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/research-synthesize

Synthesize research findings from memory into structured reports with evidence grading, contradiction resolution, and actionable recommendations

From plugin
claude-flow
67k200 skills157 agents194 commands1 MCP
Install
$ npx -y skills add ruvnet/claude-flow --skill research-synthesize --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/research-synthesize

Context preview

The summary Claude sees to decide when to auto-load this skill.

Synthesize research findings from memory into structured reports with evidence grading, contradiction resolution, and actionable recommendations

SKILL.md

research-synthesize.SKILL.md
name: research-synthesize
description: Synthesize research findings from memory into structured reports with evidence grading, contradiction resolution, and actionable recommendations
argument-hint: "<topic> [--format report|brief|table]"
allowed-tools: mcp__plugin_ruflo-core_ruflo__memory_search mcp__plugin_ruflo-core_ruflo__memory_search_unified mcp__plugin_ruflo-core_ruflo__memory_list mcp__plugin_ruflo-core_ruflo__memory_retrieve mcp__plugin_ruflo-core_ruflo__memory_store mcp__plugin_ruflo-core_ruflo__agentdb_context-synthesize mcp__plugin_ruflo-core_ruflo__agentdb_pattern-search mcp__plugin_ruflo-core_ruflo__neural_predict Bash Read Write

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__plugin_ruflo-core_ruflo__memory_search` namespace `research` for raw findings
  • `mcp__plugin_ruflo-core_ruflo__memory_search` namespace `research-sources` for references
  • `mcp__plugin_ruflo-core_ruflo__agentdb_pattern-search` for discovered patterns
  • `mcp__plugin_ruflo-core_ruflo__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__plugin_ruflo-core_ruflo__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__plugin_ruflo-core_ruflo__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]
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An agent meta-harness for Claude Code and Codex. Agent = Model + Harness. The model writes; the harness gives it tools, memory, loops, sandboxes, and controls so it can actually work.

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