meta-analysis-forest-plotter
Use when creating forest plots for meta-analyses, visualizing effect sizes across studies, or generating publication-ready meta-analysis figures. Produces high-quality forest plots with confidence intervals, heterogeneity metrics, and subgroup analyses.
npx skills add majiayu000/claude-skill-registry --skill meta-analysis-forest-plotter --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 Forest Plot Generator Create publication-ready forest plots for systematic reviews and meta-analyses with customizable styling and statistical annotations. ## Quick Start ```python from scripts.forest_plotter import ForestPlotter plotter = ForestPlotter() # Generate forest plot plot = plotter.create_plot( studies=["Study A", "Study B", "Study C"], effect_sizes=[1.2, 0.8, 1.5], ci_lower=[0.9, 0.5, 1.1], ci_upper=[1.5, 1.1, 1.9], overall_effect=1.15 ) ``` ## Core Capabilities ### 1. Basic Forest Plot ```python fig = plotter.plot( data=studies_df, effect_col="HR", ci_lower_col="CI_lower", ci_upper_col="CI_upper", study_col="study_name" ) ``` **Required Data Columns:** - Study name/identifier - Effect size (OR, HR, RR, MD, etc.) - Confidence interval lower bound - Confidence interval upper bound - Weight (optional, for precision) ### 2. Statistical Annotations ```python fig = plotter.plot_with_stats( data, heterogeneity_stats={ "I2": 45.2, "p_value": 0.03, "Q_statistic": 18.4 }, overall_effect={ "estimate": 1.15, "ci": [0.98, 1.35], "p_value": 0.08 } ) ``` **Heterogeneity Metrics:** | Metric | Interpretation | |--------|---------------| | I² < 25% | Low heterogeneity |
- Quick Start
- Core Capabilities
- 1. Basic Forest Plot
- 2. Statistical Annotations
- 3. Subgroup Analysis
- 4. Custom Styling
- CLI Usage
- Output Formats
- References
From CSV data python scripts/forest_plotter.py \ With custom styling
What does the meta-analysis-forest-plotter skill do?
Use when creating forest plots for meta-analyses, visualizing effect sizes across studies, or generating publication-ready meta-analysis figures. Produces high-quality forest plots with confidence intervals, heterogeneity metrics, and subgroup analyses.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill meta-analysis-forest-plotter --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 majiayu000/claude-skill-registry, a repository with 534 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.