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Skill

/query

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.

From plugin
gbrain
28k57 skills
Install
$ npx -y skills add garrytan/gbrain --skill query --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/query

Context preview

The summary Claude sees to decide when to auto-load this skill.

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.

SKILL.md

query.SKILL.md
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 five 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 at
Read more
Ships withgbrain

Search gives you raw pages. GBrain gives you the answer. It's the brain layer your AI agent has been missing — the only one that does synthesis, graph traversal, and gap analysis in one box.

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