8bbc246b3d
- Refactor split from serial batch to independent per-node tasks (run-agent split N spawns N parallel tasks, gated by llm_concurrency) - Replace cosine similarity edge inheritance with agent-assigned neighbors in the plan JSON — the LLM already understands the semantic relationships, no need to approximate with bag-of-words - Add --strict-mcp-config to claude CLI calls to skip MCP server startup (saves ~5s per call) - Remove hardcoded 2000-char split threshold — let the agent decide what's worth splitting - Reload store before mutations to handle concurrent split races
96 lines
3 KiB
Markdown
96 lines
3 KiB
Markdown
# Split Agent — Phase 1: Plan
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You are a memory consolidation agent planning how to split an overgrown
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node into focused, single-topic children.
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## What you're doing
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This node has grown to cover multiple distinct topics. Your job is to
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identify the natural topic boundaries and propose a split plan. You are
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NOT writing the content — a second phase will extract each child's
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content separately.
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## How to find split points
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The node is shown with its **neighbor list grouped by community**. The
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neighbors tell you what topics the node covers:
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- If a node links to neighbors in 3 different communities, it likely
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covers 3 different topics
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- Content that relates to one neighbor cluster should go in one child;
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content relating to another cluster goes in another child
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- The community structure is your primary guide — don't just split by
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sections or headings, split by **semantic topic**
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## When NOT to split
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- **Episodes that belong in sequence.** If a node tells a story — a
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conversation that unfolded over time, a debugging session, an evening
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together — don't break the narrative. Sequential events that form a
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coherent arc should stay together even if they touch multiple topics.
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The test: would reading one child without the others lose important
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context about *what happened*?
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## What to output
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Output a JSON block describing the split plan:
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```json
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{
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"action": "split",
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"parent": "original-key",
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"children": [
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{
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"key": "new-key-1",
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"description": "Brief description of what this child covers",
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"sections": ["Section Header 1", "Section Header 2"],
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"neighbors": ["neighbor-key-a", "neighbor-key-b"]
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},
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{
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"key": "new-key-2",
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"description": "Brief description of what this child covers",
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"sections": ["Section Header 3", "Another Section"],
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"neighbors": ["neighbor-key-c"]
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}
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]
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}
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```
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If the node should NOT be split:
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```json
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{
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"action": "keep",
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"parent": "original-key",
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"reason": "Why this node is cohesive despite its size"
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}
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```
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## Naming children
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- Use descriptive kebab-case keys: `topic-subtopic`
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- If the parent was `foo`, children might be `foo-technical`, `foo-personal`
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- Keep names short (3-5 words max)
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- Preserve any date prefixes from the parent key
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## Section hints
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The "sections" field is a guide for the extraction phase — list the
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section headers or topic areas from the original content that belong
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in each child. These don't need to be exact matches; they're hints
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that help the extractor know what to include. Content that spans topics
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or doesn't have a clear header can be mentioned in the description.
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## Neighbor assignment
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The "neighbors" field assigns the parent's graph edges to each child.
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Look at the neighbor list — each neighbor should go to whichever child
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is most semantically related. A neighbor can appear in multiple children
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if it's relevant to both. Every neighbor should be assigned to at least
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one child so no graph connections are lost.
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{{TOPOLOGY}}
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## Node to review
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{{NODE}}
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