query

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

1. 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
2. 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
3. Read top results. Read the top 3-5 pages from gbrain to get full context.
4. Synthesize answer with citations. Every claim traces back to a specific page slug.
5. 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:

1. User's direct statements (highest authority -- what the user told you directly)
2. Compiled truth (the brain's synthesized, cited understanding)
3. Timeline entries (raw evidence, reverse-chronological)
4. 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)