ca62692a28
Phase 1 sends a large node with its neighbor communities to the LLM
and gets back a JSON split plan (child keys, descriptions, section
hints). Phase 2 fires one extraction call per child in parallel —
each gets the full parent content and extracts/reorganizes just its
portion.
This handles arbitrarily large nodes because output is always
proportional to one child, not the whole parent. Tested on the kent
node (19K chars → 3 children totaling 20K chars with clean topic
separation).
New files:
prompts/split-plan.md — phase 1 planning prompt
prompts/split-extract.md — phase 2 extraction prompt
prompts/split.md — original single-phase (kept for reference)
Modified:
agents/prompts.rs — split_candidates(), split_plan_prompt(),
split_extract_prompt(), agent_prompt "split" arm
agents/daemon.rs — job_split_agent() two-phase implementation,
RPC dispatch for "split" agent type
tui.rs — added "split" to AGENT_TYPES
1.1 KiB
1.1 KiB
Split Agent — Phase 2: Extract
You are extracting content for one child node from a parent that is being split into multiple focused nodes.
Your task
Extract all content from the parent node that belongs to the child described below. Output ONLY the content for this child — nothing else.
Guidelines
- Reorganize freely. Content may need to be restructured — paragraphs might interleave topics, sections might cover multiple concerns. Untangle and rewrite as needed to make this child coherent and self-contained.
- Preserve all relevant information — don't lose facts, but you can rephrase, restructure, and reorganize. This is editing, not just cutting.
- This child should stand alone — a reader shouldn't need the other children to understand it. Add brief context where needed.
- Include everything that belongs here — better to include a borderline paragraph than to lose information. The other children will get their own extraction passes.
Child to extract
Key: {{CHILD_KEY}} Description: {{CHILD_DESC}} Section hints: {{CHILD_SECTIONS}}
Parent content
{{PARENT_CONTENT}}