meta-analysis
Statistical methods for combining results across multiple studies. Use when aggregating cross-study or cross-experiment results.
DeepseekModel
官方收录技能
质量 优秀 · 90
v1.0.0
获取
https://deepseekmodel.com/api/download.php?id=aiming-lab-autoresearchclaw-researchclaw-skills-builtin-experiment-meta-analysis-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 meta-analysis description Statistical methods for combining results across multiple studies. Use when aggregating cross-study or cross-experiment results. metadata {"category":"experiment","trigger-keywords":"meta-analysis,effect size,pooled,cross-study,aggregat","applicable-stages":"7,14","priority":"5","version":"1.0","author":"researchclaw","references":"Borenstein et al., Introduction to Meta-Analysis, 2009"} Meta-Analysis Best Practice When comparing results across studies or experiments: Report effect sizes, not just p-values Use standardized metrics for cross-study comparison Account for heterogeneity (different setups, datasets, seeds) Report confidence intervals alongside point estimates Use forest plots to visualize cross-study comparisons Identify and discuss outliers or inconsistent results Consider publication bias when interpreting aggregate results
Agent 识别该技能的关键词，点击任意一个即可复制。
该技能未提供触发词。
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） |
