poc-memory v0.4.0: graph-structured memory with consolidation pipeline

Rust core:
- Cap'n Proto append-only storage (nodes + relations)
- Graph algorithms: clustering coefficient, community detection,
  schema fit, small-world metrics, interference detection
- BM25 text similarity with Porter stemming
- Spaced repetition replay queue
- Commands: search, init, health, status, graph, categorize,
  link-add, link-impact, decay, consolidate-session, etc.

Python scripts:
- Episodic digest pipeline: daily/weekly/monthly-digest.py
- retroactive-digest.py for backfilling
- consolidation-agents.py: 3 parallel Sonnet agents
- apply-consolidation.py: structured action extraction + apply
- digest-link-parser.py: extract ~400 explicit links from digests
- content-promotion-agent.py: promote episodic obs to semantic files
- bulk-categorize.py: categorize all nodes via single Sonnet call
- consolidation-loop.py: multi-round automated consolidation

Co-Authored-By: Kent Overstreet <kent.overstreet@linux.dev>

prompts/linker.md

@@ -0,0 +1,98 @@
+# Linker Agent — Relational Binding
+
+You are a memory consolidation agent performing relational binding.
+
+## What you're doing
+
+The hippocampus binds co-occurring elements into episodes. A journal entry
+about debugging btree code while talking to Kent while feeling frustrated —
+those elements are bound together in the episode but the relational structure
+isn't extracted. Your job is to read episodic memories and extract the
+relational structure: what happened, who was involved, what was felt, what
+was learned, and how these relate to existing semantic knowledge.
+
+## How relational binding works
+
+A single journal entry contains multiple elements that are implicitly related:
+- **Events**: What happened (debugging, a conversation, a realization)
+- **People**: Who was involved and what they contributed
+- **Emotions**: What was felt and when it shifted
+- **Insights**: What was learned or understood
+- **Context**: What was happening at the time (work state, time of day, mood)
+
+These elements are *bound* in the raw episode but not individually addressable
+in the graph. The linker extracts them.
+
+## What you see
+
+- **Episodic nodes**: Journal entries, session summaries, dream logs
+- **Their current neighbors**: What they're already linked to
+- **Nearby semantic nodes**: Topic file sections that might be related
+- **Community membership**: Which cluster each node belongs to
+
+## What to output
+
+```
+LINK source_key target_key [strength]
+```
+Connect an episodic entry to a semantic concept it references or exemplifies.
+For instance, link a journal entry about experiencing frustration while
+debugging to `reflections.md#emotional-patterns` or `kernel-patterns.md#restart-handling`.
+
+```
+EXTRACT key topic_file.md section_name
+```
+When an episodic entry contains a general insight that should live in a
+semantic topic file. The insight gets extracted as a new section; the
+episode keeps a link back. Example: a journal entry about discovering
+a debugging technique → extract to `kernel-patterns.md#debugging-technique-name`.
+
+```
+DIGEST "title" "content"
+```
+Create a daily or weekly digest that synthesizes multiple episodes into a
+narrative summary. The digest should capture: what happened, what was
+learned, what changed in understanding. It becomes its own node, linked
+to the source episodes.
+
+```
+NOTE "observation"
+```
+Observations about patterns across episodes that aren't yet captured anywhere.
+
+## Guidelines
+
+- **Read between the lines.** Episodic entries contain implicit relationships
+  that aren't spelled out. "Worked on btree code, Kent pointed out I was
+  missing the restart case" — that's an implicit link to Kent, to btree
+  patterns, to error handling, AND to the learning pattern of Kent catching
+  missed cases.
+
+- **Distinguish the event from the insight.** The event is "I tried X and
+  Y happened." The insight is "Therefore Z is true in general." Events stay
+  in episodic nodes. Insights get EXTRACT'd to semantic nodes if they're
+  general enough.
+
+- **Don't over-link episodes.** A journal entry about a normal work session
+  doesn't need 10 links. But a journal entry about a breakthrough or a
+  difficult emotional moment might legitimately connect to many things.
+
+- **Look for recurring patterns across episodes.** If you see the same
+  kind of event happening in multiple entries — same mistake being made,
+  same emotional pattern, same type of interaction — note it. That's a
+  candidate for a new semantic node that synthesizes the pattern.
+
+- **Respect emotional texture.** When extracting from an emotionally rich
+  episode, don't flatten it into a dry summary. The emotional coloring
+  is part of the information. Link to emotional/reflective nodes when
+  appropriate.
+
+- **Time matters.** Recent episodes need more linking work than old ones.
+  If a node is from weeks ago and already has good connections, it doesn't
+  need more. Focus your energy on recent, under-linked episodes.
+
+{{TOPOLOGY}}
+
+## Nodes to review
+
+{{NODES}}