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Official agent skill

Planetscale Schema Recommendations Agent Loop

by planetscale in planetscale/skills

Safely triage PlanetScale schema recommendations and turn them into reviewed branches, migrations, issues, or pull requests without applying production changes.

OfficialMITAuto-check passedDevelopment

Install Planetscale Schema Recommendations Agent Loop

skills CLI
$ npx skills add planetscale/skills --skill planetscale-schema-recommendations-agent-loop -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install planetscale/skills planetscale-schema-recommendations-agent-loop --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/planetscale/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/planetscale-schema-recommendations-agent-loop .claude/skills/planetscale-schema-recommendations-agent-loop && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
planetscale-schema-recommendations-agent-loop
GitHub stars
132
Token cost
~979 tokens
SKILL.md length
493 words
Files
1
Skills in repo
15
Repo updated
First seen
Licence
MIT

At a glance

Safely triage PlanetScale schema recommendations and turn them into reviewed branches, migrations, issues, or pull requests without applying production changes.

  • Works in 7 steps: Create or use a development branch. → Apply the schema change to that branch… → Open a deploy request only after approval. → …
  • Tasks that involve Autonomous loops
  • SKILL.md covers Purpose, Inputs, Recommendation types to… and Triage questions, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Planetscale Schema Recommendations Agent Loop is an agent skill from planetscale/skills, published by the product's own GitHub organization. Safely triage PlanetScale schema recommendations and turn them into reviewed branches, migrations, issues, or pull requests without applying production changes.

Its SKILL.md is about 980 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Development, covering Autonomous loops and Pull requests. It works with PlanetScale. The repository describes itself as: Skills that help you configure and get the most out of PlanetScale. The licence is MIT.

When your agent uses it

  • Tasks that involve Autonomous loops
  • Tasks that involve Pull requests

Example prompts

  • “/planetscale-schema-recommendations-agent-loop”

Workflow steps

7 steps, taken from the first numbered list in SKILL.md.

  1. Create or use a development branch.
  2. Apply the schema change to that branch only after approval.
  3. Open a deploy request only after approval.
  4. Use deploy request review to inspect schema, shard impact, data-loss warnings, lint errors, and conflicts.
  5. Use normal safe migration path unless instant deployment is explicitly justified.
  6. Deploy only after approval.
  7. Monitor Insights and anomaly state after deployment.

What it can do on your machine

Read from SKILL.md and the folder at commit 999045c. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Planetscale Schema Recommendations Agent Loop loads about 979 tokens when it runs. Until then it costs about 52 tokens; SKILL.md has 493 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~52
When it runs · the whole SKILL.md, loaded when a task matches
~979

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from planetscale/skills at commit 999045c, republished under its MIT licence (© planetscale). 493 words, ~979 tokens.

Download SKILL.mdSave it as .claude/skills/planetscale-schema-recommendations-agent-loop/SKILL.md (or your agent's skills folder).
name
planetscale-schema-recommendations-agent-loop
description
Safely triage PlanetScale schema recommendations and turn them into reviewed branches, migrations, issues, or pull requests without applying production changes.

Schema recommendations agent loop

Purpose

Use PlanetScale schema recommendations as high-quality input to agents. Convert recommendations into safe implementation plans, issues, branches, migrations, or pull requests. Do not apply recommendations directly.

Inputs

Collect:

  • Open schema recommendations.
  • Recommendation type.
  • Affected table, keyspace, schema, and query pattern.
  • Suggested DDL.
  • Supporting Insights evidence.
  • Application repository and migration system.
  • Engine: Vitess or Postgres.
  • Target branch.

Recommendation types to recognize

  • Add index for inefficient query.
  • Remove redundant index.
  • Prevent primary key ID exhaustion.
  • Drop unused table.
  • Upgrade legacy charset or collation.
  • Other DDL recommendation.

Triage questions

For each recommendation, answer:

  • Is this still open and relevant?
  • Which query patterns triggered it?
  • Which application code paths generate those queries?
  • Is the recommendation safely expressible in the application’s migration framework?
  • Does the ORM/schema source of truth need to change?
  • Can it be tested on a non-production branch?
  • What is the expected impact on reads, writes, storage, and deploy time?
  • Is there a rollback or revert path?
  • Is there a competing recommendation or migration?

Engine-specific implementation path

Vitess

Recommended path:

  1. Create or use a development branch.
  2. Apply the schema change to that branch only after approval.
  3. Open a deploy request only after approval.
  4. Use deploy request review to inspect schema, shard impact, data-loss warnings, lint errors, and conflicts.
  5. Use normal safe migration path unless instant deployment is explicitly justified.
  6. Deploy only after approval.
  7. Monitor Insights and anomaly state after deployment.

Default output before approval: issue or PR with migration proposal, not a live deploy request.

Postgres

Recommended path:

  1. Convert DDL into the application’s migration framework where possible.
  2. Test against a non-production branch.
  3. Run application tests and relevant query checks.
  4. Open PR.
  5. Apply production migration only after approval.
  6. Use backups/PITR runbook as recovery plan, not as a substitute for migration review.

