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Memory Management

OrganizationPopular
ThinkInAIXYZ
memory-management

Guide the agent to recall, remember, and route durable learning into Memory, Skills, Scheduled Tasks, or Tape.

Overview

PublisherThinkInAIXYZ
Repositorydeepchat
Skill namememory-management
Stars
6.3K
Forks
734
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 ThinkInAIXYZ on GitHub. Read the source before you install it.

Installation

Install the Memory Management 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/ThinkInAIXYZ/deepchat.git /tmp/deepchat
mkdir -p .claude/skills
cp -r /tmp/deepchat/resources/skills/memory-management .claude/skills/memory-management
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Memory Management 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 Management 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 Management 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 Management

Use this skill when a task may produce durable learning or when the user asks you to recall, remember, continue earlier work, preserve an exact statement, capture a reusable procedure, or handle a recurring need.

Recall

Rely on automatic memory injection for ordinary context. Use memory_recall when the user refers to previous work with cues such as again, last time, before, continue, same project, remember, or asks what you already know.

Use tape_search and then tape_context when the user needs source evidence, exact wording, logs, command output, file snippets, or why a prior decision was made. Memory is a durable conclusion layer, not the raw transcript.

Remember

Use memory_remember only for durable conclusions that should change future behavior. Choose the most specific category:

  • user_preference: stable user preferences, constraints, communication style, environment choices.
  • project_fact: durable project conventions, architecture entry points, commands, dependencies, paths, or operational constraints.
  • task_outcome: completed, blocked, or deliberately deferred task results. Include status, outcome, and blocker in prose when relevant.
  • heuristic: reusable troubleshooting strategy, workflow, decision rule, or engineering lesson.
  • anti_pattern: repeated mistake, unsafe approach, brittle pattern, stale assumption, or thing to avoid.

Do not remember raw tool results, bash output, grep output, file contents, transient mechanics, one-off failures, secrets, credentials, hidden reasoning, or anything only useful for the current turn.

Verbatim Scope

Store exact wording only when the user explicitly asks you to remember a sentence or phrase verbatim. In that case, keep the requested text intact and make the surrounding content minimal.

Automatic extraction is different: it should normalize durable facts into concise memory content, deduplicate related entries, and avoid preserving raw transcript text.

Procedures -> Skill

When the useful learning is a reusable multi-step procedure, prefer drafting a skill with skill_manage instead of stuffing the full procedure into Memory. Memory may keep a short pointer or heuristic, but the repeatable workflow belongs in a Skill.

Use skill_manage for draft skills only. Do not modify installed skills unless the user explicitly asks through the supported review flow.

Recurring -> Scheduled Task

When the user asks for a periodic, low-frequency, or future recurring action, suggest creating a Scheduled Task in settings. Memory does not wake the agent, schedule future work, or create automation side effects.

End-of-task Learning Check

Before finishing a non-trivial task, check whether there is one durable lesson to save:

  1. Did the user reveal a stable preference or constraint?
  2. Did you learn a durable project fact?
  3. Is there a task outcome, blocker, or explicit deferral worth preserving?
  4. Did a reusable heuristic work?
  5. Did an anti-pattern or stale assumption become clear?
  6. Is this actually a reusable procedure for skill_manage or a recurring need for Scheduled Tasks rather than Memory?

Remember only the smallest durable conclusion. Leave raw process in Tape.

Frequently asked questions

What does the Memory Management AI skill do?

Guide the agent to recall, remember, and route durable learning into Memory, Skills, Scheduled Tasks, or Tape.

Why use Memory Management on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ThinkInAIXYZ/deepchat/tree/dev/resources/skills/memory-management. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Memory Management?

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 Management?

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

Is the Memory Management AI skill free?

Yes. It is published on GitHub by ThinkInAIXYZ 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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