Memory Continue

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

作者 basicmachines-cob941460b4d99480fa2eb1a230a62d2847927ea05无许可证4.1K 个星标收录于 2026年10月9日更新于 2026年10月9日仓库今天更新

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 生成的概览

从 Basic Memory 知识图谱重建先前会话的上下文,让智能体能够接着之前的工作继续。

功能
该技能指导智能体在会话开始时从 Basic Memory 知识图谱重建上下文。它说明如何使用 build_context、recent_activity、search_notes、read_note 和 list_memory_projects 等 MCP 工具,并结合 memory:// URL、通配符和自然语言时间范围来查找并阅读相关笔记。它还提供恢复规格或项目、继续一般工作以及跟进某个主题的操作手册,最后向用户呈现工作状态、近期变更和待办事项的摘要。
适用场景
适用于开始新会话并需要接着之前工作继续的场景,或用户说出“continue with...”“back to...”“where were we?”之类的话时。也适用于跨多个会话的任务或关于先前对话内容的提问。
运行要求
需要具备 Basic Memory 知识图谱及其 MCP 工具(build_context、recent_activity、search_notes、read_note、list_memory_projects)。不附带脚本,仅为说明文档。

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.

来源与署名

来源:basicmachines-co/basic-memory位于skills/memory-continue提交b941460

许可证: 无许可证

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