Default output before approval: migration PR or issue, not production DDL.

Show full SKILL.md (177 more words)Show less

Codebase correlation

When a repository is available:

  • Search for the table and column names.
  • Search for ORM model definitions.
  • Search for migrations.
  • Search for query fingerprints, route tags, job names, and controller/action names from Insights.
  • Identify whether the recommendation should be implemented in database DDL, ORM schema, raw migration, or application query code.

Safety checks before proposing implementation

Block direct application when:

  • The recommendation is stale or already addressed.
  • The affected table is small enough that the benefit is unclear.
  • The index would be redundant with an existing index.
  • The index would hurt write-heavy workloads without enough read benefit.
  • The table appears unused but repository references are ambiguous.
  • Dropping a table or index lacks owner confirmation.
  • The migration framework has a different schema source of truth.
  • The recommendation targets production and no branch/test plan exists.

Output

For each recommendation, produce:

  • Recommendation ID/number.
  • Type.
  • Severity and expected benefit.
  • Evidence from Insights.
  • Affected schema.
  • Suggested DDL.
  • Application code owner or likely location.
  • Safe implementation path.
  • Validation plan.
  • Rollback/revert plan.
  • Approval requirement.

End with:

“No schema recommendations have been applied.”

© planetscale, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in planetscale-schema-recommendations-agent-loop of planetscale/skills.

Open the folder on GitHubat commit 999045c

Compare with similar skills

Planetscale Schema Recommendations Agent Loop next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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PRP LoopWirasm/prp2.3k—~894Automated safety check: PassMIT
PR BabysitterEveryInc/compound-engineering-plugin25k—~2kAutomated safety check: PassMIT
PRP Loop: Autonomous PipelineWirasm/prp2.3k—~863Automated safety check: PassMIT

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Works with

Categories

Questions about Planetscale Schema Recommendations Agent Loop

What does Planetscale Schema Recommendations Agent Loop do?

Safely triage PlanetScale schema recommendations and turn them into reviewed branches, migrations, issues, or pull requests without applying production changes. Planetscale Schema Recommendations Agent Loop is an agent skill from planetscale/skills, published by the product's own GitHub organization. Safely triage PlanetScale schema recommendations and turn them into reviewed branches, migrations, issues, or pull requests without applying production changes.

When should I use Planetscale Schema Recommendations Agent Loop?

Planetscale Schema Recommendations Agent Loop fits situations like: tasks that involve Autonomous loops; tasks that involve Pull requests.

How do I install Planetscale Schema Recommendations Agent Loop in Claude Code?

Run `npx skills add planetscale/skills --skill planetscale-schema-recommendations-agent-loop -a claude-code`. Or copy the skill folder (planetscale-schema-recommendations-agent-loop in planetscale/skills) into .claude/skills/planetscale-schema-recommendations-agent-loop in your project. Claude Code loads it when a task matches its description.

How do I install Planetscale Schema Recommendations Agent Loop in Codex?

Run `npx skills add planetscale/skills --skill planetscale-schema-recommendations-agent-loop -a codex`. Or copy the skill folder (planetscale-schema-recommendations-agent-loop in planetscale/skills) into .agents/skills/planetscale-schema-recommendations-agent-loop in your project. Codex loads it when a task matches its description.

Can I use Planetscale Schema Recommendations Agent Loop in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add planetscale/skills --skill planetscale-schema-recommendations-agent-loop -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/planetscale-schema-recommendations-agent-loop, .gemini/skills/planetscale-schema-recommendations-agent-loop, .github/skills/planetscale-schema-recommendations-agent-loop and .opencode/skills/planetscale-schema-recommendations-agent-loop in your project.

What does Planetscale Schema Recommendations Agent Loop need to run?

SKILL.md names no scripts, command-line tools or credentials: Planetscale Schema Recommendations Agent Loop is instructions for the agent only.

Does Planetscale Schema Recommendations Agent Loop access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Planetscale Schema Recommendations Agent Loop safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Planetscale Schema Recommendations Agent Loop use?

Planetscale Schema Recommendations Agent Loop is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Planetscale Schema Recommendations Agent Loop use?

About 979 tokens (SKILL.md is roughly 3.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Planetscale Schema Recommendations Agent Loop?

Skills that share tags, products or a category with Planetscale Schema Recommendations Agent Loop: PR Greenlight (UniClipboard/UniClipboard, 1.8k stars), Cap Feature Building Workflow (CapSoftware/Cap, 23k stars), PRP Loop (Wirasm/prp, 2.3k stars) and PR Babysitter (EveryInc/compound-engineering-plugin, 25k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Planetscale Schema Recommendations Agent Loop?

planetscale (a GitHub organization, an official publisher) maintains it in planetscale/skills, which has 132 GitHub stars. The repository holds 15 skills in this directory. The repository was last updated on October 3, 2026.

Source: planetscale/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.