Planetscale Vitess Safety Review logo

Planetscale Vitess Safety Review

Organization
planetscale
planetscale-vitess-safety-review

Review a PlanetScale Vitess database for safe migrations, deploy requests, schema recommendations, Insights, webhooks, and operational safety.

Overview

Publisherplanetscale
Repositoryskills
Skill nameplanetscale-vitess-safety-review
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 Vitess Safety Review 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-vitess-safety-review
  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-vitess-safety-review .claude/skills/planetscale-vitess-safety-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Planetscale Vitess Safety Review 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 Vitess Safety Review 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 Vitess Safety Review 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.

Vitess safety review

Purpose

Recommend best practices for a PlanetScale Vitess database. Focus on production safety, deployability, observability, and agent-safe automation. Do not apply any changes.

Primary safety features to evaluate

Safe migrations

Check whether safe migrations are enabled on production and staging branches.

Recommend enabling safe migrations when:

  • The branch receives production traffic.
  • The team runs DDL outside deploy requests.
  • There is no protected branch workflow.
  • The application is expected to evolve schema frequently.

Explain the tradeoff: safe migrations reject direct DDL on protected branches and force schema changes through deploy requests. That is a feature, but it can break teams relying on direct production DDL. Treat enablement as a behavior-changing change requiring approval.

Deploy requests

Check:

  • Whether deploy requests are used for schema changes.
  • Whether administrator approval is required.
  • Whether deploy requests are reviewed for data loss, conflicts, lint errors, foreign key problems, charset issues, and shard impact.
  • Whether teams use gated deployments for cutover control.
  • Whether “deploy instantly” is used and whether the team understands it removes the gated-deployment/revert shape.
  • Whether cutover is regularly delayed by long-running transactions.
  • Whether deploy request events are subscribed to via webhooks.

Recommend:

  • Require deploy requests for production schema changes.
  • Enable administrator approval for production deploy requests when there is more than one administrator.
  • In a single-admin organization, administrator approval does not stop an agent that acts as that admin: the admin who opens a deploy request can also approve it. If the goal is to keep agents from self-approving, give the agent a separate user, or a service token without deploy request approval permission.
  • Prefer normal safe deployments over instant deployments unless the migration is known to be instant-safe and the rollback story is acceptable.
  • Use gated deployment when cutover timing matters.
  • Treat “force cutover now” as an operator-controlled action for delayed cutovers: it aggressively stops running transactions to complete schema cutover. Recommend reviewing the blocking workload and incident context before use, and only recommend the database-level aggressive cutover default when frequent cutover blocking is understood and accepted.

Schema revert

Check whether the team knows the revert window and whether their incident runbook includes it.

Recommend documenting:

  • How to identify a bad schema migration.
  • How to revert within the supported window.
  • Who is authorized to revert.
  • Which application deploy should be rolled back together with the schema revert.

Branch strategy

Recommend a branch topology:

  • main or equivalent production branch with safe migrations enabled.
  • staging branch based from production with safe migrations enabled.
  • Short-lived development branches based from staging.
  • Deploy requests from development to staging, then staging to production when appropriate.

Do not create branches without approval.

Query Insights

Review Insights for:

  • Slow queries.
  • Queries reading too many rows.
  • Erroring queries.
  • Queries with poor index usage.
  • For sharded databases, whether query patterns use relevant vindexes and how vindex usage changes after index or routing changes.
  • Unusual query volume.
  • Missing SQL comment tags.
  • Tag breakdowns when built-in metadata or SQLCommenter tags are present: tag:key:value filtering in the dashboard, and the insights/tags and insights/tags/summaries API endpoints for programmatic breakdowns.
  • Deploy correlation data.

Recommend enabling or improving application query tagging so Insights can attribute queries to app, route, controller, action, job, deployment SHA, and feature.

Anomalies

Review active and recent anomalies.

Recommend:

  • Subscribe branch.anomaly webhooks to the team’s alerting or automation system.
  • Route anomaly payloads to a triage workflow that opens an issue or agent task.
  • Correlate anomalies with deploy requests, application deploys, and query tags.
  • Do not automatically apply schema or code changes from anomaly events; generate a proposal or PR only.

Schema recommendations

Review open schema recommendations.

For each recommendation, capture:

  • Type: add index, remove redundant index, primary key exhaustion, unused table, legacy charset/collation, or other.
  • Affected table and keyspace.
  • Supporting query telemetry.
  • DDL.
  • Expected benefit.
  • Risk.
  • Test plan.

Recommend implementation path:

  • Convert the recommendation into an application migration or PlanetScale branch schema change.
  • Open a deploy request.
  • Review generated DDL and shard impact.
  • Benchmark or validate on a branch.
  • Deploy with safe migrations.
  • Monitor Insights and anomaly state after deployment.

Do not apply recommendations directly.

Backups and restore

Check backup posture and restore runbooks.

Recommend:

  • Verify automated backups exist.
  • Run a non-production restore drill periodically.
  • Document restore target, RPO/RTO expectation, and application cutover plan.
  • For sharded databases, document shard-aware restore expectations.

Sharding and keyspace safety

If the database is sharded, review:

  • Keyspaces and shards.
  • Vschema.
  • Cross-shard query patterns.
  • Whether schema deploy requests show per-shard impact.
  • Whether queries use shard-friendly access paths.

Recommend an agent-safe sharding review only as a proposal. Never reshard, change vschema, or alter routing automatically.

Webhook recommendations for Vitess

Evaluate and recommend webhooks for:

  • branch.anomaly
  • branch.primary_promoted
  • branch.ready
  • branch.sleeping
  • cluster.storage
  • keyspace.storage
  • deploy_request.opened
  • deploy_request.queued
  • deploy_request.in_progress
  • deploy_request.pending_cutover
  • deploy_request.schema_applied
  • deploy_request.errored
  • deploy_request.reverted
  • deploy_request.closed
  • branch.schema_recommendation if available in the webhook API for the customer’s database

Recommended destinations:

  • Alerting for anomaly, primary promotion, storage, and deploy errors.
  • Slack or internal notifications for deploy request lifecycle.
  • Agent intake queue for schema recommendations and anomalies, with PR-only output by default.

Output

Return:

  • Current Vitess safety posture.
  • Missing safety features.
  • Recommended workflow.
  • Recommended webhook subscriptions.
  • Schema recommendation triage table.
  • Deploy safety gaps.
  • Proposed changes requiring approval.

End with:

“No Vitess changes have been applied.”

Frequently asked questions

What does the Planetscale Vitess Safety Review AI skill do?

Review a PlanetScale Vitess database for safe migrations, deploy requests, schema recommendations, Insights, webhooks, and operational safety.

Why use Planetscale Vitess Safety Review on TypingMind?

Because you install it once and use it with any model. Planetscale Vitess Safety Review 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 Vitess Safety Review in TypingMind?

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

Which AI models can use Planetscale Vitess Safety Review?

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 Vitess Safety Review?

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

Is the Planetscale Vitess Safety Review 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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