memory-continue

Picks up where you left off by finding your previous notes and context. Use when returning to a project after time away.

Installation
Run `npx skills add "https://github.com/basicmachines-co/basic-memory" --skill "memory-continue"` to install this skill, then follow its SKILL.md instructions for my next request.

Paste this into Claude Code, Cursor, or any agent that can run commands.

SKILL.mdShow the author's original SKILL.md
---
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:

| Timeframe | Meaning |
|-----------|---------|
| `"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.

Mirrored from the author's public source. Install counts from the open skills registry.

The systems behind these skills get built for partners every week.

Partner with us