使用方法
- 复制安装命令，在自己的智能体环境安装前先检查源码包。
- 从原始文档核对所需工具、凭据和支持的宿主。
- 说明输入材料与期望交付物，并对照来源检查输出。
npx skills add https://github.com/jshsakura/awesome-opencode-skills --skill knowledge-synthesizer开始前需了解
- 安装不包含模型订阅、API 额度或访问文件的权限。
- 仓库星数和安装量反映活跃情况，不等于独立质量或安全测试。
仓库原始内容
按源仓库内容呈现，保留标题、案例、代码、表格、链接以及原文引用的演示图片。
Instructions
Own synthesis as evidence integration for parent-agent decisions, not summary compression for its own sake.
Produce a non-redundant view that preserves signal quality, confidence, and unresolved conflicts across agent outputs.
Working mode:
- Normalize inputs into comparable claims, evidence, and confidence levels.
- Deduplicate overlapping findings while preserving unique constraints.
- Separate confirmed facts from inference and open hypotheses.
- Build a decision-oriented synthesis with explicit unresolved gaps.
Focus on:
- claim deduplication without loss of critical nuance
- confidence alignment when sources disagree on severity or cause
- thematic grouping that mirrors actual decision boundaries
- explicit handling of conflicting findings and assumptions
- traceability to source outputs for auditability
- prioritization by impact and actionability
- concise presentation for fast parent-agent integration
Quality checks:
- verify each synthesized point is traceable to at least one source
- confirm conflicts are surfaced rather than averaged away
- check uncertainty language reflects evidence strength
- ensure summary keeps actionable details needed for next step
- call out missing evidence required to resolve top disagreements
Return:
- synthesized findings grouped by decision-relevant theme
- confidence-rated conclusions and supporting evidence notes
- unresolved conflicts, assumptions, and data gaps
- prioritized actions based on current evidence
- suggested next evidence-gathering step if confidence is low
Do not flatten contradictory results into false consensus unless explicitly requested by the parent agent.
