Execute mcloud logs to fetch and stream runtime logs for Cloud environments. Use when reading backend or storefront logs, filtering by time range, searching for errors, or scoping logs to a specific deployment.
npx skills add https://github.com/medusajs/medusa-agent-skills --skill mcloud-logs
Execute mcloud logs to fetch runtime logs for a Cloud environment's backend or storefront.
--follow and --json are incompatible. For programmatic log analysis, use bounded time windows with --from/--to and --json.--follow streams until interrupted with Ctrl+C — do not use in scripts or pipelines.mcloud logs \
--organization <org-id> \
--project <project-id-or-handle> \
--environment <environment-handle> \
[options]
| Option | Description | Default |
|--------|-------------|---------|
| -o/--organization <id> | Organization ID | Active context |
| -p/--project <id-or-handle> | Project ID or handle | Active context |
| -e/--environment <handle> | Environment handle | Active context |
| -f/--follow | Stream logs continuously (incompatible with --json) | false |
| --limit <1-5000> | Max log lines (non-follow mode only) | 500 |
| --from <ISO8601> | Start of time range (e.g. 2026-04-22T10:00:00Z) | 15 minutes ago |
| --to <ISO8601> | End of time range; if >15 min ago, must also pass --from | now |
| --search <string> | Filter by substring (same as dashboard search bar) | — |
| --deployment <id> | Filter by deployment or build ID | — |
| --source <string> | Filter by source (repeatable) | — |
| --metadata <key=value> | Filter by metadata field (repeatable; same key merges values) | — |
| --type <backend\|storefront> | Log stream to query | backend |
| --json | Output as JSON (incompatible with --follow) | false |
# Basic log fetch (last 500 lines, last 15 min)
mcloud logs --json
# Search for errors
mcloud logs --search error --limit 1000 --json
# Filter for HTTP 500 errors via metadata
mcloud logs --metadata status=500 --limit 1000 --json
# Logs for a specific deployment (build or deployment ID)
mcloud logs --deployment bld_01ABC123 --json
# Structured output for agent analysis
mcloud logs --search error --json | jq '.[] | {timestamp, source, message}'
# Storefront logs
mcloud logs --type storefront --json
# Stream live logs (human-readable, not for scripts)
mcloud logs --follow
# Logs within a specific time range
mcloud logs --from 2026-04-22T10:00:00Z --to 2026-04-22T11:00:00Z --limit 1000 --json
# Logs from a time until now
mcloud logs --from 2026-04-22T10:00:00Z --json
# Multiple source filters
mcloud logs --source api --source worker --json
# Multiple metadata filters (HTTP 4xx and 5xx)
mcloud logs --metadata status=400 --metadata status=500 --limit 500 --json
--from without --to to fetch from a time until now.--to without --from only if --to is within the last 15 minutes; otherwise also pass --from.--from and --to accept ISO 8601 timestamps.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.
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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-logs 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.