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Memory Defrag

OrganizationPopular
basicmachines-co
memory-defrag

Defragment and reorganize agent memory files: split bloated files, merge duplicates, remove stale information, and restructure the memory hierarchy. Use when memory files have grown unwieldy, contain redundancies, or need reorganization. Run periodically (weekly) or on demand.

Overview

Publisherbasicmachines-co
Repositorybasic-memory
Skill namememory-defrag
Stars
4K
Forks
283
Bundled files
Instructions only
LicenseAGPL-3.0
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 basicmachines-co on GitHub. Read the source before you install it.

Installation

Install the Memory Defrag 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/basicmachines-co/basic-memory.git /tmp/basic-memory
mkdir -p .claude/skills
cp -r /tmp/basic-memory/skills/memory-defrag .claude/skills/memory-defrag
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Memory Defrag 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 Memory Defrag 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 Memory Defrag 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.

Memory Defrag

Reorganize memory files for clarity, efficiency, and relevance. Like filesystem defragmentation but for knowledge.

When to Run

  • Periodic: Weekly or biweekly via cron (recommended)
  • On demand: User asks to clean up, reorganize, or defrag memory
  • Threshold: When MEMORY.md exceeds ~500 lines or daily notes accumulate without consolidation

Process

1. Audit Current State

Inventory all memory files:

MEMORY.md           — long-term memory
memory/             — daily notes, tasks, topical files
memory/tasks/       — active and completed tasks

For each file, note: line count, last modified, topic coverage, staleness.

2. Identify Problems

Look for these common issues:

ProblemSignalFix
Bloated file>300 lines, covers many topicsSplit into focused files
Duplicate infoSame fact in multiple placesConsolidate to one location
Stale entriesReferences to completed work, old dates, resolved issuesRemove or archive
Orphan filesFiles in memory/ never referenced or updatedReview, merge, or remove
InconsistenciesContradictory information across filesResolve to ground truth
Poor organizationRelated info scattered across filesRestructure by topic
Recursive nestingmemory/memory/memory/... directoriesDelete nested dirs (indexer bug artifact)

3. Plan Changes

Before making edits, write a brief plan:

markdown
## Defrag Plan
- [ ] Split MEMORY.md "Key People" section → memory/people.md
- [ ] Remove completed tasks older than 30 days from memory/tasks/
- [ ] Merge memory/bm-marketing-ideas.md into memory/competitive/
- [ ] Update stale project status entries in MEMORY.md

4. Execute

Apply changes one at a time:

  • Split: Extract sections from large files into focused topical files
  • Merge: Combine related small files into coherent documents
  • Prune: Remove information that is no longer relevant or accurate
  • Restructure: Move files to appropriate directories, rename for clarity
  • Update: Fix outdated facts, dates, statuses

5. Verify & Log

After changes:

  • Verify no information was lost (compare before/after)
  • Update any cross-references between files
  • Log what was done in today's daily note:
markdown
## Memory Defrag (HH:MM)
- Files reviewed: N
- Split: [list]
- Merged: [list]
- Pruned: [list]
- Net result: X files, Y total lines (was Z lines)

Guidelines

  • Preserve raw daily notes. Don't delete or modify memory/YYYY-MM-DD.md files — they're the audit trail.
  • Target 15-25 focused files. Too few means bloated files; too many means fragmentation. Aim for the sweet spot.
  • File names should be scannable. Use descriptive names: people.md, project-status.md, competitive-landscape.md — not notes-2.md.
  • Don't over-organize. One level of directories is usually enough. memory/tasks/ and memory/competitive/ are fine; memory/work/projects/active/basic-memory/notes/ is not.
  • Completed tasks: Tasks with status: done older than 14 days can be removed. Their insights should already be in MEMORY.md via reflection.
  • Ask before destructive changes. If uncertain whether information is still relevant, keep it with a (review needed) tag rather than deleting.

Frequently asked questions

What does the Memory Defrag AI skill do?

Defragment and reorganize agent memory files: split bloated files, merge duplicates, remove stale information, and restructure the memory hierarchy. Use when memory files have grown unwieldy, contain redundancies, or need reorganization. Run periodically (weekly) or on demand.

Why use Memory Defrag on TypingMind?

Because you install it once and use it with any model. Memory Defrag 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 Memory Defrag in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/basicmachines-co/basic-memory/tree/main/skills/memory-defrag. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Memory Defrag?

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 Memory Defrag?

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

Is the Memory Defrag AI skill free?

Yes. It is published on GitHub by basicmachines-co under the AGPL-3.0 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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