Memory Ingest

Turn raw, unstructured input into structured Basic Memory entities. Meeting transcripts, conversation logs, pasted documents, email threads — anything with information worth preserving gets parsed, cross-referenced against existing knowledge, and written as proper notes.

When to Use

• - User pastes a meeting transcript or conversation log
• User says "process these notes" or "add this to Basic Memory"
• User pastes a document, article, or email for knowledge extraction
• Any time raw external text needs to become structured knowledge

Workflow Overview

CODEBLOCK0

Step 1: Parse Raw Input

Read the pasted content and identify its structure:

• - Format: Meeting transcript, email thread, conversation log, article, freeform notes
• Date: When this happened (extract from content or ask)
• Participants: Who was involved (names, roles, organizations)
• Sections: Any existing structure (headings, speaker labels, timestamps)

Don't rewrite or summarize the source content. Preserve it verbatim in the note — you'll add structured observations alongside it.

Step 2: Extract Entities

Scan the content for entities worth tracking in the knowledge graph:

Infer type from context. If someone is introduced as "CTO of Acme Corp", that's both a Person and an Organization entity. If a technology is discussed in depth, it might warrant a Concept entity.

Exclude noise. Not every name mentioned is worth an entity. Filter for:

• - People with substantive roles or interactions (not passing mentions)
• Organizations discussed in business/technical context
• Topics with enough detail to warrant their own note

Step 3: Search Existing Entities

For each extracted entity, search Basic Memory with multiple query variations:

CODEBLOCK1

Classify each entity as:

• - Existing — found in Basic Memory. Will link to it with [[wiki-link]].
• Proposed — not found. Will propose creation pending approval.

Step 4: Research New Entities (Optional)

For proposed entities where more context would be valuable, do a brief web search (2-3 queries max per entity):

• - Organizations: What they do, size, public/private, key products
• People: Current role, background, expertise
• Topics: Brief definition, relevance

Use hedging language ("appears to be", "estimated", "based on public information"). Never fabricate details.

This step is optional — skip it if the source material provides enough context, or if the user is in a hurry. See the memory-research skill for deeper research workflows.

Step 5: Present Entity Proposal

Before creating anything, present what you found and what you'd like to create:

CODEBLOCK2

Include enough context with each proposed entity for the user to make a quick decision.

Step 6: Create the Source Note

Create the primary note for the ingested content. This is the "record of what happened" — it preserves the raw material and adds structured metadata.

Meeting / Conversation Note

CODEBLOCK3

Document / Article Note

CODEBLOCK4

Observation Categories

Use categories that capture the nature of the information. Common categories for ingested content:

Invent categories as needed — these are suggestions, not a fixed list.

Step 7: Create Approved Entities

For each entity the user approved, create a structured note. Match the entity type to an appropriate template.

Person

CODEBLOCK5

Organization

CODEBLOCK6

Concept / Topic

CODEBLOCK7

Adapt templates to your domain. The key elements are: type and tags as parameters, an overview section, observations with categories, and relations linking back to the source.

Step 8: Extract Action Items

Review the source content for commitments and follow-ups:

CODEBLOCK8

If using the memory-tasks skill, create Task notes for your action items. Otherwise, capture them as observations in the source note.

Guidelines

• - Preserve source content verbatim. The original text is the ground truth. Structure and observations are your interpretation layered on top.
• Search before creating. Always check if entities already exist (see memory-notes search-before-create pattern). Update existing entities with new information rather than creating duplicates.
• Get approval for new entities. Present proposed entities and let the user decide which to create. Don't silently populate the knowledge graph.
• Infer, don't interrogate. Extract entity types and relationships from context. Only ask the user when genuinely ambiguous.
• Be selective about entities. Not every name mentioned deserves its own note. Focus on entities the user will want to reference again.
• Use hedging for researched info. Web research supplements — don't present it as fact. "Appears to be", "estimated", "based on public information".
• Link everything back. Every created entity should relate back to the source note. The source note should link to all entities discussed.
• Prose and observations together. Notes work best with both narrative context and structured observations. Prose gives meaning and tells the story; observations make individual facts searchable. Use the body for context, then distill key facts into categorized observations.