System Storage Cleanup logo

System Storage Cleanup

OrganizationPopular
vellum-ai
system-storage-cleanup

Handle disk pressure, critically low storage, safe storage limits, and storage cleanup mode by inspecting disk usage and proposing safe cleanup steps.

Overview

Publishervellum-ai
Repositoryvellum-assistant
Skill namesystem-storage-cleanup
Stars
1.3K
Forks
186
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 vellum-ai on GitHub. Read the source before you install it.

Installation

Install the System Storage Cleanup 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.
  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/vellum-ai/vellum-assistant.git /tmp/vellum-assistant
mkdir -p .claude/skills
cp -r /tmp/vellum-assistant/assistant/src/config/bundled-skills/system-storage-cleanup .claude/skills/system-storage-cleanup
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable System Storage Cleanup 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 System Storage Cleanup 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 System Storage Cleanup 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.

You are operating under a critical storage cleanup contract. Your only goal is to free enough storage for the assistant to resume normal work without damaging user data.

Cleanup Contract

Start by warning the user that storage is critically low and normal work is suspended until storage cleanup mode clears. Stay scoped to freeing storage until the disk-pressure lock clears or the guardian explicitly overrides it.

Prefer foreground inspection with available cleanup-safe tools before any mutation. Identify both the target volume that is actually full and the workspace path before proposing deletions. Do not work on unrelated tasks, refactors, installs, upgrades, or product changes while the storage lock is active.

Ask for explicit approval before deleting files, caches, logs, package caches, Docker artifacts, or any other data unless the user has already approved that exact action. Before asking, present each proposed deletion with:

  • Exact path or artifact name.
  • Estimated reclaimable size.
  • Expected consequence, including whether it is regenerable or may remove user-visible history.

If the user approves a broad category, narrow it to exact paths or artifacts before deleting. If the user approves one exact path, do not treat that as approval for adjacent paths.

Never delete credentials, security material, workspace database files, config files, active profiler runs, migrations, skill source, app source, conversation records, memory graph nodes or segments, journal/, data/reflections/, PKB files, backups, or backup keys unless the user explicitly names that path and accepts the consequence.

Inspection Procedure

Use local/container-visible inspection first. Prefer df -h on the current workspace path and on VELLUM_WORKSPACE_DIR when that variable is available. In Docker/container mode, /workspace is the persistent volume and cleanup should normally focus there.

Use du one level at a time and sort by size to identify large directories before drilling deeper. Keep each pass readable and bounded to the volume or workspace that is actually full. Avoid whole-filesystem scans unless the target volume cannot be isolated.

Use host_bash only when the sandbox cannot see the volume that is actually full and host-level inspection is necessary. Explain why host-level inspection is needed before using it.

SQLite Diagnosis Only

If data/db/assistant.db dominates disk usage, inspect it only through read-only sqlite3 access. Diagnostics may use PRAGMA and SELECT queries for:

  • page_size
  • page_count
  • freelist_count
  • dbstat object-size breakdowns

The purpose is to determine whether growth comes from specific tables/indexes or from free-page bloat. Do not edit the database manually.

Never run ad hoc DELETE, UPDATE, INSERT, DROP, REINDEX, schema changes, VACUUM, PRAGMA writable_schema, or any other mutating SQLite command from this skill. If SQLite tables are the main culprit, tell the user this needs product-owned retention or maintenance work rather than manual database editing.

Safer Cleanup Candidates

Good candidates to inspect and propose, when they are clearly nonessential and user-approved, include:

  • Scratch or temporary downloads created by the assistant.
  • Generated build artifacts.
  • Old logs.
  • Stale temporary directories.
  • Completed profiler runs that are no longer active.
  • Stale caches.
  • Large old diagnostic text attachments such as spindumps.
  • Package cache cleanup or Docker cache/artifact cleanup after the user approves the exact package manager or Docker action.

Treat source files, persistent records, and user-authored content as protected unless the user explicitly names them for deletion and accepts the consequence.

Non-Goals

ATL-450 product work is out of scope for this skill. Do not implement configurable trace or audit retention jobs, attachment retention or compression jobs, upload-time image re-encoding, first-class disk-usage UI, one-click cleanup buttons, or scheduled SQLite vacuuming. If inspection shows those are needed, report that as follow-up product work after the immediate storage cleanup path is safe.

Frequently asked questions

What does the System Storage Cleanup AI skill do?

Handle disk pressure, critically low storage, safe storage limits, and storage cleanup mode by inspecting disk usage and proposing safe cleanup steps.

Why use System Storage Cleanup on TypingMind?

Because you install it once and use it with any model. System Storage Cleanup 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 System Storage Cleanup in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/vellum-ai/vellum-assistant/tree/main/assistant/src/config/bundled-skills/system-storage-cleanup. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use System Storage Cleanup?

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 System Storage Cleanup?

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

Is the System Storage Cleanup AI skill free?

Yes. It is published on GitHub by vellum-ai 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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