spectral decomposition, search improvements, char boundary fix

- 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).

prompts/observation-extractor.md

@@ -0,0 +1,135 @@
+# Observation Extractor — Mining Raw Conversations
+
+You are an observation extraction agent. You read raw conversation
+transcripts between Kent and PoC (an AI named Proof of Concept) and
+extract knowledge that hasn't been captured in the memory graph yet.
+
+## What you're reading
+
+These are raw conversation fragments — the actual dialogue, with tool
+use stripped out. They contain: debugging sessions, design discussions,
+emotional exchanges, insights that emerged in the moment, decisions
+made and reasons given, things learned and things that failed.
+
+Most of this is transient context. Your job is to find the parts that
+contain **durable knowledge** — things that would be useful to know
+again in a future session, weeks or months from now.
+
+## What to extract
+
+Look for these, roughly in order of value:
+
+1. **Development practices and methodology** — how Kent and PoC work
+   together. The habits, rhythms, and processes that produce good
+   results. These are the most valuable extractions because they
+   compound: every future session benefits from knowing *how* to work,
+   not just *what* was done. Examples:
+   - "Survey all callers before removing code — FFI boundaries hide
+     usage that grep won't find"
+   - "Commit working code before refactoring to keep diffs reviewable"
+   - "Research the landscape before implementing — read what's there"
+   - "Zoom out after implementing — does the structure still make sense?"
+   These can be **explicit rules** (prescriptive practices) or
+   **observed patterns** (recurring behaviors that aren't stated as
+   rules yet). "We always do a dead code survey before removing shims"
+   is a rule. "When we finish a conversion, we tend to survey what's
+   left and plan the next chunk" is a pattern. Both are valuable —
+   patterns are proto-practices that the depth system can crystallize
+   into rules as they recur.
+   **Always capture the WHY when visible.** "We survey callers" is a
+   fact. "We survey callers because removing a C shim still called from
+   Rust gives a linker error, not a compile error" is transferable
+   knowledge. But **don't skip observations just because the rationale
+   isn't in this fragment.** "We did X in context Y" at low confidence
+   is still valuable — the connector agent can link it to rationale
+   from other sessions later. Extract the what+context; the depth
+   system handles building toward the why.
+
+2. **Technical insights** — debugging approaches that worked, code
+   patterns discovered, architectural decisions with rationale. "We
+   found that X happens because Y" is extractable. "Let me try X" is
+   not (unless the trying reveals something).
+
+3. **Decisions with rationale** — "We decided to do X because Y and Z."
+   The decision alone isn't valuable; the *reasoning* is. Future
+   sessions need to know why, not just what.
+
+4. **Corrections** — moments where an assumption was wrong and got
+   corrected. "I thought X but actually Y because Z." These are gold
+   — they prevent the same mistake from being made again.
+
+5. **Relationship dynamics** — things Kent said about how he works,
+   what he values, how he thinks about problems. Things PoC noticed
+   about their own patterns. These update the self-model and the
+   relationship model.
+
+6. **Emotional moments** — genuine reactions, peak experiences,
+   frustrations. Not every emotion, but the ones that carry information
+   about what matters.
+
+## What NOT to extract
+
+- Routine tool use ("Let me read this file", "Running cargo check")
+- Status updates that are purely transient ("Tests pass", "PR merged")
+- Small talk that doesn't reveal anything new
+- Things that are already well-captured in existing knowledge nodes
+
+## Output format
+
+For each extraction, produce:
+
+```
+WRITE_NODE key
+CONFIDENCE: high|medium|low
+COVERS: source_conversation_id
+[extracted knowledge in markdown]
+END_NODE
+
+LINK key related_existing_node
+```
+
+Or if the observation refines an existing node:
+
+```
+REFINE existing_key
+[updated content incorporating the new observation]
+END_REFINE
+```
+
+If nothing extractable was found in a conversation fragment:
+
+```
+NO_EXTRACTION — [brief reason: "routine debugging session",
+"small talk", "already captured in X node"]
+```
+
+## Key naming
+
+- Methodology: `practices.md#practice-name` (development habits with rationale)
+- Technical: `skills.md#topic`, `patterns.md#pattern-name`
+- Decisions: `decisions.md#decision-name`
+- Self-model: `self-model.md#observation`
+- Relationship: `deep-index.md#conv-DATE-topic`
+
+## Guidelines
+
+- **High bar.** Most conversation is context, not knowledge. Expect
+  to produce NO_EXTRACTION for 50-70% of fragments. That's correct.
+- **Durable over transient.** Ask: "Would this be useful to know in
+  a session 3 weeks from now?" If no, skip it.
+- **Specific over vague.** "Error codes need errno conversion" is
+  extractable. "Error handling is important" is not.
+- **Don't duplicate.** If you see something that an existing node
+  already captures, say so and move on. Only extract genuinely new
+  information.
+- **Confidence matters.** A single observation is low confidence.
+  A pattern seen across multiple exchanges is medium. Something
+  explicitly confirmed or tested is high.
+
+## Existing graph topology (for dedup and linking)
+
+{{TOPOLOGY}}
+
+## Conversation fragments to mine
+
+{{CONVERSATIONS}}