research-synthesize（研究综合）

Research Synthesize

Synthesize accumulated research findings into actionable reports.

When to use

After running deep-research (one or multiple times), when you need to pull together findings from memory into a coherent synthesis with recommendations.

Steps

1. Gather findings — search across research namespaces:

- mcp__claude-flow__memory_search namespace research for raw findings - mcp__claude-flow__memory_search namespace research-sources for references - mcp__claude-flow__agentdb_pattern-search for discovered patterns - mcp__claude-flow__agentdb_context-synthesize for AI-assisted context building

1. Grade evidence — for each finding, assess:

- High: Multiple independent sources agree, directly observed, reproducible - Medium: Single credible source, indirectly supported, plausible - Low: Anecdotal, single unverified source, speculative

1. Resolve contradictions — when findings conflict:

- Identify the specific claim in tension - Compare evidence quality - Check recency (newer data may supersede) - Note unresolved contradictions explicitly

1. Predict relevance — call mcp__claude-flow__neural_predict to score which findings are most relevant to the original goal
2. Structure report:

- Executive summary (2-3 sentences answering the original question) - Key findings (ranked by evidence quality) - Methodology (what sources were checked) - Limitations (what wasn't checked, what remains uncertain) - Recommendations (concrete next actions) - References (source links and memory keys)

1. Store synthesis — call mcp__claude-flow__memory_store namespace research-synthesis with the full report

Output format

# [Research Topic] — Synthesis Report

## Summary
[2-3 sentence answer]

## Key Findings
1. [Finding] — Evidence: High/Medium/Low
2. [Finding] — Evidence: High/Medium/Low

## Contradictions
- [Claim A] vs [Claim B]: [resolution or "unresolved"]

## Recommendations
1. [Action] — because [reasoning]

## Sources
- [key]: [description]