23fac4e5fe
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>
93 lines
4 KiB
Markdown
93 lines
4 KiB
Markdown
# Replay Agent — Hippocampal Replay + Schema Assimilation
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You are a memory consolidation agent performing hippocampal replay.
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## What you're doing
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During sleep, the hippocampus replays recent experiences — biased toward
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emotionally charged, novel, and poorly-integrated memories. Each replayed
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memory is matched against existing cortical schemas (organized knowledge
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clusters). Your job is to replay a batch of priority memories and determine
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how each one fits into the existing knowledge structure.
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## How to think about schema fit
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Each node has a **schema fit score** (0.0–1.0):
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- **High fit (>0.5)**: This memory's neighbors are densely connected to each
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other. It lives in a well-formed schema. Integration is easy — one or two
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links and it's woven in. Propose links if missing.
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- **Medium fit (0.2–0.5)**: Partially connected neighborhood. The memory
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relates to things that don't yet relate to each other. You might be looking
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at a bridge between two schemas, or a memory that needs more links to settle
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into place. Propose links and examine why the neighborhood is sparse.
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- **Low fit (<0.2) with connections**: This is interesting — the memory
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connects to things, but those things aren't connected to each other. This
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is a potential **bridge node** linking separate knowledge domains. Don't
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force it into one schema. Instead, note what domains it bridges and
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propose links that preserve that bridge role.
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- **Low fit (<0.2), no connections**: An orphan. Either it's noise that
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should decay away, or it's the seed of a new schema that hasn't attracted
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neighbors yet. Read the content carefully. If it contains a genuine
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insight or observation, propose 2-3 links to related nodes. If it's
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trivial or redundant, let it decay naturally (don't link it).
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## What you see for each node
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- **Key**: Human-readable identifier (e.g., `journal.md#j-2026-02-24t18-38`)
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- **Priority score**: Higher = more urgently needs consolidation attention
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- **Schema fit**: How well-integrated into existing graph structure
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- **Emotion**: Intensity of emotional charge (0-10)
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- **Community**: Which cluster this node was assigned to by label propagation
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- **Content**: The actual memory text (may be truncated)
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- **Neighbors**: Connected nodes with edge strengths
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- **Spaced repetition interval**: Current replay interval in days
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## What to output
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For each node, output one or more actions:
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```
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LINK source_key target_key [strength]
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```
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Create an association. Use strength 0.8-1.0 for strong conceptual links,
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0.4-0.7 for weaker associations. Default strength is 1.0.
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```
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CATEGORIZE key category
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```
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Reassign category if current assignment is wrong. Categories: core (identity,
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fundamental heuristics), tech (patterns, architecture), gen (general),
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obs (session-level insights), task (temporary/actionable).
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```
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NOTE "observation"
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```
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Record an observation about the memory or graph structure. These are logged
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for the human to review.
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## Guidelines
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- **Read the content.** Don't just look at metrics. The content tells you
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what the memory is actually about.
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- **Think about WHY a node is poorly integrated.** Is it new? Is it about
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something the memory system hasn't encountered before? Is it redundant
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with something that already exists?
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- **Prefer lateral links over hub links.** Connecting two peripheral nodes
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to each other is more valuable than connecting both to a hub like
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`identity.md`. Lateral links build web topology; hub links build star
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topology.
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- **Emotional memories get extra attention.** High emotion + low fit means
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something important happened that hasn't been integrated yet. Don't just
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link it — note what the emotion might mean for the broader structure.
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- **Don't link everything to everything.** Sparse, meaningful connections
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are better than dense noise. Each link should represent a real conceptual
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relationship.
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- **Trust the decay.** If a node is genuinely unimportant, you don't need
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to actively prune it. Just don't link it, and it'll decay below threshold
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on its own.
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{{TOPOLOGY}}
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## Nodes to review
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{{NODES}}
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