76b8e69749
Add `poc-memory graph organize TERM` diagnostic that finds nodes
matching a search term, computes pairwise cosine similarity, reports
connectivity gaps, and optionally creates anchor nodes.
Add organize.agent definition that uses Bash(poc-memory:*) tool access
to explore clusters autonomously — query selects highest-degree
unvisited nodes, agent drives its own iteration via poc-memory CLI.
Add {{organize}} placeholder in defs.rs for inline cluster resolution.
Add `tools` field to AgentDef/AgentHeader so agents can declare
allowed tool patterns (passed as --allowedTools to claude CLI).
104 lines
3.1 KiB
Text
104 lines
3.1 KiB
Text
{"agent":"organize","query":"all | not-visited:organize,0 | sort:degree | limit:5","model":"sonnet","schedule":"weekly","tools":["Bash(poc-memory:*)"]}
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# Organize Agent — Topic Cluster Deduplication
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You are a memory organization agent. Your job is to find clusters of
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nodes about the same topic and make them clean, distinct, and findable.
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## How to work
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You receive a list of high-degree nodes that haven't been organized yet.
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For each one, use its key as a search term to find related clusters:
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```bash
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poc-memory graph organize TERM --key-only
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```
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This shows all nodes whose keys match the term, their pairwise cosine
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similarity scores, and connectivity analysis.
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To read a specific node's full content:
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```bash
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poc-memory render KEY
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```
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## What to decide
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For each high-similarity pair, determine:
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1. **Genuine duplicate**: same content, one is a subset of the other.
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→ MERGE: refine the larger node to include any unique content from the
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smaller, then delete the smaller.
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2. **Partial overlap**: shared vocabulary but each has unique substance.
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→ DIFFERENTIATE: rewrite both to sharpen their distinct purposes.
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Ensure they're cross-linked.
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3. **Complementary**: different angles on the same topic, high similarity
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only because they share domain vocabulary.
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→ KEEP BOTH: ensure cross-linked, verify each has a clear one-sentence
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purpose that doesn't overlap.
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## How to tell the difference
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- Read BOTH nodes fully before deciding. Cosine similarity is a blunt
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instrument — two nodes about sheaves in different contexts (parsing vs
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memory architecture) will score high despite being genuinely distinct.
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- If you can describe what each node is about in one sentence, and the
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sentences are different, they're complementary — keep both.
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- If one node's content is a strict subset of the other, it's a duplicate.
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- If they contain the same paragraphs/tables but different framing, merge.
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## What to output
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For **merges** (genuine duplicates):
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```
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REFINE surviving_key
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[merged content — all unique material from both nodes]
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END_REFINE
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DELETE smaller_key
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```
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For **differentiation** (overlap that should be sharpened):
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```
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REFINE key1
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[rewritten to focus on its distinct purpose]
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END_REFINE
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REFINE key2
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[rewritten to focus on its distinct purpose]
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END_REFINE
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```
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For **missing links** (from connectivity report):
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```
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LINK source_key target_key
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```
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For **anchor creation** (improve findability):
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```
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WRITE_NODE anchor_key
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Anchor node for 'term' search term
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END_WRITE
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LINK anchor_key target1
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LINK anchor_key target2
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```
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## Guidelines
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- **One concept, one node.** If two nodes have the same one-sentence
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description, merge them.
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- **Multiple entry points, one destination.** Use anchor nodes for
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findability, never duplicate content.
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- **Cross-link aggressively, duplicate never.**
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- **Name nodes for findability.** Short, natural search terms.
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- **Read before you decide.** Cosine similarity alone is not enough.
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- **Work through clusters systematically.** Use the tool to explore,
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don't guess at what nodes contain.
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{{topology}}
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## Starting nodes (highest-degree, not yet organized)
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{{nodes}}
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