/research-synthesize
Synthesize research findings from memory into structured reports with evidence grading, contradiction resolution, and actionable recommendations
Claude Code
$ npx -y skills add ruvnet/ruflo --skill research-synthesize --agent claude-code
Codex
$ npx skills add ruvnet/ruflo --skill research-synthesize -a codex -y
Cursor
$ npx skills add ruvnet/ruflo --skill research-synthesize -a cursor -y
Gemini CLI
$ npx skills add ruvnet/ruflo --skill research-synthesize -a gemini-cli -y
GitHub Copilot
$ npx skills add ruvnet/ruflo --skill research-synthesize -a github-copilot -y
OpenCode
$ npx skills add ruvnet/ruflo --skill research-synthesize -a opencode -y
Windsurf
$ npx skills add ruvnet/ruflo --skill research-synthesize -a windsurf -y
SKILL.md
research-synthesize.SKILL.mdname: 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
- Synthesize the findings yourself from what the searches return. `mcp__plugin_ruflo-core_ruflo__agentdb_context-synthesize` is not a document synthesizer: it summarizes validated task episodes (task, reward, success) and answers `no-memories` or `no-eligible-episodes` for research notes and sources, which is the correct answer, not missing research
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]
Read more
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
- Synthesize the findings yourself from what the searches return. `mcp__plugin_ruflo-core_ruflo__agentdb_context-synthesize` is not a document synthesizer: it summarizes validated task episodes (task, reward, success) and answers `no-memories` or `no-eligible-episodes` for research notes and sources, which is the correct answer, not missing research
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]
📖 RuFlo Explained — Build an AI Team That Plans, Remembers, Tests, and Improves A 14-chapter guide: from the basic idea to a first useful task, then memory, agent teams, plugins, cost and verification. Agent = Model + Harness.
Other skills on ruflo.
More skills from other plugins.
agentmail
Use your assigned AgentMail inbox to read email tasks, explicitly send or reply, and check delivery. Provided automatically by…
bmad
Answers BMad questions and recommends the next skill from what is installed. Use when the user asks bmad for help, what to do…
Questions about research-synthesize.
- Which coding agents can use research-synthesize?
- research-synthesize installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.

