name: query version: 1.0.0 description: | Answer questions using the brain's knowledge with 3-layer search, synthesis, and citation propagation. Use when the user asks a question, wants a lookup, or needs information from the brain. triggers:

"what do we know about"
"tell me about"
"who is"
"what happened"
"search for"
"look up"
"background on"
"notes on"
"who knows who"
"relationship between"
"connections"
"graph query" tools:
search
query
get_page
list_pages
get_backlinks
traverse_graph
get_timeline mutating: false

Query Skill

Answer questions using the brain's knowledge with 3-layer search and synthesis.

Memory verbs (MEMORY_VERBS v1, gbrain ≥ 0.43). When connected to a brain over MCP, prefer the seven frozen memory verbs for memory work — they carry provenance, evidence, and a server-enforced token budget:

recall(query | entity, budget_tokens) — the budget-packed memory read. Use it instead of bare search for "what do we know that we SAVED about X".

entity(name) — a zero-LLM person/company/project card (aliases, last-touched, open threads, top edges). Use it instead of get_page + get_backlinks when you just need the card.

synthesize(question) — the explicitly-expensive cross-page answer; the heavy version of query. Reach for it only when the answer must combine evidence across pages. Fall back to search/query/get_page when the verbs aren't on the surface (pre-0.43 servers; --surface full includes the verbs alongside every other op). See docs/protocol/MEMORY_VERBS_v1.md.

Contract

This skill guarantees:

Every answer is grounded in brain content (no hallucination)
Every claim has a citation tracing back to a specific page slug
Gaps are flagged explicitly ("the brain doesn't have information on X")
Source precedence is respected (user statements > compiled truth > timeline > external)
Conflicting sources are noted with both citations

Phases

Decompose the question into search strategies:
- Keyword search for specific names, dates, terms
- Semantic query for conceptual questions
- Structured queries (list by type, backlinks) for relational questions
Execute searches:
- Cheap-hybrid search gbrain for exact tokens / known names (search)
- Full-hybrid search gbrain with multi-query expansion for concept questions (query)
- List pages in gbrain by type or check backlinks for structural queries
Read top results. Read the top 3-5 pages from gbrain to get full context.
Synthesize answer with citations. Every claim traces back to a specific page slug.
Flag gaps. If the brain doesn't have info, say "the brain doesn't have information on X" rather than hallucinating.

Anti-Patterns

Answering from general knowledge when the brain has relevant content
Hallucinating facts not in the brain
Silently picking one source when sources conflict
Loading full pages when search chunks are sufficient
Ignoring source precedence (user statements are highest authority)

Output Format

Answers should include:

Direct response to the question
Citations: "According to [Source: people/jane-doe, compiled truth]..."
Gap flags: "The brain doesn't have information on X"
Conflict notes when sources disagree

Quality Rules

Never hallucinate. Only answer from brain content.
Cite sources: "According to concepts/do-things-that-dont-scale..."
Flag stale results: if a search result shows [STALE], note that the info may be outdated
For "who" questions, use backlinks and typed links to find connections
For "what happened" questions, use timeline entries
For "what do we know" questions, read compiled_truth directly

Token-Budget Awareness

Search returns chunks, not full pages. Read the excerpts first before deciding whether to load a full page.

gbrain search / gbrain query return ranked chunks with context snippets. These are often enough to answer the question directly.
Only use gbrain get <slug> to load the full page when a chunk confirms the page is relevant and you need more context (e.g., compiled truth, timeline).
"Tell me about X" -- get the full page (the user wants the complete picture).
"Did anyone mention Y?" -- search results are enough (the user wants a yes/no with evidence).

Source precedence

When multiple sources provide conflicting information, follow this precedence:

User's direct statements (highest authority -- what the user told you directly)
Compiled truth (the brain's synthesized, cited understanding)
Timeline entries (raw evidence, reverse-chronological)
External sources (web search, API enrichment -- lowest authority)

When sources conflict, note the contradiction with both citations. Don't silently pick one.

Citation in Answers

When referencing brain pages in your answer, propagate inline citations:

Cite the page: "According to [Source: people/jane-doe, compiled truth]..."
When brain pages have inline [Source: ...] citations, propagate them so the user can trace facts to their origin
When you synthesize across multiple pages, cite all sources

Graph Traversal (v0.10.1+)

For relationship questions ("who knows who at X?", "connections between A and B", "who works at Acme?", "who attended the standup?"), use the graph layer instead of full-text search:

gbrain graph-query <slug> --type <link_type> --depth N --direction in|out|both
Available link types: attended, works_at, invested_in, founded, advises, mentions, source
--direction in answers "who points to X?" (e.g., who works at company X)
--direction out answers "what does X point to?" (default)
--depth N controls multi-hop traversal (default 5)

Examples:

"Who works at Acme?" → gbrain graph-query companies/acme --type works_at --direction in
"Who attended Demo Day W26?" → gbrain graph-query meetings/demo-day-w26 --type attended --direction out
"What companies has Emily advised?" → gbrain graph-query people/emily --type advises --direction out
"Who has Alice met (via meetings)?" → gbrain graph-query people/alice --type attended --depth 2

Combine with gbrain query for queries that need BOTH semantic similarity AND graph structure. Search results are ranked with a small backlink boost so well- connected entities surface higher.

Search Quality Awareness

If search results seem off (wrong results, missing known pages, irrelevant hits):

Run gbrain doctor --json to check index health
Check embedding coverage -- partial embeddings degrade hybrid search
Compare keyword search (gbrain search) vs hybrid search (gbrain query) for the same query to isolate whether the issue is embedding-related
Report search quality issues in the maintain workflow (see maintain skill)

Tools Used

Keyword search gbrain (search)
Hybrid search gbrain (query)
Read a page from gbrain (get_page)
List pages in gbrain with filters (list_pages)
Check backlinks in gbrain (get_backlinks)
Traverse the link graph in gbrain (traverse_graph)
View timeline entries in gbrain (get_timeline)