research-synthesize

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English

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Chinese

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

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

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
Resolve contradictions — when findings conflict:
- Identify the specific claim in tension
- Compare evidence quality
- Check recency (newer data may supersede)
- Note unresolved contradictions explicitly
Predict relevance — call
```
mcp__claude-flow__neural_predict
```
to score which findings are most relevant to the original goal
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)
Store synthesis — call
```
mcp__claude-flow__memory_store
```
namespace
```
research-synthesis
```
with the full report

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

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
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
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
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）
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

undefined

undefined

[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

[Finding] — Evidence: High/Medium/Low
[Finding] — Evidence: High/Medium/Low

[发现内容] —— 证据等级：高/中/低
[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

[Action] — because [reasoning]

[行动内容] —— 原因：[推理依据]

Sources

来源

undefined

[密钥]：[描述]

undefined