A concise feature matrix for deciding which PlanetScale safety, observability, and automation recommendations apply by engine.
npx skills add https://github.com/planetscale/skills --skill planetscale-best-practices-matrix
Map database findings to recommended PlanetScale features. Use this to ensure the assessment does not miss major safety and operational surfaces.
Recommend for every production database:
sort=cpuTime on the Insights API)when diagnosing CPU pressure.
trend after index or routing changes.
(insights/tags, insights/tags/summaries) on both engines, plus
tag:key:value filtering and per-execution tag drill-down in the Vitess
dashboard.
Recommend for operational events:
Recommend:
AGENTS.md.Recommend:
Recommend:
Recommend for production branches and staging branches that accept deploy requests.
Recommend for schema changes into protected branches.
Recommend documenting who may use "force cutover now" for deploy requests
delayed by long-running transactions. It stops running transactions to finish
schema cutover, so frequent use should trigger workload review before enabling
aggressive cutover as the database default.
Recommend for production deploy requests in multi-admin organizations.
In a single-admin organization, approval alone is not a guard against agents: that admin can
open and approve the same deploy request. Prefer a separate agent identity, or a service token
that cannot approve deploy requests.
Recommend when cutover timing and human control matter.
Recommend documenting revert responsibilities and the application rollback relationship.
Recommend production, staging, and short-lived development branches with safe migrations on protected targets.
Recommend when query patterns or growth suggest shard-awareness problems. Do not reshard automatically.
Recommend for application servers instead of default role. If roles are managed
by Terraform and passwords should stay outside Terraform state, prefer
planetscale_postgres_redacted_branch_role plus a separate password reset and
secret-manager storage path.
Recommend for application roles after evaluation, especially to block accidental full-table update/delete mistakes.
Recommend for resource isolation of agents, exports, reports, workers, integrations, BI, and known expensive fingerprints.
Recommend verifying retention and restore drill coverage.
If Terraform is the customer's source of truth, recommend managing backup
policies there so backup posture changes are reviewed as infrastructure code.
Recommend where connection churn or serverless/edge behavior creates pressure, subject to transaction-pooling limitations.
Recommend for customers requiring private network posture or reduced public exposure. Treat changes as production-risking.
Recommend only when use case is clear and restart/activation impact is accepted.
Include auto_explain when automatic plan logging for slow queries would
materially improve diagnosis and the resulting log volume is acceptable.
If Terraform manages Postgres branch parameters or supported extensions, keep
that source of truth aligned with approved dashboard/API changes.
Recommend inspecting pscale branch connections top during active connection
pressure incidents to identify sessions, blockers, and idle-in-transaction
roots without relying on normal database connection capacity.
For each matrix item, mark:
fingerprint, anomaly count, incident, metric). State the mechanism and
the measurement. Gaps are capability gaps with quantified impact, not
risks safely avoided.
End with:
“No changes have been applied.”
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 planetscale/planetscale-best-practices-matrix 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.