- 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
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
