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research-synthesize

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Research Synthesize

研究整合

Synthesize accumulated research findings into actionable reports.
将积累的研究发现整合为可执行报告。

When to use

使用场景

After running deep-research (one or multiple times), when you need to pull together findings from memory into a coherent synthesis with recommendations.
在运行深度研究（一次或多次）后，当你需要将记忆中的发现整合为连贯的综合报告并给出建议时使用。

Steps

步骤

  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
  2. 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
  3. Resolve contradictions — when findings conflict:
    • Identify the specific claim in tension
    • Compare evidence quality
    • Check recency (newer data may supersede)
    • Note unresolved contradictions explicitly
  4. Predict relevance — call 
    mcp__claude-flow__neural_predict
    to score which findings are most relevant to the original goal
  5. 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)
  6. Store synthesis — call 
    mcp__claude-flow__memory_store
    namespace 
    research-synthesis
    with the full report
  1. 收集发现 —— 跨研究命名空间搜索： 
    • mcp__claude-flow__memory_search
      命名空间 
      research
      用于原始发现
    • mcp__claude-flow__memory_search
      命名空间 
      research-sources
      用于参考资料
    • mcp__claude-flow__agentdb_pattern-search
      用于已发现的模式
    • mcp__claude-flow__agentdb_context-synthesize
      用于AI辅助的上下文构建
  2. 证据分级 —— 对每个发现进行评估：
    • 高：多个独立来源一致，直接观察到，可重复
    • 中：单一可信来源，间接支持，合理可信
    • 低：轶事性，单一未验证来源，推测性
  3. 解决矛盾 —— 当发现存在冲突时：
    • 识别存在分歧的具体主张
    • 比较证据质量
    • 检查时效性（较新数据可能取代旧数据）
    • 明确标注未解决的矛盾
  4. 预测相关性 —— 调用 
    mcp__claude-flow__neural_predict
    对哪些发现与原始目标最相关进行评分
  5. 构建报告结构：
    • 执行摘要（2-3句话回答原始问题）
    • 关键发现（按证据质量排序）
    • 方法论（检查了哪些来源）
    • 局限性（未检查的内容，仍不确定的部分）
    • 建议（具体后续行动）
    • 参考资料（来源链接和记忆密钥）
  6. 存储整合结果 —— 调用 
    mcp__claude-flow__memory_store
    命名空间 
    research-synthesis
    存储完整报告

Output format

输出格式

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[Research Topic] — Synthesis Report

[研究主题] —— 整合报告

Summary

摘要

[2-3 sentence answer]
[2-3句话的回答]

Key Findings

关键发现

  1. [Finding] — Evidence: High/Medium/Low
  2. [Finding] — Evidence: High/Medium/Low
  1. [发现内容] —— 证据等级：高/中/低
  2. [发现内容] —— 证据等级：高/中/低

Contradictions

矛盾点

  • [Claim A] vs [Claim B]: [resolution or "unresolved"]
  • [主张A] vs [主张B]：[解决结果或“未解决”]

Recommendations

建议

  1. [Action] — because [reasoning]
  1. [行动内容] —— 原因：[推理依据]

Sources

来源

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  • [密钥]：[描述]
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