Skill Market

research synthesize

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

GitHub
githubcommunityclaudemcp
0.0
0 installs73.4K GitHub starsby ruvnet

Skill Introduction

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

Core value

Turns reusable Other know-how into an installable skill, helping users complete github, community, claude, mcp work faster.

Target users

  • Developers, testers, and maintainers who handle Other tasks in Focus Code.
  • Teams that already trust workflows or content from ruvnet.
  • Users who want standardized prompts, steps, or conventions instead of repeating setup work.

Best practices

  • Read the skill content first to confirm required inputs, expected outputs, and dependencies.
  • Try it on a small task before relying on it for critical work.
  • Add project-specific constraints such as coding style, target platform, test expectations, and delivery format.
  • For external sources, verify the source link, version, and recent maintenance activity.

Best use cases

  • Tasks related to github, community, claude, mcp that need a reusable execution flow.
  • Converting a community repo, team convention, or personal workflow into day-to-day assistance.
  • Starting from a proven skill instead of writing prompts or procedures from scratch.

Limits and boundaries

  • Results depend on the quality of the original skill content and may need human correction.
  • It does not replace code review, tests, security review, or professional judgment.
  • External tools, APIs, account permissions, and local dependencies still need separate setup.

Differentiation

  • Structured around Other, making it easier to discover and reuse than loose prompt snippets.
  • Marked as GitHub, which helps users judge trust and maintenance expectations.
  • Keeps the original source link available for repository, documentation, or discussion follow-up.
  • Tagged with github, community, claude, mcp, so it can be filtered by concrete task intent.

Install and use

Install
Copy Install Command
focus install research-synthesize-050103
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Detail Preview

SKILL.md

Primary filemarkdown3 KB

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]

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