| title | Teaching Your AI How to Remember |
|---|---|
| description | Pre-built skills that teach AI agents when and how to use Basic Memory effectively — not just tool access, but good habits. |
You gave your AI a knowledge base. It has the tools — write notes, search, build context. But does it actually use them well?
Probably not. At least, not consistently.
An AI with access to Basic Memory's MCP tools is like a new hire with access to the company wiki. They can use it. But without guidance, they'll write notes in five different formats, forget to link related concepts, and never think to review what they wrote last week. The tools are there. The habits aren't.
That's what skills fix.
Basic Memory Skills are markdown instruction files that teach AI agents when and how to use their memory tools. Not API docs. Not reference pages. Playbooks — "here's the workflow, here's when to use it, here are the patterns that work."
Install them with a single command:
npx skills add basicmachines-co/basic-memory-skillsYour agent reads the skill file, learns the pattern, and follows it. Claude Desktop, Claude Code, Cursor, Windsurf — anything that supports markdown-based skills.
The foundation:
memory-notes is the one you install first. It teaches proper note structure — frontmatter, semantic observation categories, relations with wiki-links, memory:// URL usage. This is the difference between an agent that dumps text into a file and an agent that builds a connected knowledge graph.
memory-tasks handles structured task tracking. AI context windows reset. Sessions end. Your agent loses its train of thought. Task notes externalize multi-step work so it survives compaction. The agent writes down where it is in a process, and picks up from that checkpoint next session.
memory-schema manages the lifecycle of structured note types. Infer a schema from what you've already written. Validate notes against it. Detect when patterns drift over time. This is how consistency scales — not by enforcing rules upfront, but by observing patterns and codifying them.
Knowledge maintenance:
memory-reflect is inspired by what sleep researchers call "memory consolidation." Your agent reviews recent conversations, extracts the important parts, and writes them into long-term storage. Run it daily. It's like journaling — the act of reviewing forces the agent to decide what matters.
memory-defrag is spring cleaning. Split a 500-line note into focused pieces. Merge three notes that are basically saying the same thing. Remove information that's no longer true. Run it weekly. Knowledge bases that never get reorganized become knowledge graveyards.
memory-lifecycle manages status transitions — moving notes from active to archive, tracking where things are in a workflow. The philosophy is "archive, never delete." Information is preserved through moves, not removal.
Advanced workflows:
memory-metadata-search teaches your agent to query by structured fields instead of guessing at text matches. Find all notes with status: active and priority: high. Filter by date ranges. Search by tags. Frontmatter becomes queryable.
memory-ingest handles external input — meeting transcripts, pasted documents, conversation logs. The agent parses unstructured text into properly formatted notes with observations, relations, and metadata. Bulk import without bulk cleanup.
memory-research sends the agent to the web and brings findings back as structured knowledge. Research a company, a technology, a person — the agent searches, synthesizes, and writes a note you can actually use later.
You don't need all nine. Start with what matches your workflow:
Developers: memory-notes + memory-tasks + memory-schema — core note writing, task tracking across sessions, consistent project documentation.
Researchers: memory-notes + memory-research + memory-ingest — capture web research and process external documents into structured knowledge.
Knowledge maintenance: memory-notes + memory-defrag + memory-lifecycle + memory-reflect — keep your knowledge base healthy over time.
Everything: All nine skills for comprehensive memory management. Install the full collection and let your agent use what's relevant.
The gap between "AI has access to tools" and "AI uses tools effectively" is larger than most people realize. An agent without skills will use write_note to create flat, unstructured text dumps. An agent with the memory-notes skill creates notes with proper observations, semantic categories, and wiki-link relations that connect to the rest of your knowledge graph.
Same tool. Dramatically different output. The skill is the difference.
This is also how Basic Memory stays useful as your knowledge base grows. A hundred notes are easy to manage. A thousand notes need structure, consistency, and maintenance. Skills teach your agent the habits that keep a large knowledge base navigable instead of overwhelming.
Install a single skill:
npx skills add basicmachines-co/basic-memory-skills --skill memory-tasksInstall everything:
npx skills add basicmachines-co/basic-memory-skillsBasic Memory is local-first AI knowledge infrastructure. Plain text files you can read, edit, and keep forever. Get started →