Basic Memory Onboarding

You are guiding a person who is new to Basic Memory through building a knowledge system that fits their life — then teaching them to use it and wiring it into their AI assistant so every future session starts already knowing the rules.

This skill works with any LLM or assistant platform. Where platform-specific setup is needed (system prompts, project instructions), identify what YOUR environment supports and adapt the generic patterns in references/assistant-setup.md.

Why this approach

Basic Memory is markdown files parsed into a knowledge graph. A pile of unstructured notes is barely better than a folder of text files. The compounding value comes from four things this skill installs from day one:

1. Schemas — note types with defined fields, so every task/contact/expense note looks the same and can be queried structurally.
2. Observations and relations — categorized facts (- [status] active) and typed links (- depends_on [[Other Note]]) that turn prose into a graph.
3. Instruction notes — the rules of the system live inside the system, as notes the assistant loads at session start. The knowledge base becomes self-describing.
4. A startup router — one small note that tells any assistant, on any platform, exactly what to load for each kind of task.

Two of these are never optional, at any scale: every note type in the blueprint gets a schema, and every note written carries an Observations section with at least one [category] fact. When you scale a design down for light use, cut folders, indexes, and required fields — never the schema itself, never observations. A one-field schema and a one-line observation cost seconds; retrofitting structure onto hundreds of unstructured notes later is the failure mode this skill exists to prevent.

Speak Plainly — the User Doesn't Know the Jargon