Best use case
meta-analysis-engine is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Skill for conducting meta-analyses of research findings
Teams using meta-analysis-engine should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/meta-analysis-engine/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How meta-analysis-engine Compares
| Feature / Agent | meta-analysis-engine | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Skill for conducting meta-analyses of research findings
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# Meta-Analysis Engine Skill ## Purpose Conduct meta-analyses to synthesize effect sizes across studies and estimate pooled effects with heterogeneity assessment. ## Capabilities - Extract effect sizes - Calculate pooled estimates - Assess heterogeneity - Perform subgroup analyses - Generate forest plots - Evaluate publication bias ## Usage Guidelines 1. Extract study data 2. Calculate effect sizes 3. Pool estimates 4. Assess heterogeneity 5. Perform subgroup analyses 6. Evaluate bias ## Process Integration Works within scientific discovery workflows for: - Systematic reviews - Evidence synthesis - Research summarization - Effect estimation ## Configuration - Effect size metrics - Pooling methods - Heterogeneity tests - Visualization options ## Output Artifacts - Pooled estimates - Forest plots - Heterogeneity statistics - Bias assessments
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