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.
84
86%
Does it follow best practices?
Impact
64%
1.42xAverage score across 3 eval scenarios
Passed
No known issues
Evaluation results
92%
20%Cardiovascular Meta-Analysis Forest Plot
CLI usage and CSV data format
CSV column names
100%
100%
Script entrypoint used
0%
100%
PNG output format
100%
100%
Output filename matches spec
100%
100%
Heterogeneity I² reported
100%
100%
Q statistic reported
100%
100%
Q p-value reported
100%
100%
Heterogeneity interpretation
100%
100%
Dependencies installed
0%
0%
Null value appropriate
100%
100%
Model flag used
0%
100%
42%
3%Oncology Treatment Subgroup Meta-Analysis
Subgroup analysis and Python API usage
Correct import path
0%
100%
subgroup_plot() method used
28%
0%
subgroup_col parameter
20%
0%
subgroups list parameter
60%
0%
Statistical annotations included
25%
33%
heterogeneity_stats dict structure
12%
0%
overall_effect dict structure
12%
0%
JSON input parsed correctly
100%
100%
Heterogeneity threshold interpretation
40%
100%
Three subgroups reported
100%
100%
60%
36%Preparing a Forest Plot for Journal Submission
Journal styling and publication output format
style='publication'
0%
0%
journal parameter set
0%
0%
color_scheme='monochrome'
0%
0%
show_weights=True
0%
100%
Journal-submission output format
100%
100%
null_value set to 1
0%
100%
Skill library imported
0%
100%
Format justification in checklist
100%
100%
Styling params documented
0%
50%
- Repository
- aipoch/medical-research-skills
- Commit
ca9aaa4
- Evaluated
- Agent
- Claude Code
- Model
- Claude Sonnet 4.6
Table of Contents
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