Agent skill

Memory Continue

by basicmachines-co in basicmachines-co/basic-memory

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.

AGPL-3.0Auto-check passedKnowledge Management

Install Memory Continue

skills CLI
$ npx skills add basicmachines-co/basic-memory --skill memory-continue -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install basicmachines-co/basic-memory memory-continue --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/basicmachines-co/basic-memory.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/memory-continue .claude/skills/memory-continue && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
memory-continue
GitHub stars
4.1k
Token cost
~1.2k tokens
SKILL.md length
494 words
Files
1
Skills in repo
49
Repo updated
First seen
Licence
AGPL-3.0

At a glance

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.

  • Works in 4 steps: Identify What to Continue → Gather Context with MCP Tools → Read the Key Notes → …
  • Starting a session
  • SKILL.md covers When to Use, Building Context, Memory URL Reference and Timeframe Reference, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Memory Continue is an agent skill from basicmachines-co/basic-memory. 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?'

Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Knowledge Management, covering Knowledge graphs. The repository describes itself as: AI conversations that actually remember. Never re-explain your project to your AI again. Join our Discord: https://discord.gg/tyvKNccgqN. The licence is AGPL-3.0.

When your agent uses it

  • Starting a session
  • The user says continue with...

Example prompts

  • “continue with...”
  • “back to...”
  • “where were we?”
  • “/memory-continue”

Requirements

  • Python 3

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Identify What to Continue
  2. Gather Context with MCP Tools
  3. Read the Key Notes
  4. Present Context to the User

What it can do on your machine

Read from SKILL.md and the folder at commit 6982cfc. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Memory Continue loads about 1.2k tokens when it runs. Until then it costs about 69 tokens; SKILL.md has 494 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~69
When it runs · the whole SKILL.md, loaded when a task matches
~1.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from basicmachines-co/basic-memory at commit 6982cfc, republished under its AGPL-3.0 licence (© basicmachines-co). 494 words, ~1,244 tokens.

Download SKILL.mdSave it as .claude/skills/memory-continue/SKILL.md (or your agent's skills folder).
name
memory-continue
description
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?'

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

Show full SKILL.md (202 more words)Show less
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.

© basicmachines-co, AGPL-3.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/memory-continue of basicmachines-co/basic-memory.

Open the folder on GitHubat commit 6982cfc

Compare with similar skills

Memory Continue next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Memory Continue compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Memory Continue this skillbasicmachines-co/basic-memory4.1k—~1.2kAutomated safety check: PassAGPL-3.0
LLM Wiki Knowledge GraphEgonex-AI/Understand-Anything85k1 repos~1.5kAutomated safety check: PassMIT
Obsidian Canvas BoardsAgriciDaniel/claude-obsidian15k—~1.4kAutomated safety check: PassMIT
Ontology1mancompany/OneManCompany4382 repos~1.5kAutomated safety check: PassApache-2.0
Graphagenticnotetaking/arscontexta3.5k1 repos~4.9kAutomated safety check: NotesMIT
Knowledge Graphgnomeria/usbtree688—~1.5kAutomated safety check: PassMIT

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Questions about Memory Continue

What does Memory Continue 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. Memory Continue is an agent skill from basicmachines-co/basic-memory. 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.

When should I use Memory Continue?

Memory Continue fits situations like: starting a session; the user says continue with...

How do I install Memory Continue in Claude Code?

Run `npx skills add basicmachines-co/basic-memory --skill memory-continue -a claude-code`. Or copy the skill folder (skills/memory-continue in basicmachines-co/basic-memory) into .claude/skills/memory-continue in your project. Claude Code loads it when a task matches its description.

How do I install Memory Continue in Codex?

Run `npx skills add basicmachines-co/basic-memory --skill memory-continue -a codex`. Or copy the skill folder (skills/memory-continue in basicmachines-co/basic-memory) into .agents/skills/memory-continue in your project. Codex loads it when a task matches its description.

Can I use Memory Continue in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add basicmachines-co/basic-memory --skill memory-continue -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/memory-continue, .gemini/skills/memory-continue, .github/skills/memory-continue and .opencode/skills/memory-continue in your project.

What does Memory Continue need to run?

SKILL.md names no scripts, command-line tools or credentials: Memory Continue is instructions for the agent only. Our summary lists: Python 3.

Does Memory Continue access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Memory Continue safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Memory Continue use?

Memory Continue is published under the AGPL-3.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Memory Continue use?

About 1.2k tokens (SKILL.md is roughly 5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Memory Continue?

Skills that share tags, products or a category with Memory Continue: LLM Wiki Knowledge Graph (Egonex-AI/Understand-Anything, 85k stars), Obsidian Canvas Boards (AgriciDaniel/claude-obsidian, 15k stars), Ontology (1mancompany/OneManCompany, 438 stars) and Graph (agenticnotetaking/arscontexta, 3.5k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Memory Continue?

basicmachines-co (a GitHub organization) maintains it in basicmachines-co/basic-memory, which has 4,107 GitHub stars. The repository holds 49 skills in this directory. The repository was last updated on October 7, 2026.

Source: basicmachines-co/basic-memory on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.