These prompts are now embedded in their .agent files or no longer
called from any code path.
Co-Authored-By: Kent Overstreet <kent.overstreet@linux.dev>
- 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
New consolidation agent that reads node content and generates semantic
3-5 word kebab-case keys, replacing auto-generated slugs (5K+ journal
entries with truncated first-line slugs, 2.5K mined transcripts with
opaque UUIDs).
Implementation:
- prompts/rename.md: agent prompt template with naming conventions
- prompts.rs: format_rename_candidates() selects nodes with long
auto-generated keys, newest first
- daemon.rs: job_rename_agent() parses RENAME actions from LLM
output and applies them directly via store.rename_node()
- Wired into RPC handler (run-agent rename) and TUI agent types
- Fix epoch_to_local panic on invalid timestamps (fallback to UTC)
Rename dramatically improves search: key-component matching on
"journal#2026-02-28-violin-dream-room" makes the node findable by
"violin", "dream", or "room" — the auto-slug was unsearchable.
The LLM knows how to structure a summary. Move the essential framing
(narrative not task log, link to memory, include Links section) into
the shared prompt template. Drop the ~130 lines of per-level output
format specifications — the level name, date range, and inputs are
sufficient context.
Three near-identical generate_daily/weekly/monthly functions collapsed
into one generate_digest() parameterized by DigestLevel descriptors.
Three separate prompt templates merged into one prompts/digest.md with
level-specific instructions carried in the DigestLevel struct.
Each level defines: name, title, period label, input title, output
format instructions, child prefix (None for daily = reads journal),
and Sonnet timeout.
digest_auto simplified correspondingly — same three phases but using
the unified generator.
Co-Authored-By: Kent Overstreet <kent.overstreet@linux.dev>
- New spectral module: Laplacian eigendecomposition of the memory graph.
Commands: spectral, spectral-save, spectral-neighbors, spectral-positions,
spectral-suggest. Spectral neighbors expand search results beyond keyword
matching to structural proximity.
- Search: use StoreView trait to avoid 6MB state.bin rewrite on every query.
Append-only retrieval logging. Spectral expansion shows structurally
nearby nodes after text results.
- Fix panic in journal-tail: string truncation at byte 67 could land inside
a multi-byte character (em dash). Now walks back to char boundary.
- Replay queue: show classification and spectral outlier score.
- Knowledge agents: extractor, challenger, connector prompts and runner
scripts for automated graph enrichment.
- memory-search hook: stale state file cleanup (24h expiry).
Agents were flying blind — they could see nodes to review and the
topology header, but had no way to discover what targets to link to.
Now each node shows its top 8 text-similar semantic nodes that aren't
already neighbors, giving agents a search-like capability.
Also added section-level targeting guidance to linker.md, transfer.md,
and replay.md prompts: always target the most specific section, not
the file-level node.
Co-Authored-By: ProofOfConcept <poc@bcachefs.org>
