research-query

Research Query

Ask a question against the local research corpus and get a synthesized answer with inline citations.

Triggers

• "what does the research say about X?"
• "query the corpus for X"
• "search research for X"
• "what evidence do we have for X?"
• "research query X"
• /research-query "question"

Parameters

<question> (required)

A natural language question to answer from the corpus.

--depth (optional)

Search depth: quick (tag + title matching only) or thorough (full-text content search). Default: thorough.

--backend (optional)

Retrieval backend for the executable source-selection wrapper: fortemi-core or local. Default: fortemi-core. By default, research-query uses the Fortemi static cache after it is synced with aiwg index sync; missing, stale, or malformed caches fail with recovery guidance instead of falling back silently. Use --backend local only for the legacy local corpus fallback.

--save (optional)

Save the synthesized answer as a new artifact in .aiwg/research/synthesis/.

--sources-only (optional)

List matching sources without synthesizing an answer.

--max-sources (optional)

Maximum number of sources to read and synthesize from. Default: 10.

Execution Flow

The executable CLI wrapper aiwg research-query performs deterministic source selection and can emit JSON for tests and automation. It does not replace the skill's synthesis responsibilities; agents still write the final answer with GRADE-aware hedging, contradictions, gaps, and inline REF citations.

Phase 1: Corpus Search

Search the local research corpus for relevant sources:

1. Tag-based search: Grep frontmatter tags: fields in .aiwg/research/findings/REF-*.md for topic matches
2. Title search: Match question keywords against title: frontmatter fields
3. Full-text search (if --depth thorough): Search body content of all REF-XXX notes for question terms
4. Synthesis search: Also check .aiwg/research/synthesis/ for existing synthesis on the topic
5. Knowledge search: Check .aiwg/research/knowledge/ for related concept notes

Search locations (priority order):

.aiwg/research/findings/REF-*.md     # Primary: literature notes
.aiwg/research/synthesis/*.md        # Secondary: existing synthesis
.aiwg/research/knowledge/*.md        # Tertiary: knowledge base entries

Phase 2: Source Reading

For each matching source (up to --max-sources):

1. Read the full content of the REF-XXX note
2. Extract:
   • Key claims and findings
   • GRADE quality assessment
   • Methodology and evidence type
   • Related source references
3. Rank by relevance to the question

Phase 3: Answer Synthesis

Synthesize a comprehensive answer from the matched sources:

1. Lead with the answer — state the synthesized finding clearly
2. Cite inline — reference specific REF-XXX identifiers with the finding they support
3. Note evidence quality — use GRADE-appropriate hedging:
   • HIGH: "Evidence strongly supports..."
   • MODERATE: "Evidence suggests..."
   • LOW: "Limited evidence indicates..."
   • VERY LOW: "Preliminary findings hint at..."
4. Flag contradictions — if sources disagree, state both positions with citations
5. Identify gaps — if the question touches areas with sparse coverage, note what's missing

Answer format:

## Answer

[Synthesized answer with inline citations]

Evidence strongly supports that agent orchestration patterns improve
task completion rates by 30-45% compared to single-agent approaches
(REF-012, REF-034). However, this comes with increased latency —
REF-067 measured a 2-3x slowdown for multi-agent coordination on
tasks under 5 minutes. Limited evidence indicates that the breakeven
point is approximately 15 minutes of task complexity (REF-042, GRADE: Low).

### Sources Consulted

| REF | Title | GRADE | Relevance |
|-----|-------|-------|-----------|
| REF-012 | Multi-Agent Orchestration Patterns | High | Direct |
| REF-034 | Agent Coordination Benchmarks | Moderate | Direct |
| REF-067 | Latency Analysis of LLM Pipelines | Moderate | Supporting |
| REF-042 | Cost-Benefit of Agent Architectures | Low | Tangential |

### Evidence Quality
- 1 HIGH, 2 MODERATE, 1 LOW sources
- Overall confidence: MODERATE

### Gaps
- No sources address orchestration in resource-constrained environments
- Missing: longitudinal studies on orchestration pattern stability

### Related Queries
- "What are the latency costs of multi-agent systems?"
- "How does orchestration affect token consumption?"

Phase 4: Save (if --save)

If --save is specified, write the answer as a synthesis artifact:

.aiwg/research/synthesis/query-<slug>-<date>.md

With frontmatter:

---
type: query-synthesis
question: "<original question>"
date: YYYY-MM-DD
sources: [REF-012, REF-034, REF-067, REF-042]
confidence: moderate
---

Distinction from Other Skills

Examples

# Ask a question
/research-query "What are the security risks of LLM agents?"

# Quick search (tags and titles only)
/research-query "prompt injection defenses" --depth quick

# Use the Fortemi Core static cache for source selection
aiwg research-query "prompt injection defenses" --sources-only --json

# Just list matching sources
/research-query "multi-agent orchestration" --sources-only

# Save the answer as a synthesis artifact
/research-query "What evidence supports HITL gates?" --save

# Limit sources consulted
/research-query "cost optimization strategies" --max-sources 5

References

• @$AIWG_ROOT/agentic/code/frameworks/research-complete/skills/research-discover/SKILL.md — External search
• @$AIWG_ROOT/agentic/code/frameworks/research-complete/skills/research-gap/SKILL.md — Gap analysis
• @$AIWG_ROOT/agentic/code/frameworks/research-complete/skills/research-cite/SKILL.md — Citation formatting
• @$AIWG_ROOT/agentic/code/frameworks/research-complete/skills/research-status/SKILL.md — Corpus health
• @$AIWG_ROOT/agentic/code/frameworks/research-complete/elaboration/templates/REF-XXX-template.md — REF note format