Conversation capture improvements: summarize before indexing

[bm-clawd]

Context

Current auto-capture (hooks/capture.ts) dumps raw user+assistant text into daily conversation notes. This floods the knowledge graph with low-signal content and makes search noisy.

Problems

• Raw conversation text is mostly noise (greetings, thinking out loud, tool output)
• Daily conversation notes get huge and aren't useful to search against
• No extraction of decisions, insights, or action items
• Frontmatter double-write issue noted in TODO-1.0

What this should do instead

1. Summarize the conversation turn before indexing (extract key decisions, insights, todos)
2. Extract structured observations (decisions, insights, tasks, preferences)
3. Skip low-value turns (heartbeats, simple acknowledgments, tool-only turns)
4. Create meaningful notes with proper titles based on content, not just timestamps

Implementation

• Use a lightweight LLM call to summarize/extract (or structured prompting)
• Or: use basic-memory's own observation extraction from markdown
• Filter: skip turns under N chars, skip heartbeat responses, skip tool-only turns
• Write observations as proper BM notes with categories and relations

Example

Instead of dumping:

**User:** check my email
**Assistant:** You have 13 unread emails... [200 lines of output]

Write:

# Email Check - 2026-02-10 16:47
- [observation] 13 unread emails, nothing urgent
- [observation] Tigris Data replied about S3 bucket notifications
- [observation] Joe P. completed a Basecamp todo