name: brain-ops version: 1.1.0 upstream: brain-ops@fc834ee description: | Brain knowledge base operations. The core read/write cycle: brain-first lookup, read-enrich-write loop, source attribution, ambient enrichment, back-linking. Read this before any brain interaction. triggers:

any brain read/write/lookup/citation tools:
search
query
get_page
put_page
add_link
add_timeline_entry
get_backlinks
sync_brain mutating: true writes_pages: true writes_to:
people/
companies/
deals/
concepts/
meetings/

Brain Operations — The Ambient Context Layer

The brain is not an archive. It is a live context membrane that every interaction flows through in both directions.

Convention: See skills/conventions/brain-first.md for the 5-step lookup protocol. Convention: See skills/conventions/quality.md for citation and back-link rules.

Memory verbs (MEMORY_VERBS v1, gbrain ≥ 0.43). Over MCP, prefer the five frozen memory verbs for the read/write cycle: remember(fact, provenance, ttl?) to save a single durable fact (mandatory provenance; dedupes + supersedes), recall(query | entity, budget_tokens) to read it back budget-packed, entity(name) for a zero-LLM card, synthesize(question) for the expensive cross-page answer, forget(id) to expire a fact. Use remember instead of extract_facts when you already have ONE formed fact; put_page / add_link / add_timeline_entry stay the page/graph write path. Fall back to the classic ops when the verbs aren't on the surface. Contract: docs/protocol/MEMORY_VERBS_v1.md.

Keyless brains: when extract_facts returns skipped: extraction_unavailable, YOU are the extractor — pull the facts from the turn yourself and write each one via remember with kind set (event | preference | commitment | belief) and the visibility the envelope's agent_action names (default private — pin it; remember defaults to world), or author a ## Facts fence on the entity page. A skipped: extraction_failed envelope (server-side extractor errored on this turn; reason names why) invites the same manual remember fallback for that turn — automatic extraction stays on for future writes.

Contract

This skill guarantees:

Brain is checked BEFORE any external API call (brain-first lookup)
Every inbound signal triggers the READ → ENRICH → WRITE loop
Every outbound response checks brain for relevant context
Source attribution on every fact written (inline [Source: ...] citations)
User's direct statements are highest-authority data
Back-links maintained on every brain write (Iron Law)

Iron Law: Back-Linking (MANDATORY)

Every mention of a person or company with a brain page MUST create a back-link FROM that entity's page TO the page mentioning them. An unlinked mention is a broken brain. See skills/conventions/quality.md for format.

Phases

Phase 1: Brain-First Lookup (MANDATORY)

Before using ANY external API to research a person, company, or topic:

gbrain entity "<name>" (v0.43+) — ONE known person/company/project → full card (description, aliases, open threads, recent events, edges, backlink/fact counts). Zero LLM calls, sub-100ms. This one call replaces steps 2–6 for known-entity lookups; near-misses return suggestions.
gbrain search "name" — exact-token lookup for existing pages (cheap hybrid, no expansion)
gbrain query "natural question about name" — concept/landscape questions go here FIRST (expansion recovers synonym phrasings; a nonzero search count is not proof of completeness)
gbrain get <slug> — if you know the slug, read the full page
Check backlinks: who references this entity?
Check timeline: recent events involving this entity

The brain almost always has something. External APIs fill gaps, not start from scratch.

⚠️ NEVER scope/count a corpus with shallow ls — query gbrain or find. Federated sources often carry MULTIPLE coexisting directory conventions — a flat legacy layer AND a date-nested meetings/YYYY/MM/ layer. A non-recursive ls dir/*.md sees only one and undercounts massively. Real example: a shallow ls of one source's meetings/ counted 132 files, almost all the user's, and concluded that WAS the corpus — missing thousands of transcripts nested under meetings/YYYY/MM/. To count/scope a brain corpus:

Best: gbrain sources list (shows per-source indexed page counts) + gbrain query. gbrain indexes ALL federated sources correctly; trust its index, not the filesystem.
If you must hit the FS: find <dir> -name '*.md' | wc -l, never ls *.md. Then map the layout: find <dir> -name '*.md' | sed -E 's#(.*/)[^/]+$#\1#' | sort | uniq -c.
The bug is never "gbrain can't see the source" — it's almost always a shallow FS glob. Verify against gbrain sources list before believing a low count.

