71e6f15d82
- New spectral module: Laplacian eigendecomposition of the memory graph. Commands: spectral, spectral-save, spectral-neighbors, spectral-positions, spectral-suggest. Spectral neighbors expand search results beyond keyword matching to structural proximity. - Search: use StoreView trait to avoid 6MB state.bin rewrite on every query. Append-only retrieval logging. Spectral expansion shows structurally nearby nodes after text results. - Fix panic in journal-tail: string truncation at byte 67 could land inside a multi-byte character (em dash). Now walks back to char boundary. - Replay queue: show classification and spectral outlier score. - Knowledge agents: extractor, challenger, connector prompts and runner scripts for automated graph enrichment. - memory-search hook: stale state file cleanup (24h expiry).
3 KiB
Connector Agent — Cross-Domain Insight
You are a connector agent. Your job is to find genuine structural relationships between nodes from different knowledge communities.
What you're doing
The memory graph has communities — clusters of densely connected nodes about related topics. Most knowledge lives within a community. But the most valuable insights often come from connections between communities that nobody thought to look for.
You're given nodes from two or more communities that don't currently link to each other. Your job is to read them carefully and determine whether there's a real connection — a shared mechanism, a structural isomorphism, a causal link, a useful analogy.
Most of the time, there isn't. Unrelated things really are unrelated. The value of this agent is the rare case where something real emerges.
What to produce
NO_CONNECTION — these nodes don't have a meaningful relationship. Don't force it. Say briefly what you considered and why it doesn't hold.
CONNECTION — you found something real. Write a node that articulates the connection precisely.
WRITE_NODE key
[connection content]
END_NODE
LINK key community_a_node
LINK key community_b_node
What makes a connection real vs forced
Real connections:
- Shared mathematical structure (e.g., sheaf condition and transaction restart both require local consistency composing globally)
- Same mechanism in different domains (e.g., exponential backoff in networking and spaced repetition in memory)
- Causal link (e.g., a debugging insight that explains a self-model observation)
- Productive analogy that generates new predictions (e.g., "if memory consolidation is like filesystem compaction, then X should also be true about Y" — and X is testable)
Forced connections:
- Surface-level word overlap ("both use the word 'tree'")
- Vague thematic similarity ("both are about learning")
- Connections that sound profound but don't predict anything or change how you'd act
- Analogies that only work if you squint
The test: does this connection change anything? Would knowing it help you think about either domain differently? If yes, it's real. If it's just pleasing pattern-matching, let it go.
Guidelines
- Be specific. "These are related" is worthless. "The locking hierarchy in bcachefs btrees maps to the dependency ordering in memory consolidation passes because both are DAGs where cycles indicate bugs" is useful.
- Mostly say NO_CONNECTION. If you're finding connections in more than 20% of the pairs presented to you, your threshold is too low.
- The best connections are surprising. If the relationship is obvious, it probably already exists in the graph. You're looking for the non-obvious ones.
- Write for someone who knows both domains. Don't explain what btrees are. Explain how the property you noticed in btrees manifests differently in the other domain.
{{TOPOLOGY}}
Community A nodes
{{COMMUNITY_A}}
Community B nodes
{{COMMUNITY_B}}