Execute mcloud deployments commands to list deployments, retrieve deployment details, and fetch build logs. Use when listing deployments, checking deployment status, or reading build output for debugging build failures.
npx skills add https://github.com/medusajs/medusa-agent-skills --skill mcloud-deployments
Execute mcloud deployments commands to inspect deployments and their build logs.
--json when parsing output — plaintext format may change.mcloud whoami --json) before running commands if org/project are not already known.--deployment IDs in the format depl_* or build IDs; build IDs resolve to their latest deployment automatically.List recent deployments for a project (default: 20 most recent across all environments).
mcloud deployments list --organization <org-id> --project <project-id-or-handle> --json
Options:
-o/--organization <id> — Organization ID (falls back to active context)-p/--project <id-or-handle> — Project ID or handle (falls back to active context)-e/--environment <handle> — Filter by environment handle--environment-type <production|long-lived|preview> — Filter by environment type--commit <sha> — Filter by Git commit SHA (full or prefix)--limit <1-200> — Max results (default: 20)--offset <number> — Pagination offset (default: 0)--json — Output as JSONRetrieve a single deployment's details by ID.
mcloud deployments get <deployment-id> --organization <org-id> --project <project-id-or-handle> --json
Arguments:
deployment — Deployment ID (required)Options:
-o/--organization <id>, -p/--project <id-or-handle>, --jsonFetch build logs for a deployment. Use this to debug build-failed status.
mcloud deployments build-logs <deployment-id> --organization <org-id> --project <project-id-or-handle>
Arguments:
deployment — Deployment ID (required)Options:
-o/--organization <id>, -p/--project <id-or-handle>--type <backend|storefront> — Which build log stream to read (default: backend)--json — Output as JSON| Status | Meaning |
|--------|---------|
| created | Build not started yet |
| building | Build running |
| built | Build succeeded, awaiting rollout |
| deploying | Rolling out to environment |
| deployed | Live and serving traffic |
| build-failed | Build step failed — read build-logs |
| deployment-failed | Build succeeded, runtime crashed — read mcloud logs |
| timed-out | Exceeded time budget (backend only) |
| canceled | Superseded by a newer deployment |
| idle | No longer the active deployment |
# List all deployments (with active context set)
mcloud deployments list --json
# Find most recent build-failed deployment
mcloud deployments list --json \
| jq -r '[.[] | select(.backend_status == "build-failed")][0].id'
# Get deployment details
mcloud deployments get bld_01ABC123 --json
# Read backend build logs
mcloud deployments build-logs bld_01ABC123
# Read storefront build logs
mcloud deployments build-logs bld_01ABC123 --type storefront
# Filter deployments by commit SHA
mcloud deployments list --commit a1b2c3d --json | jq '.'
# Get deployments for a specific environment
mcloud deployments list --environment production --json
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 medusajs/mcloud-deployments 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.