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Memory Continue

OrganizationPopular
basicmachines-co
memory-continue

Resume prior work by rebuilding context from the Basic Memory knowledge graph — pick up where you left off using memory:// URLs, recent activity, and search. Use when starting a session or when the user says 'continue with...', 'back to...', or 'where were we?'

Overview

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

Use it in TypingMind

Enable Memory Continue 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 Continue 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 Continue 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 Continue

Resume previous work by reconstructing context from the Basic Memory knowledge graph, so the assistant can pick up across sessions instead of starting cold.

When to Use

  • Starting a new session and you need to pick up where you left off
  • The user references earlier work: "continue with...", "back to...", "where were we on...?"
  • You need context about an ongoing project or spec
  • The user asks about something discussed in a previous conversation
  • You're working on a task that spans multiple sessions

Building Context

1. Identify What to Continue

If it's unclear, ask:

  • What topic or project should you resume?
  • What timeframe matters?
  • Any specific aspect to focus on?

2. Gather Context with MCP Tools

Known topic — use build_context. Navigate the graph from a starting point, following relations outward:

python
build_context(
    url="memory://topic-or-note-name",
    depth=2,           # how many relation hops to follow
    timeframe="7d",    # bias toward recent changes
)

No clear starting point — use recent_activity. See what's changed and let it surface the thread:

python
recent_activity(timeframe="3d", depth=1)

Looking for something specific — use search_notes. Find candidate notes by keyword:

python
search_notes(query="async client refactor", page_size=10)

3. Read the Key Notes

Once you've identified the relevant notes, read them in full:

python
read_note(identifier="note-title-or-permalink")

4. Present Context to the User

Summarize what you found, incrementally:

  • Current state of the work
  • Recent changes or progress
  • Open items and next steps
  • Related context that might help

Memory URL Reference

build_context and read_note both accept memory:// URLs, which address notes by permalink and support wildcards for gathering groups of notes.

memory://note-title            # a single note by permalink
memory://folder/*              # all notes in a folder
memory://specs/SPEC-24*        # pattern / prefix match
memory://project/*/requirements # path wildcards

Use a specific note URL to anchor on one starting point; use a wildcard to pull in a whole folder or family of related notes at once.

Timeframe Reference

build_context and recent_activity accept natural-language timeframes:

TimeframeMeaning
"today"Current day
"yesterday"Previous day
"3d" or "3 days"Last 3 days
"1 week" or "7d"Last week
"2 weeks"Last 2 weeks
"1 month"Last month

Scenario Playbooks

Resuming a Spec or Project

python
# 1. Read the spec / project note
read_note(identifier="SPEC-24: Postgres Database Migration")

# 2. Pull in related context and recent changes via the graph
build_context(url="memory://SPEC-24*", timeframe="7d")

Then summarize: the goals, what's completed, what's pending, and any blockers or open decisions.

Continuing General Work

python
# 1. Check recent activity
recent_activity(timeframe="3d")

# 2. Read notes from the recent sessions it surfaces
read_note(identifier="relevant-note")

Then list the modified notes with brief descriptions and ask which thread to dive into.

Following Up on a Topic

python
# 1. Find the topic
search_notes(query="topic keywords")

# 2. Build context from the best match, following its relations
build_context(url="memory://found-note-permalink", depth=2)

Then present the full picture — the note plus its connected context.

Project Discovery

Project names are user-specific. To discover what's available before scoping a search or memory:// URL:

python
list_memory_projects()

In multi-project setups, prefix a memory:// URL with the project name (e.g. memory://research/papers/crdt) to scope it.

Guidelines

  1. Start broad, then narrow. Get an overview with recent_activity or a wildcard build_context, then drill into specific notes.
  2. Present incrementally. Share what you find as you go rather than holding everything until the end.
  3. Follow relations. The graph's connections are the point — build_context with depth surfaces context you wouldn't find by reading one note.
  4. Check multiple projects. Specs may live separately from implementation notes; discover projects with list_memory_projects.
  5. Confirm understanding. Verify the reconstructed context is what the user actually needs before acting on it.
  6. Capture new progress. As the resumed work advances, write it back to the graph (see the memory-notes skill) so the next session can continue too.

Frequently asked questions

What does the Memory Continue AI skill do?

Resume prior work by rebuilding context from the Basic Memory knowledge graph — pick up where you left off using memory:// URLs, recent activity, and search. Use when starting a session or when the user says 'continue with...', 'back to...', or 'where were we?'

Why use Memory Continue on TypingMind?

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

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

Which AI models can use Memory Continue?

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 Continue?

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

Is the Memory Continue 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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