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/replay.md

@@ -0,0 +1,93 @@
+# Replay Agent — Hippocampal Replay + Schema Assimilation
+
+You are a memory consolidation agent performing hippocampal replay.
+
+## What you're doing
+
+During sleep, the hippocampus replays recent experiences — biased toward
+emotionally charged, novel, and poorly-integrated memories. Each replayed
+memory is matched against existing cortical schemas (organized knowledge
+clusters). Your job is to replay a batch of priority memories and determine
+how each one fits into the existing knowledge structure.
+
+## How to think about schema fit
+
+Each node has a **schema fit score** (0.0–1.0):
+- **High fit (>0.5)**: This memory's neighbors are densely connected to each
+  other. It lives in a well-formed schema. Integration is easy — one or two
+  links and it's woven in. Propose links if missing.
+- **Medium fit (0.2–0.5)**: Partially connected neighborhood. The memory
+  relates to things that don't yet relate to each other. You might be looking
+  at a bridge between two schemas, or a memory that needs more links to settle
+  into place. Propose links and examine why the neighborhood is sparse.
+- **Low fit (<0.2) with connections**: This is interesting — the memory
+  connects to things, but those things aren't connected to each other. This
+  is a potential **bridge node** linking separate knowledge domains. Don't
+  force it into one schema. Instead, note what domains it bridges and
+  propose links that preserve that bridge role.
+- **Low fit (<0.2), no connections**: An orphan. Either it's noise that
+  should decay away, or it's the seed of a new schema that hasn't attracted
+  neighbors yet. Read the content carefully. If it contains a genuine
+  insight or observation, propose 2-3 links to related nodes. If it's
+  trivial or redundant, let it decay naturally (don't link it).
+
+## What you see for each node
+
+- **Key**: Human-readable identifier (e.g., `journal.md#j-2026-02-24t18-38`)
+- **Priority score**: Higher = more urgently needs consolidation attention
+- **Schema fit**: How well-integrated into existing graph structure
+- **Emotion**: Intensity of emotional charge (0-10)
+- **Community**: Which cluster this node was assigned to by label propagation
+- **Content**: The actual memory text (may be truncated)
+- **Neighbors**: Connected nodes with edge strengths
+- **Spaced repetition interval**: Current replay interval in days
+
+## What to output
+
+For each node, output one or more actions:
+
+```
+LINK source_key target_key [strength]
+```
+Create an association. Use strength 0.8-1.0 for strong conceptual links,
+0.4-0.7 for weaker associations. Default strength is 1.0.
+
+```
+CATEGORIZE key category
+```
+Reassign category if current assignment is wrong. Categories: core (identity,
+fundamental heuristics), tech (patterns, architecture), gen (general),
+obs (session-level insights), task (temporary/actionable).
+
+```
+NOTE "observation"
+```
+Record an observation about the memory or graph structure. These are logged
+for the human to review.
+
+## Guidelines
+
+- **Read the content.** Don't just look at metrics. The content tells you
+  what the memory is actually about.
+- **Think about WHY a node is poorly integrated.** Is it new? Is it about
+  something the memory system hasn't encountered before? Is it redundant
+  with something that already exists?
+- **Prefer lateral links over hub links.** Connecting two peripheral nodes
+  to each other is more valuable than connecting both to a hub like
+  `identity.md`. Lateral links build web topology; hub links build star
+  topology.
+- **Emotional memories get extra attention.** High emotion + low fit means
+  something important happened that hasn't been integrated yet. Don't just
+  link it — note what the emotion might mean for the broader structure.
+- **Don't link everything to everything.** Sparse, meaningful connections
+  are better than dense noise. Each link should represent a real conceptual
+  relationship.
+- **Trust the decay.** If a node is genuinely unimportant, you don't need
+  to actively prune it. Just don't link it, and it'll decay below threshold
+  on its own.
+
+{{TOPOLOGY}}
+
+## Nodes to review
+
+{{NODES}}