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Compartmentalize

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MaxFreedomPollard
compartmentalize

Sweep this conversation and save everything worth referencing again in future work into Compartment, the encrypted memory vault. Run it before compacting or summarizing so nothing is lost to the summary, or on its own at any point to bank the session.

Overview

PublisherMaxFreedomPollard
RepositoryCompartment
Skill namecompartmentalize
Stars
579
Forks
7
Bundled files
Instructions only
LicenseApache-2.0
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 MaxFreedomPollard on GitHub. Read the source before you install it.

Installation

Install the Compartmentalize 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.
    https://github.com/MaxFreedomPollard/Compartment/tree/main/skills/compartmentalize
  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/MaxFreedomPollard/Compartment.git /tmp/Compartment
mkdir -p .claude/skills
cp -r /tmp/Compartment/skills/compartmentalize .claude/skills/compartmentalize
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Compartmentalize 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 Compartmentalize 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 Compartmentalize 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.

Save to Compartment before compacting.

Sweep the entire conversation, including any part already summarized, and store to Compartment what it holds that is worth keeping.

STORE anything worth referencing again in future work: names, addresses, contacts, account IDs, passwords, API keys and other credentials, lasting file locations and configuration, preferences, and every durable fact or decision reached. Not transient chatter, one-off trivia, things freely available on the internet, or the working details of a task in progress, such as run results, errors, log contents and temporary paths or settings.

For each item, memory_search first, then memory_store: update the existing memory when one already covers it, create a new one when none does.

Always store, when present: people, contacts and addresses. Passwords, API keys, tokens, account IDs, and where each one lives. URLs, hostnames, repo and release locations.

Then properly associate and store every durable observation, decision, opinion and thought that is not publicly available: anything that would be expensive or impossible to work out again from scratch.

Also store the session itself - as several one-claim memories, never one narrative: one for what was asked, one for what it turned into, one for what changed by the end, one for what is still open. That the work happened and what it did is information in its own right, sometimes more useful than any single detail inside it, and each of those claims is recalled on its own.

Skip: everything the STORE rule above excludes, anything already stored unless it is an update with additional or changed information, and the Compartment vault passphrase itself.

Write each memory to stand alone: ONE claim of at most 200 characters by default - the vault enforces this and refuses lists, headings and paragraphs - with no pronouns pointing back at this conversation and no "as discussed above". Never leave out information that is necessary to understand the memory on its own. Several facts go through memory_store_many, one record each, in one call. Store preferences, stances and judgement calls with kind='opinion': opinions update instead of accumulate, and one resembling a live opinion comes back for an explicit supersedes=[old id] resend. Set namespace, tags and importance. When a fact was established before today, pass discovered=YYYY-MM-DD; the vault stamps every memory's dates itself, so never type dates into the text.

Do not stop early. Finish the sweep, then report how many were stored and how many updated.

Frequently asked questions

What does the Compartmentalize AI skill do?

Sweep this conversation and save everything worth referencing again in future work into Compartment, the encrypted memory vault. Run it before compacting or summarizing so nothing is lost to the summary, or on its own at any point to bank the session.

Why use Compartmentalize on TypingMind?

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

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

Which AI models can use Compartmentalize?

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 Compartmentalize?

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

Is the Compartmentalize AI skill free?

Yes. It is published on GitHub by MaxFreedomPollard under the Apache-2.0 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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