research-synthesis

name	research-synthesis
description	Use when researching unfamiliar topics, comparing technology approaches, or building comprehensive understanding from multiple sources
effort	medium

Tier-aware discipline — apply at session start and before every major step:

1. Read widest → narrowest before duplicating effort:
   • T3 (cross-project): mcp__plugin_nx_nexus__nx_answer(...) for verb-shape questions; mcp__plugin_nx_nexus__search(...) for keyword lookup.
   • T2 (project): mcp__plugin_nx_nexus__memory_search(query="<topic>", project="<repo>").
   • T1 (siblings, this session): mcp__plugin_nx_nexus__scratch(action="search", query="<topic>").
2. Reuse plans before dispatching multiple agents: mcp__plugin_nx_nexus__plan_search(query="<task>", limit=3).
3. Write back at end — findings not stored are findings lost. Pick the tier that matches the audience:
   • mcp__plugin_nx_nexus__scratch(action="put", ..., tags="<topic>") for sibling agents downstream THIS session (T1, narrowest scope, cheapest write).
   • mcp__plugin_nx_nexus__memory_put(...) for project-scoped decisions, future sessions same project (T2).
   • mcp__plugin_nx_nexus__store_put(...) for permanent cross-project knowledge, future sessions everywhere (T3).
   • mcp__plugin_nx_nexus__plan_save(...) for multi-agent pipeline outcomes (so future callers hit plan-match).

Research Synthesis Skill

Delegates to the deep-research-synthesizer agent.

Model Selection

Default: haiku. Escalate via model parameter on the Agent tool:

Task Shape	Model	When
Single-topic lookup, ≤3 sources	haiku (default)	Quick research
Multi-source synthesis, >5 sources, or cross-project	sonnet	Comprehensive research

When This Skill Activates

• Researching a new topic or technology
• Investigating best practices for an unfamiliar domain
• Comparing different approaches or frameworks
• Questions requiring synthesis from multiple sources
• When nx T3 search returns insufficient context for a decision

Pre-Dispatch: Seed Link Context

Before dispatching the deep-research-synthesizer agent, seed T1 scratch with link targets so the auto-linker can create catalog links when the agent stores findings:

1. If the task references an RDR (pattern RDR-\d+) or a known document, resolve it: mcp__plugin_nx_nexus-catalog__search(query="RDR-NNN or document title")
2. Check T1 scratch for rdr-planning-context
3. Write link context to scratch:

   mcp__plugin_nx_nexus__scratch(action="put", content='{"targets": [{"tumbler": "<resolved-tumbler>", "link_type": "cites"}], "source_agent": "deep-research-synthesizer"}', tags="link-context")
   

4. If no RDR/document reference found, skip seeding (the auto-linker handles empty context gracefully)

Agent Invocation

Use the Agent tool to invoke deep-research-synthesizer:

## Relay: deep-research-synthesizer

**Task**: [what needs to be done]
**Bead**: [ID] or 'none'

### Input Artifacts
- Files: [relevant files]

### Deliverable
Research synthesis across multiple sources

### Quality Criteria
- [ ] All relevant sources consulted
- [ ] Key findings synthesized (not just listed)
- [ ] Recommendations provided with supporting evidence

For full relay structure and optional fields, see RELAY_TEMPLATE.md.

Research Methodology

The agent uses mcp__plugin_nx_sequential-thinking__sequentialthinking:

1. Form hypothesis about what information is needed
2. Search nx store for existing knowledge — for scoped evidence, prefer query(question=..., author=..., content_type=...) over search() + manual scoping
3. Search web resources for current information
4. Analyze relevant code if applicable
5. Synthesize findings from all sources
6. Resolve contradictions between sources
7. Formulate actionable recommendations

Success Criteria

• All relevant sources consulted (nx store, web, code)
• Key findings synthesized (not just listed)
• Contradictions identified and resolved
• Recommendations provided with supporting evidence
• Findings persisted to nx T3 store via mcp__plugin_nx_nexus__store_put (RDR-080 — direct call, no agent spawn)
• Round artifacts persisted to T2 or T3

Agent-Specific PRODUCE

• Research Synthesis: Store in nx T3 via store_put tool: content="# Research: {topic}\n{content}", collection="knowledge", title="research-{topic}-{date}", tags="research,{domain}"
• Source Citations: Include in document content (not separate)
• Knowledge Gaps: Create research beads for follow-up
• Cross-Reference Maps: Document relationships in nx T3 document content
• Round Artifacts: Use T1 scratch to track findings per research round:
  • scratch tool: action="put", content="# Round {N} findings\n{content}", tags="research,round-{N}"
  • scratch_manage tool: action="flag", entry_id="", project="{project}", title="research-round-{N}.md"