Planetscale Traffic Control Recommendations

Organization

planetscale

planetscale-traffic-control-recommendations

Build a safe recommendation plan for PlanetScale Postgres Database Traffic Control budgets and rules without applying them.

Overview

Publisher	planetscale
Repository	`skills`
Skill name	`planetscale-traffic-control-recommendations`
Stars	136
Forks	6
Bundled files	Instructions only
License	MIT
Links	Skill source Repository

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 Traffic Control Recommendations AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/planetscale/skills/tree/main/planetscale-traffic-control-recommendations
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/planetscale/skills/tree/main/planetscale-traffic-control-recommendations

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

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-traffic-control-recommendations .claude/skills/planetscale-traffic-control-recommendations

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Planetscale Traffic Control Recommendations 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 Traffic Control Recommendations 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 Traffic Control Recommendations 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.

Database Traffic Control recommendations

Purpose

For PlanetScale Postgres, recommend Traffic Control budgets and rules that protect critical traffic from runaway queries, traffic spikes, batch jobs, agents, and third-party integrations. Do not create or change budgets without approval.

Preconditions

Run this skill only for PlanetScale Postgres.

Before recommending rules, inspect:

Current budgets and rules.
Insights query patterns.
Current query tags.
Application routes and jobs.
Known critical paths.
Known expensive non-critical paths.
Active incidents or recent anomalies.

If query tags are missing, recommend tagging first unless a fingerprint-specific rule is clearly needed for an immediate known offender.

Candidate traffic slices

Look for:

Exports.
Reports.
Search endpoints.
Admin dashboards.
Backfills.
Workers and queues.
Webhooks from third-party systems.
BI tools.
Agent-generated read queries.
High-frequency polling.
Known expensive query fingerprints.
Customer-triggered endpoints with high variance.

Budget modes

Recommend in this order:

warn mode first for normal rollout.
Observe warnings and false positives.
Tune tags, fingerprints, and thresholds.
Move to enforce only with explicit approval and an emergency rollback path.

Do not recommend starting directly in enforce unless there is an active incident and the operator explicitly asks for emergency mitigation.

Rule strategy

Prefer tag-based rules when tags are stable and bounded:

source=agent
source=bi
feature=export
feature=report
route=/admin/reports
job=DailyBackfill
service=analytics-worker

Use fingerprint rules when:

A specific known query pattern is dangerous.
Tagging is missing or unreliable.
The query source is hard to attribute.

Use a separate budget for each materially different traffic class.

Do not combine unrelated traffic in one budget because it hides who is consuming the budget.

Suggested default budgets

Use these as recommendation patterns, not as values to apply blindly.

Agent budget

Target: queries tagged source=agent or source=mcp.

Intent: prevent agents from starving application traffic.

Mode: start in warn.

Recommendation: agents should prefer replicas and read-only scopes. Writes require human approval.

Export/reporting budget

Target: feature=export, feature=report, or specific report route/job.

Intent: keep customer-triggered reporting from consuming all database resources.

Mode: start in warn; consider enforce after observation.

Background job budget

Target: worker service, queue, or job tags.

Intent: prevent backfills and retries from starving interactive traffic.

Mode: warn first; enforce only after confirming queue backpressure behavior.

Third-party integration budget

Target: source=integration, partner-specific bounded tags, or route templates for inbound integration calls.

Intent: isolate unpredictable partner behavior.

Mode: warn first.

Known fingerprint budget

Target: specific expensive query fingerprint.

Intent: contain a known pathological query while code or schema fixes are developed.

Mode: warn first unless emergency.

“Each tag value” strategy

When PlanetScale supports applying a budget separately for each unique value of a selected tag, recommend it for bounded tags such as:

application
service
route when normalized
job
feature
source

Do not recommend it for unbounded tags such as user IDs, request IDs, raw tenant IDs, emails, UUIDs, or raw URLs.

Limits and caveats to include

Every recommendation must explain:

Traffic Control limits resource use; it does not replace query tuning.
It is not a web application firewall.
It does not replace application-level rate limits.
Limits are guardrails, not exact guarantees for every failure mode.
Bad tags create bad rules.
Enforce mode can reject queries and affect application behavior.

Output format

For each proposed budget:

Budget name.
Target branch.
Mode: off, warn, or enforce.
Matched traffic slice.
Rule type: tag, fingerprint, keyspace, query kind.
Proposed tags or fingerprint.
Limit rationale.
Queries seen in Insights that justify it.
Safety risk.
Test/observe plan.
Rollback plan.
Approval requirement.

End with:

“No Traffic Control budgets or rules have been created, updated, deleted, or enforced.”

Frequently asked questions

What does the Planetscale Traffic Control Recommendations AI skill do?

Build a safe recommendation plan for PlanetScale Postgres Database Traffic Control budgets and rules without applying them.

Why use Planetscale Traffic Control Recommendations on TypingMind?

Because you install it once and use it with any model. Planetscale Traffic Control Recommendations 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 Traffic Control Recommendations in TypingMind?

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

Which AI models can use Planetscale Traffic Control Recommendations?

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 Traffic Control Recommendations?

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

Is the Planetscale Traffic Control Recommendations 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Database Traffic Control recommendations

Purpose

Preconditions

Candidate traffic slices

Budget modes

Rule strategy

Suggested default budgets

Agent budget

Export/reporting budget

Background job budget

Third-party integration budget

Known fingerprint budget

“Each tag value” strategy

Limits and caveats to include

Output format

Frequently asked questions

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