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research-synthesize

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

DeepseekModel 官方收录技能 质量 优秀 · 90 v1.0.0

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https://deepseekmodel.com/api/download.php?id=ruvnet-ruflo-plugins-ruflo-goals-skills-research-synthesize-skill-md&format=skill
下载 .skill 标准格式,含 system_prompt 与 model_config,导入任意 Agent 框架即可使用
.skill 文件中 system_prompt 字段的实际内容。
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 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 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__plugin_ruflo-core_ruflo__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__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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下载的 .skill 包内含以下字段。
字段 说明
format格式标识(skill/v1)
skill_id技能唯一 ID
name技能名称
version版本号
description技能描述
category所属分类(数组)
trigger_words触发词列表
tags标签列表
source来源标识
source_url来源链接(本页地址)
exported_at导出时间(每次下载生成)
system_prompt系统提示词正文
model_config模型参数:provider / model / temperature / max_tokens / top_p
examples示例
install_guide各平台导入说明(Coze / Dify / Claude / 自定义框架)
同一份技能可按不同平台格式导出。
.skill 标准格式,含 system_prompt 与 model_config,导入任意 Agent 框架即可使用 下载
.skillpro 增强格式,额外含脚本 / 工具 / 依赖 / 钩子占位 下载
.json 纯 JSON 导出,只含 system_prompt 与模型参数 下载
Coze 带 frontmatter 的 Markdown,Coze 平台导入用 下载
Dify Dify DSL,创建应用后直接导入 下载

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