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Planetscale Best Practices Matrix

Organization
planetscale
planetscale-best-practices-matrix

A concise feature matrix for deciding which PlanetScale safety, observability, and automation recommendations apply by engine.

Overview

Publisherplanetscale
Repositoryskills
Skill nameplanetscale-best-practices-matrix
Stars
136
Forks
6
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by planetscale on GitHub. Read the source before you install it.

Installation

Install the Planetscale Best Practices Matrix AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/planetscale/skills/tree/main/planetscale-best-practices-matrix
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/planetscale/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/planetscale-best-practices-matrix .claude/skills/planetscale-best-practices-matrix
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Planetscale Best Practices Matrix in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Planetscale Best Practices Matrix on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Planetscale Best Practices Matrix is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Best-practices matrix

Purpose

Map database findings to recommended PlanetScale features. Use this to ensure the assessment does not miss major safety and operational surfaces.

Cross-engine recommendations

Query Insights

Recommend for every production database:

  • Review slow, expensive, high-frequency, and erroring query patterns.
  • For Postgres, sort Insights by CPU (sort=cpuTime on the Insights API) when diagnosing CPU pressure.
  • For sharded Vitess, review vindex usage per query pattern and the usage trend after index or routing changes.
  • Correlate regressions with deploys.
  • Use tags/comments to map queries back to code.
  • Use tag filtering/navigation in Query Insights: the tags API (insights/tags, insights/tags/summaries) on both engines, plus tag:key:value filtering and per-execution tag drill-down in the Vitess dashboard.
  • Use anomalies as alert and automation inputs.

Webhooks

Recommend for operational events:

  • Anomalies.
  • Storage pressure.
  • Branch readiness/sleeping.
  • Primary promotion.
  • Maintenance.
  • Schema recommendations where available.
  • Deploy request lifecycle for Vitess.

MCP and agents

Recommend:

  • Use insights-only MCP for most autonomous analysis.
  • Use full MCP only with narrow scopes and read-only default.
  • Put database targeting and safety rules in AGENTS.md.
  • Agents generate PRs/issues/change plans; humans approve database changes.

SQLCommenter / query tags

Recommend:

  • Add framework-native SQLCommenter-compatible instrumentation.
  • Use low-cardinality tags.
  • Include application, service, route/job, feature, source, and release SHA.
  • Avoid PII and unbounded IDs.

Schema recommendation workflow

Recommend:

  • Triage open recommendations.
  • Correlate with code and Insights.
  • Convert into migrations or branch changes.
  • Test before production.
  • Apply only through approved workflow.

Vitess-specific recommendations

Safe migrations

Recommend for production branches and staging branches that accept deploy requests.

Deploy requests

Recommend for schema changes into protected branches.

Force cutover discipline

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.

Admin approval

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.

Gated deployments

Recommend when cutover timing and human control matter.

Schema revert runbook

Recommend documenting revert responsibilities and the application rollback relationship.

Branch strategy

Recommend production, staging, and short-lived development branches with safe migrations on protected targets.

Sharding/keyspace review

Recommend when query patterns or growth suggest shard-awareness problems. Do not reshard automatically.

Postgres-specific recommendations

User-defined roles

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.

pg_strict

Recommend for application roles after evaluation, especially to block accidental full-table update/delete mistakes.

Traffic Control

Recommend for resource isolation of agents, exports, reports, workers, integrations, BI, and known expensive fingerprints.

Backups and PITR

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.

PgBouncer and connection pooling

Recommend where connection churn or serverless/edge behavior creates pressure, subject to transaction-pooling limitations.

Private connectivity and IP restrictions

Recommend for customers requiring private network posture or reduced public exposure. Treat changes as production-risking.

Extensions

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.

Live connections

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.

Output

For each matrix item, mark:

  • Applies: yes/no/unknown.
  • Current state.
  • Gap.
  • Value: the specific measured finding this feature addresses (query fingerprint, anomaly count, incident, metric). State the mechanism and the measurement. Gaps are capability gaps with quantified impact, not risks safely avoided.
  • Recommendation ID.
  • Approval requirement.

End with:

“No changes have been applied.”

Frequently asked questions

What does the Planetscale Best Practices Matrix AI skill do?

A concise feature matrix for deciding which PlanetScale safety, observability, and automation recommendations apply by engine.

Why use Planetscale Best Practices Matrix on TypingMind?

Because you install it once and use it with any model. Planetscale Best Practices Matrix is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Planetscale Best Practices Matrix in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/planetscale/skills/tree/main/planetscale-best-practices-matrix. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Planetscale Best Practices Matrix?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Planetscale Best Practices Matrix?

As many as you like. As long as a model supports skills, you can use Planetscale Best Practices Matrix with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Planetscale Best Practices Matrix AI skill free?

Yes. It is published on GitHub by planetscale under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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