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query

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

Skill by weareturnone

IndexedMIT
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About

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.

Use cases: research

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What's inside

Skill name
query
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.
License
MIT
Instructions
126 lines in SKILL.md · view on GitHub

Files (2)

  • SKILL.md
  • routing-eval.jsonl

SKILL.md (first lines)

# Query Skill

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

## 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:**
   - Keyword search gbrain for FTS matches (search)
   - Hybrid search gbrain for semantic+keyword with expansion (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)

From weareturnone/gbrain, MIT.

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Last commit (this folder)
Apr 27, 2026 (6 months ago)

From GitHub (fetched Oct 24, 2026).

Details

License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

Same skill elsewhere

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Cite this page

query skill. Agent Mart. https://agentmart.dev/skills/weareturnone-query (last updated 2026-10-24)