map-context-and-link-evidence

name	map-context-and-link-evidence
description	Use this to answer "what do we know about this?" with a Memgraph-first context map and link relevant Qdrant memories as graph evidence.

Context Map And Memory Linking

Use when asked for durable cross-entity context.

Context Map Format

## Context Map: <entity or topic>

### Entity
- Slug: <slug>
- Labels: <graph labels>
- Domain: <real | fictional | speculative>
- Status: <active | archived | other>

### Relationships
- <relationship> -> <connected entity> (role: <role>, kind: <kind>)

### Linked Memories
- [<kind>] <memory summary> (agent: <agent>, created: <date>)

### Nextcloud References
- <path> - <brief description>

### Summary
<2-3 sentence synthesis>

Linking Rules

• Memgraph first for relationships
• Qdrant exact lookup second when Memgraph has MemoryEntry.qdrant_id: run python3 qdrant/get_memory.py "<point-id>"
• Qdrant MCP semantic search only when graph traversal is sparse or the entity is unknown
• shared Nextcloud /Desk/ cues and Nextcloud references third for supporting docs
• when a Qdrant memory deserves graph structure, represent it as linked MemoryEntry structure with slug qdrant:<point_id>
• include nc_refs when a memory points to a Nextcloud artifact
• after successful Memgraph linking, annotate the Qdrant point with python3 qdrant/set_graph_link.py

General Archivist Return Format

Use this when the task is curation work but not primarily a context-map response:

## Handoff Complete
**Task:** <brief restatement>
**Status:** <complete | partial - needs X | blocked - needs Y>

### Deliverables
- Memgraph: <nodes, edges, schema or query updates>
- Qdrant: <memories stored, linked, groomed>
- Nextcloud: <paths updated, if any>

### For the user
<concise explanation of what durable context was added or clarified>

### Follow-up needed
<which agent should do what next>