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Minutes Cleanup

CommunityPopular
silverstein
minutes-cleanup

Manage old recordings — find large files, archive old meetings, delete processed originals. Use when the user says "clean up recordings", "how much space are meetings using", "delete old recordings", "archive meetings", "manage meeting storage", or asks about disk space from minutes.

Overview

Publishersilverstein
Repositoryminutes
Skill nameminutes-cleanup
Stars
1.5K
Forks
163
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 silverstein on GitHub. Read the source before you install it.

Installation

Install the Minutes 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/silverstein/minutes.git /tmp/minutes
mkdir -p .claude/skills
cp -r /tmp/minutes/tooling/skills/goldens/claude/minutes-cleanup .claude/skills/minutes-cleanup
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Minutes 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 Minutes 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 Minutes 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.

/minutes-cleanup

Help the user manage disk space and organize old recordings. Minutes is transcript-first: markdown notes and structured memory are durable, while raw audio is a temporary recovery/reprocessing layer unless pinned.

Check current usage

bash
minutes storage
minutes storage --json

Present this to the user before taking any action.

Common cleanup tasks

Preview raw-audio cleanup

After transcription, the original audio files are no longer needed for search or recap. They only matter if you want to re-transcribe with a better model or debug recovery. Cleanup is preview-only unless --apply is passed.

bash
# Use the configured retention policy
minutes cleanup

# Try a shorter successful-audio window
minutes cleanup --older-than 14d

# Machine-readable preview for agents
minutes cleanup --json

Delete raw audio candidates

Only apply cleanup after showing the preview and getting explicit confirmation.

bash
minutes cleanup --apply
minutes cleanup --older-than 14d --apply

Archive old meetings

Move meetings older than N days to an archive folder:

bash
mkdir -p ~/meetings/archive

# Find meetings older than 90 days
find ~/meetings -maxdepth 1 -name "*.md" -mtime +90

# Move them (confirm with user first)
find ~/meetings -maxdepth 1 -name "*.md" -mtime +90 -exec mv {} ~/meetings/archive/ \;

Archived meetings won't appear in minutes list or minutes search (which only scans ~/meetings/), but they're still on disk if needed.

Clean up processed voice memos

The watcher moves originals to ~/meetings/memos/processed/ after transcription:

bash
du -sh ~/meetings/memos/processed/ 2>/dev/null

Clean up stale state

bash
# Remove stale PID file
rm -f ~/.minutes/recording.pid

# Clean old logs (keep last 7 days)
find ~/.minutes/logs -name "*.log" -mtime +7 -delete 2>/dev/null

# Remove last-result.json (transient)
rm -f ~/.minutes/last-result.json

Gotchas

  • Never delete .md files without asking — These are the transcripts. They're small and contain the actual value. WAV files are the space hogs.
  • Prefer minutes cleanup over raw find -delete — The CLI understands pinned audio and sidecar stems.
  • Archived meetings are invisible to searchminutes search only walks ~/meetings/ and ~/meetings/memos/. If you need archived meetings searchable, configure QMD to index ~/meetings/archive/ too.
  • Audio deletion is irreversible — If the user might want to re-transcribe with a better model later, suggest pinning important recordings and only deleting old unpinned candidates.
  • Pin exceptions in frontmatter — Add audio_retention: pinned to a meeting to keep its raw audio out of cleanup candidates.
  • Audio is ~10 MB/minute, transcripts are ~1 KB/minute — Deleting audio saves 99%+ of space while keeping all searchable content.
  • iCloud sync caveat — If ~/meetings/ is in an iCloud-synced folder, deleted files go to "Recently Deleted" and still count against storage for 30 days.

Frequently asked questions

What does the Minutes Cleanup AI skill do?

Manage old recordings — find large files, archive old meetings, delete processed originals. Use when the user says "clean up recordings", "how much space are meetings using", "delete old recordings", "archive meetings", "manage meeting storage", or asks about disk space from minutes.

Why use Minutes Cleanup on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/silverstein/minutes/tree/main/tooling/skills/goldens/claude/minutes-cleanup. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Minutes 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 Minutes Cleanup?

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

Is the Minutes Cleanup AI skill free?

Yes. It is published on GitHub by silverstein 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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