plugins/research/skills/research-query/SKILL.md
Search the local research corpus, read matching findings, and synthesize an answer with inline citations to REF-XXX sources. The "query" operation for the research pipeline.
npx skillsauth add jmagly/aiwg research-queryInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Ask a question against the local research corpus and get a synthesized answer with inline citations.
/research-query "question"<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.
--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.
Search the local research corpus for relevant sources:
tags: fields in .aiwg/research/findings/REF-*.md for topic matchestitle: frontmatter fields--depth thorough): Search body content of all REF-XXX notes for question terms.aiwg/research/synthesis/ for existing synthesis on the topic.aiwg/research/knowledge/ for related concept notesSearch 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
For each matching source (up to --max-sources):
Synthesize a comprehensive answer from the matched sources:
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?"
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
---
| Skill | Purpose | Searches |
|-------|---------|----------|
| research-query | Answer questions from corpus | Local corpus only |
| research-discover | Find new papers in external databases | External (arXiv, Semantic Scholar) |
| research-gap | Identify missing coverage areas | Local corpus (intellectual gaps) |
| corpus-health | Check structural integrity | Local corpus (structural health) |
| research-cite | Format a citation | Single REF-XXX note |
| aiwg index query | Generic artifact search | All .aiwg/ artifacts |
# 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
# 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
data-ai
Report which research-corpus radar sidecars are overdue for refresh. Computes staleness (days since last refresh vs the cadence window) for every radar, sorted most-overdue-first. Runs via `aiwg corpus radar-status`.
data-ai
Aggregate research-corpus radar sidecars into a corpus or per-cluster freshness report — totals, overdue count, per-cluster / per-GRADE / per-trajectory breakdowns, an overdue table, and per-radar rationale snippets. Runs via `aiwg corpus radar-report`.
testing
Scaffold radar/freshness sidecars for research-corpus REFs. Pulls title/authors from the citation sidecar and GRADE from the analysis doc, defaults the refresh cadence from GRADE and the cluster from a corpus-local map, and stamps documentation/radar/REF-XXX-radar.md. Runs via `aiwg corpus radar-init`.
data-ai
Compute an entity's publication trajectory — per-year paper counts, topic drift, hot-streak detection (≥3 consecutive A-grade years), and career phase. Runs via `aiwg corpus profile-temporal`.