group <RG> -o table
az resource list --resource-group <RG> --resource-type Microsoft.Monitor/accounts -o table
az resource list --resource-group <RG> --resource-type Microsoft.Dashboard/grafana -o table
Resources follow <prefix>-koji-*:
<prefix>-koji-rg-<suffix><prefix>-koji-aks<prefix>-koji-logs<prefix>-koji-monitor<prefix>-koji-grafana| Tool | Purpose |
|------|---------|
| aks_cluster_get | List/get AKS cluster details |
| aks_nodepool_get | List/get node pool details |
| monitor_table_list | List tables in a Log Analytics workspace |
Prerequisites: Node.js (npx) and an active az login session.
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.
Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare datasets (MIMIC-III/IV, eICU, OMOP), or implementing deep learning models for healthcare applications (RETAIN, SafeDrug, Transformer, GNN).
Advanced GitHub Actions workflow automation with AI swarm coordination, intelligent CI/CD pipelines, and comprehensive repository management
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.
Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.
Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare datasets (MIMIC-III/IV, eICU, OMOP), or implementing deep learning models for healthcare applications (RETAIN, SafeDrug, Transformer, GNN).
Take microsoft/skill-deployment-context 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.