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Memory

Organization
plasma-ai
memory

How to maintain node memory well -- the node's persistent knowledge store.

Overview

Publisherplasma-ai
Repositoryfractal
Skill namememory
Stars
782
Forks
57
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 plasma-ai on GitHub. Read the source before you install it.

Installation

Install the Memory 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/plasma-ai/fractal/tree/main/fractal/_node/skills/memory
  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/plasma-ai/fractal.git /tmp/fractal
mkdir -p .claude/skills
cp -r /tmp/fractal/fractal/_node/skills/memory .claude/skills/memory
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Memory 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 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 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

Memory ($MEMORY_DIR) is the node's durable brain. Read it when you orient and fold findings back before each iteration ends. Sync may also write to memory when crucial information arrives via radio. See the wiki skill for how memory relates to the shared project wiki; this doc is the discipline for keeping memory useful.

Run wiki --help and wiki <command> --help for the CLI.

Conventions

  • Write knowledge, not history. Never reference iteration numbers, timestamps, or chronological markers -- a reader shouldn't be able to tell how many iterations have run. Before COMMIT, scan memory and any page you promote for iteration numbers and run labels and rewrite them out -- promotion carries your habits onto shared surfaces.
  • Organize by topic, not time. Update the existing page for a topic; don't append a new entry.
  • Start with a topical layout. Your FIRST memory write creates the layout -- one page per topic (environment, decisions, state, todo -- rename to fit the work; stubs are fine), never a single grab-bag page; when a page starts covering two topics, split it. A leaf expecting to finish within an iteration or two may keep just the state page.
  • One current-state page. Keep exactly one present-tense state page for where-am-I / what's-next, overwritten in place -- no "prior run" sections; per-iteration narrative belongs in the plan post-mortem, never memory.
  • Pages need desc: frontmatter. A page without it (a near-miss key like description: counts as without) is invisible to wiki map; after adding or moving pages, run wiki update --path=$MEMORY_DIR so they gain frontmatter and index entries. Quote a one-line desc: that contains : or #.
  • Fold at phase ends. When a phase or a child's run ends, collapse its pages into durable facts and delete the rest -- memory carries what is still true, not what happened.
  • No append-only logs. If you're adding dated entries, stop -- replace outdated content with current understanding.
  • Todo lists are living state. Keep your private working checklist here as current open items, pruned as they complete -- never a done-log. A todo list other nodes should see and track belongs in the project wiki instead.
  • Keep indexes lean. Keep each _index.md under ~100 lines below the ***; factor overflow into child pages.
  • Wikilinks stay within one wiki. Reference anything outside this wiki -- the project wiki, source files, configs -- in plain text or backticks, never as a wikilink; wiki lint notes a wikilink that points outside the wiki. Write in-wiki links prefix-free ([[state]], never [[./state]] or [[../state]]).

Frequently asked questions

What does the Memory AI skill do?

How to maintain node memory well -- the node's persistent knowledge store.

Why use Memory on TypingMind?

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

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

Which AI models can use Memory?

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?

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

Is the Memory AI skill free?

Yes. It is published on GitHub by plasma-ai 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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