Free SKILL.md scraped from GitHub. Clone the repo or copy the file directly into your Claude Code skills directory.
npx versuz@latest install ruvnet-claude-flow-plugins-ruflo-goals-skills-research-synthesizegit clone https://github.com/ruvnet/claude-flow.gitcp claude-flow/SKILL.MD ~/.claude/skills/ruvnet-claude-flow-plugins-ruflo-goals-skills-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__claude-flow__memory_search mcp__claude-flow__memory_search_unified mcp__claude-flow__memory_list mcp__claude-flow__memory_retrieve mcp__claude-flow__memory_store mcp__claude-flow__agentdb_context-synthesize mcp__claude-flow__agentdb_pattern-search mcp__claude-flow__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__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 ## 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] ```