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Memory Lifecycle

OrganizationPopular
basicmachines-co
memory-lifecycle

Manage entity status transitions in Basic Memory: archive completed work, move notes between status folders, update frontmatter, and handle edge cases. Use when marking items complete, archiving old entities, or managing any folder-based status workflow.

Overview

Publisherbasicmachines-co
Repositorybasic-memory
Skill namememory-lifecycle
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 Lifecycle 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-lifecycle .claude/skills/memory-lifecycle
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Memory Lifecycle 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 Lifecycle 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 Lifecycle 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 Lifecycle

Manage how entities move through status stages in Basic Memory. The core principle: archive, never delete. Completed work is valuable context — move it out of the active view, but keep it in the knowledge graph.

When to Use

  • User says something is "done", "finished", "completed", "submitted", "missed", or "cancelled"
  • Moving entities between status folders (active → archive, pipeline → active, etc.)
  • Reverting a mistaken completion
  • Periodic cleanup of stale active items

Core Principle: Archive, Never Delete

Deleting a note removes it from the knowledge graph — all its observations, relations, and history disappear. Archiving preserves everything while signaling the entity is no longer active.

# Good — entity stays in the knowledge graph
move_note → active/ to archive/

# Bad — knowledge is lost
delete_note

The only exception: notes created by mistake (typos, true duplicates) can be deleted.

Folder Conventions

Organize entities by status using folders. The exact folder names depend on your domain, but follow a consistent pattern:

entities/
  active/          # Currently relevant, in-progress
  archive/         # Completed, no longer active, but worth keeping
  pipeline/        # Future items, not yet started

For tasks specifically:

tasks/
  active/          # Work in progress
  completed/       # Finished work

For any entity type with a clear lifecycle:

[type]/
  active/          # Current
  [end-state]/     # Whatever "done" means for this type

Pick folder names that match your domain. The pattern matters more than the specific names.

Status Detection

When the user mentions completion or status change, extract the intent:

SignalStatusAction
"finished", "done", "completed", "shipped"CompleteMove to archive/completed folder
"submitted", "sent", "delivered"CompleteMove to archive/completed folder
"missed", "passed", "skipped", "expired"MissedMove to archive or missed folder
"cancelled", "abandoned", "killed"CancelledMove to archive folder
"paused", "on hold", "deferred"PausedUpdate frontmatter status, keep in place
"restarting", "reopening", "reviving"ReactivateMove back to active folder

Workflow

1. Find the Entity

Search Basic Memory with multiple variations to locate the entity:

python
search_notes(query="quarterly report")
search_notes(query="Q1 report")

If multiple matches come back, present options and ask which one.

If no match is found, ask for clarification — don't guess.

2. Move the File

Use move_note to relocate the entity to the appropriate status folder:

python
move_note(
  identifier="tasks/active/quarterly-report",
  destination_path="tasks/completed/quarterly-report.md"
)

The permalink stays the same, so all existing [[wiki-links]] and memory:// URLs continue to resolve.

3. Update Frontmatter

After moving, update the status in frontmatter to match:

python
edit_note(
  identifier="quarterly-report",
  operation="find_replace",
  find_text="status: active",
  content="status: completed"
)

If there's a completion date field, set it:

python
edit_note(
  identifier="quarterly-report",
  operation="find_replace",
  find_text="completed:",
  content="completed: 2026-02-22"
)

4. Confirm

Report what was done concisely:

Marked complete: Quarterly Report
  Moved to: tasks/completed/quarterly-report.md
  Status: completed

Edge Cases

Already Archived

If the entity is already in an archive/completed folder, notify the user:

"Quarterly Report is already in tasks/completed/. Want me to update anything on it?"

Partial Completion

Sometimes only part of an entity is done. Don't move it — instead, update observations or status notes within the entity to reflect partial progress.

Revert / Reactivate

If something was archived by mistake, move it back:

python
move_note(
  identifier="tasks/completed/quarterly-report",
  destination_path="tasks/active/quarterly-report.md"
)

edit_note(
  identifier="quarterly-report",
  operation="find_replace",
  find_text="status: completed",
  content="status: active"
)

Status Without Movement

Some status changes don't require a folder move — "paused" or "blocked" items often stay in active/ with just a frontmatter update. Reserve folder moves for terminal or major state transitions.

Relationship to Other Skills

  • memory-tasks: Tasks are a specific lifecycle case. This skill covers the general pattern; memory-tasks covers task-specific fields (steps, current_step, context).
  • memory-notes: Use search-before-create (from memory-notes) to find the entity before transitioning it.
  • memory-defrag: Periodic defrag can identify stale active items that should be archived.

Guidelines

  • Archive, never delete. The knowledge graph benefits from historical context.
  • Move first, then update frontmatter. This order ensures the file is in the right place even if the edit step fails.
  • Permalinks survive moves. Links to the entity keep working after a move_note.
  • Be concise in confirmations. The user knows their system — just report what changed.
  • Ask when ambiguous. If multiple entities match or the target folder isn't clear, ask rather than guess.
  • Batch operations are fine. If the user says "archive all completed tasks", find them all, confirm the list, then move them in sequence.

Frequently asked questions

What does the Memory Lifecycle AI skill do?

Manage entity status transitions in Basic Memory: archive completed work, move notes between status folders, update frontmatter, and handle edge cases. Use when marking items complete, archiving old entities, or managing any folder-based status workflow.

Why use Memory Lifecycle on TypingMind?

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

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

Which AI models can use Memory Lifecycle?

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 Lifecycle?

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

Is the Memory Lifecycle 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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