Memory Continue

basicmachines-co/basic-memory/skills/memory-continue

作者 basicmachines-cob941460b4d99480fa2eb1a230a62d2847927ea05無授權條款4.1K 個星標收錄於 2026年10月9日更新於 2026年10月9日儲存庫今天更新

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?'

AI 產生的概覽

從 Basic Memory 知識圖譜重建先前工作階段的脈絡,讓代理能接續之前的工作。

功能
此技能引導代理在工作階段開始時,從 Basic Memory 知識圖譜重建脈絡。它說明如何使用 build_context、recent_activity、search_notes、read_note 與 list_memory_projects 等 MCP 工具,並搭配 memory:// URL、萬用字元與自然語言時間範圍來尋找並閱讀相關筆記。它也提供接續規格或專案、繼續一般工作以及追蹤某個主題的操作手冊,最後向使用者呈現工作狀態、近期變更與待辦事項的摘要。
適用情境
適用於開始新的工作階段並需要接續先前工作的情況,或使用者說出「continue with...」「back to...」「where were we?」之類的話時。也適合橫跨多個工作階段的任務,或關於先前對話內容的提問。
執行需求
需要具備 Basic Memory 知識圖譜及其 MCP 工具(build_context、recent_activity、search_notes、read_note、list_memory_projects)。不附帶指令碼,僅為說明文件。

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 permalinkmemory://folder/*              # all notes in a foldermemory://specs/SPEC-24*        # pattern / prefix matchmemory://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 noteread_note(identifier="SPEC-24: Postgres Database Migration")
# 2. Pull in related context and recent changes via the graphbuild_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 activityrecent_activity(timeframe="3d")
# 2. Read notes from the recent sessions it surfacesread_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 topicsearch_notes(query="topic keywords")
# 2. Build context from the best match, following its relationsbuild_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.

來源與署名

來源:basicmachines-co/basic-memory位於skills/memory-continue提交b941460

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