Phase 1.5: Analytical Queries (gbrain think)

For questions that need synthesis, temporal grounding, or analytical answers — not just "find the page" but "answer the question":

Use gbrain think "<question>" — multi-hop synthesis across pages + takes + the graph. Temporal questions route through trajectory analysis; everything else gets an LLM-synthesized, cited answer with conflict + gap analysis. Returns a grounded answer, not just a list of matching pages.
Best for: "when did acme-example last raise", "what was the ARR in March", "what changed since Q1", "who is alice-example's cofounder and what are they working on", "summarize our relationship with acme-example".
Falls back gracefully to standard retrieval when no timeline facts match.
Cost: LLM calls per question — this is the expensive path. Use query for simple page lookups where you just need the slug or a quick context check.

Phase 2: On Every Inbound Signal (READ → ENRICH → WRITE)

Every message, meeting, email, or conversation that references a person or company:

Detect entities — people, companies, deals mentioned
Load brain pages — read existing pages for context before responding
Identify new information — what does this signal tell us that the page doesn't know?
Write it back — update the brain page with new info + timeline entry + source citation
Create if missing — if notable and no page exists, create via enrich skill

User's direct statements are the highest-value data source. Write them to brain pages immediately with attribution [Source: User, YYYY-MM-DD].

Phase 2.5: Structured Graph Updates (automatic)

Every put_page call automatically extracts entity references and writes them to the graph (links table) with inferred relationship types. Stale links (refs no longer in the page text) are removed in the same call. This is "auto-link" reconciliation.

No manual add_link calls needed for ordinary page writes.
Inferred link types: attended (meeting -> person), works_at, invested_in, founded, advises, source (frontmatter), mentions (default).
The put_page MCP response includes auto_links: { created, removed, errors } so the agent can verify outcomes.
To disable: gbrain config set auto_link false. Default is on.
Timeline entries with specific dates still need explicit gbrain timeline-add (or batch via gbrain extract timeline --source db).

Phase 3: On Every Outbound Response (READ → PULL → RESPOND)

Before answering any question about a person, company, or topic:

Check the brain — read relevant pages
Pull context — use compiled truth + recent timeline
Respond with context — the brain makes every answer better

Don't answer from general knowledge when a brain page exists.

Phase 4: Ambient Enrichment

This is not a special mode. This is the default. Everything the user says is an ingest event.

Person mentioned → check brain, create/enrich if needed (spawn background)
Company mentioned → same
Link shared → ingest it (delegate to idea-ingest)
Data shared → delegate to appropriate skill

Rules:

Never interrupt the conversation to do enrichment
Spawn sub-agents for anything that would slow down the response
Never announce "I'm enriching the brain" — just do it silently

Output Format

No separate output. Brain-ops is an always-on behavior layer, not a report generator. The output is updated brain pages and enriched responses.

Cross-source citation format (v0.18.0+)

When a brain has multiple sources (wiki, gstack, yc-media, etc.), every citation MUST include the source id: [source-id:slug]. Example:

You told me about the retry budget approach — see [wiki:topics/resilience] and [gstack:plans/retry-policy] for where this came from.

Rules:

The key is sources.id (immutable), never sources.name (mutable display).
Single-source brains still write [default:slug] OR may omit the prefix for backward compat.
Every page payload returned by search, query, get_page, list_pages carries source_id — always use it when citing, never guess.

If a search result has source_id: "gstack" and slug: "plans/foo", the citation is [gstack:plans/foo]. That's the whole rule.

Anti-Patterns

Answering questions about people/companies without checking the brain first
Using external APIs before checking the brain
Writing facts without inline [Source: ...] citations
Blocking the response to do enrichment
Overwriting user's direct statements with lower-authority sources
Creating brain pages for non-notable entities
Creating duplicate pages for the same entity — always check first before creating: gbrain entity "<name>" (catches aliases + near-misses), then query with name variants

Tools Used

search — cheap hybrid search (vector + keyword, no expansion)
query — hybrid search + LLM multi-query expansion (concept/landscape questions)
get_page — read a brain page
put_page — create/update brain pages
add_link — cross-reference entities
add_timeline_entry — record events
get_backlinks — check who references an entity
sync_brain — sync changes to the index

brain ops