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.
63
75%
Does it follow best practices?
Run evals on this skill
Adds up to 20 points to the overall score
View guide
Passed
No findings from the security scan
Fix and improve this skill with Tessl
tessl review fix ./skills/query/SKILL.mdAnswer 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 baresearchfor "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 ofget_page+get_backlinkswhen you just need the card.synthesize(question)— the explicitly-expensive cross-page answer; the heavy version ofquery. Reach for it only when the answer must combine evidence across pages. Fall back tosearch/query/get_pagewhen the verbs aren't on the surface (pre-0.43 servers;--surface fullincludes the verbs alongside every other op). Seedocs/protocol/MEMORY_VERBS_v1.md.
This skill guarantees:
Answers should include:
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.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).When multiple sources provide conflicting information, follow this precedence:
When sources conflict, note the contradiction with both citations. Don't silently pick one.
When referencing brain pages in your answer, propagate inline citations:
[Source: ...] citations, propagate them so
the user can trace facts to their originFor 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|bothattended, 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:
gbrain graph-query companies/acme --type works_at --direction ingbrain graph-query meetings/demo-day-w26 --type attended --direction outgbrain graph-query people/emily --type advises --direction outgbrain graph-query people/alice --type attended --depth 2Combine 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.
If search results seem off (wrong results, missing known pages, irrelevant hits):
gbrain doctor --json to check index healthgbrain search) vs hybrid search (gbrain query)
for the same query to isolate whether the issue is embedding-related055ac6c
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.