メニュー
Run statistical meta-analysis in R with renv, generate effect estimates, heterogeneity, and publication bias diagnostics, and export figures and tables. Use when analyzing extracted study data.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Orchestrate agent team for meta-analysis pipeline. Creates team, spawns teammates, manages task list and handoffs between pipeline stages.
Define extraction schema, extract study data from full texts, and store it in a structured database for meta-analysis. Use when moving from full-text collection to statistical analysis.
End-to-end AI-assisted meta-analysis pipeline orchestration from TOPIC.txt to final manuscript and reviewer responses. Use when the user provides a topic and wants the full meta-analysis workflow, tracking, and final paper.
Collect and manage full-text PDFs for included studies, track provenance, and prepare documents for extraction. Use when moving from screening to data extraction.
Draft and render a meta-analysis manuscript with Quarto using an IMRaD structure and embedded figures/tables. Use when preparing the final paper from analysis outputs.
Perform title and abstract screening, apply inclusion and exclusion criteria, and assess study quality or risk of bias. Use when selecting eligible studies for meta-analysis.
| name | ma-meta-analysis |
| description | Run statistical meta-analysis in R with renv, generate effect estimates, heterogeneity, and publication bias diagnostics, and export figures and tables. Use when analyzing extracted study data. |
Analyze extracted data using standard meta-analysis methods and produce validated outputs.
05_extraction/extraction.csv05_extraction/data-dictionary.md06_analysis/01_setup.R06_analysis/02_effect_sizes.R06_analysis/03_models.R06_analysis/04_subgroups_meta_regression.R06_analysis/05_plots.R06_analysis/06_tables.R06_analysis/07_sensitivity.R06_analysis/08_bias.R06_analysis/09_validation.R06_analysis/07_export_tables.R06_analysis/figures/ PNG files at 300 dpi06_analysis/tables/ PNG + HTML + DOCX + CSV tables (via gt + flextable)06_analysis/validation.md06_analysis/renv.lockAll models must use REML + Hartung-Knapp (Cochrane mandate, July 2025):
# meta: metagen(..., method.tau = "REML", hakn = TRUE)
# metafor: rma(..., method = "REML", test = "knha")
Run DerSimonian-Laird as sensitivity analysis for comparison, but REML + HKSJ is the primary result.
renv in 06_analysis/ and record package versions.
renv::init() in 06_analysis/06_analysis/renv.lockassets/r/ into 06_analysis/ and adapt them to the study schema.
assets/r/01_setup.R → 06_analysis/01_setup.Rmetafor::escalc for the outcome type.
06_analysis/02_effect_sizes.R (use metafor::escalc() function)meta and/or metafor with REML + Hartung-Knapp defaults.
06_analysis/03_models.R (L10-30: metagen(..., method.tau = "REML", hakn = TRUE))06_analysis/04_subgroups_meta_regression.R06_analysis/07_sensitivity.R06_analysis/08_bias.R06_analysis/05_plots.R06_analysis/figures/*.png (png(..., res=300, width=3000, height=2400))gtsummary to build manuscript-ready summary tables.
06_analysis/06_tables.R (use gtsummary::tbl_summary())gt + flextable for manuscript sync.
06_analysis/07_export_tables.R06_analysis/tables/*.png, 06_analysis/tables/*.html, 06_analysis/tables/*.docx06_analysis/validation.md.
06_analysis/validation.mdassets/r/ provides a scaffolded R workflow that maps to the standard steps.references/r-meta-roadmap.md summarizes the expected analysis tasks.| Step | Skill | Stage |
|---|---|---|
| Prev | /ma-data-extraction | 05 Data Extraction |
| Next | /ma-manuscript-quarto | 07 Manuscript Drafting |
| All | /ma-end-to-end | Full pipeline orchestration |