meta-analysis-guide
Conduct systematic meta-analyses with effect size pooling and heterogeneity
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill meta-analysis-guide --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Meta-Analysis Guide A skill for conducting rigorous meta-analyses: computing and pooling effect sizes, assessing heterogeneity, evaluating publication bias, and generating forest plots. Follows Cochrane Handbook and PRISMA guidelines. ## Effect Size Computation ### Common Effect Size Measures | Measure | Use Case | Formula | Interpretation | |---------|----------|---------|----------------| | Cohen's d | Mean difference (2 groups) | (M1 - M2) / S_pooled | 0.2 small, 0.5 medium, 0.8 large | | Hedges' g | d with small-sample correction | d * J(df) | Preferred over d for small N | | Pearson r | Correlation | r | 0.1 small, 0.3 medium, 0.5 large | | Odds Ratio | Binary outcomes | (a*d)/(b*c) | 1 = no effect | | Risk Ratio | Binary outcomes | (a/(a+b))/(c/(c+d)) | 1 = no effect | | SMD | Standardized mean difference | Same as Hedges' g | When scales differ | ### Computing Effect Sizes in Python ```python import numpy as np from dataclasses import dataclass @dataclass class EffectSize: estimate: float variance: float se: float ci_lower: float ci_upper: float measure: str def cohens_d(m1: float, m2: float, sd1: float, sd2: float, n1: int, n2: int) -> EffectSize: """ Compute Hedges' g (b
- Effect Size Computation
- Common Effect Size Measures
- Computing Effect Sizes in Python
- Fixed-Effect and Random-Effects Models
- Inverse-Variance Pooling
- Forest Plot
- Publication Bias Assessment
- Reporting Standards
What does the meta-analysis-guide skill do?
Conduct systematic meta-analyses with effect size pooling and heterogeneity
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill meta-analysis-guide --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Auto-Empirical-Research-Skills, a repository with 3,244 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.