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Archive Profile Memory

Community
ahmadrosid
archive-profile-memory

Archive or clean up facts from active MEMORY.md into memory-archive/ without deleting them. Use when the user wants to forget, remove, tidy old memory, or free space for new memories.

Overview

Publisherahmadrosid
Repositorynakama
Skill namearchive-profile-memory
Stars
492
Forks
91
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 ahmadrosid on GitHub. Read the source before you install it.

Installation

Install the Archive Profile 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/ahmadrosid/nakama/tree/main/packages/core/src/skills/bundled/archive-profile-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/ahmadrosid/nakama.git /tmp/nakama
mkdir -p .claude/skills
cp -r /tmp/nakama/packages/core/src/skills/bundled/archive-profile-memory .claude/skills/archive-profile-memory
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Archive Profile 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 Archive Profile 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 Archive Profile 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.

Use this skill when the user wants to remove facts from active memory without deleting them permanently.

  • Use the update-profile-memory skill to add facts to active MEMORY.md.
  • Use profile skills for repeatable procedures — not for archiving memory.
  • Archived facts live under memory-archive/ and are not loaded into chat automatically. Use search_files or read_file to retrieve them later.

MEMORY.md shape

Active memory is MEMORY.md in the profile workspace:

  • A preamble (# Memory Log and ---) must stay intact.
  • Facts are bullets under dated sections: ## YYYY-MM-DD followed by - bullet text.

Copy bullet text verbatim from read_file output when editing. Do not paraphrase.

Archive file shape

Archive files live at memory-archive/YYYY-MM.md (year and month of the archive action, UTC).

New archive file template:

markdown
# Archived Memory

---

Each archive append starts with metadata comments, then the archived sections:

markdown
<!-- archived: 2026-07-12T10:30:00.000Z -->
<!-- reason: user asked to forget old job details -->

## 2026-06-29

- Old preference.

Preserve each bullet's original ## YYYY-MM-DD section header from MEMORY.md.

Workflow

  1. read_file MEMORY.md and identify the exact - bullet lines to archive.
  2. Confirm targets with the user when the request is ambiguous.
  3. Choose memory-archive/{YYYY-MM}.md for the current month.
  4. Check whether the archive file exists before calling read_file. With bash, run if test -f memory-archive/{YYYY-MM}.md; then printf 'exists\n'; else printf 'missing\n'; fi in the active profile workspace, replacing {YYYY-MM} with the chosen month. A missing file (or missing memory-archive/ directory) is normal for the first archive:
    • If the result is exists, read_file the archive to preserve its contents.
    • If the result is missing, skip the read and create the archive in step 5.
    • If bash is unavailable, use read_file; treat only a file-not-found result as a new archive. Stop on permission or other errors rather than overwriting an unread archive.
  5. Append to the archive first (reduces data-loss risk):
    • If the file does not exist, write_file with the template plus the append block.
    • If it exists, edit_file to append after the existing content (or write_file the full merged content).
  6. edit_file MEMORY.md to remove each archived - bullet line.
  7. Remove any ## YYYY-MM-DD section that has no bullets left.
  8. Re-read MEMORY.md and the archive file to verify the result.
  9. If archiving was to free space for new memories, confirm MEMORY.md is under 4096 bytes before retrying with update-profile-memory.

When edit_file reports a missing, duplicate, or overlapping match, read_file again and issue a corrected edit with exact text.

Legacy layout

If you find archives under data/memory-archive/ instead of memory-archive/, use the top-level memory-archive/ path for new writes.

Frequently asked questions

What does the Archive Profile Memory AI skill do?

Archive or clean up facts from active MEMORY.md into memory-archive/ without deleting them. Use when the user wants to forget, remove, tidy old memory, or free space for new memories.

Why use Archive Profile Memory on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ahmadrosid/nakama/tree/main/packages/core/src/skills/bundled/archive-profile-memory. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Archive Profile 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 Archive Profile Memory?

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

Is the Archive Profile Memory AI skill free?

Yes. It is published on GitHub by ahmadrosid 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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