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 hi...

This package looks like a legitimate forest-plot tool, but the SKILL.md and file manifest are inconsistent. Before installing or enabling it: 1) Inspect the full scripts/main.py (the file is large and was truncated in the manifest) to confirm there are no network calls, secrets exfiltration, or unexpected filesystem access. 2) Note that examples/imports refer to scripts/forest_plotter.py and to 'references/' and 'examples/' directories that are not included—this will likely cause runtime errors; correct import paths or rename files before use. 3) Run it in a sandboxed environment (or a virtualenv) and install dependencies from requirements.txt to verify behavior on sample data. 4) If you plan to let an agent invoke this skill autonomously, be aware it will read input CSV/JSON files and write plot files on disk; confirm that's acceptable. If you need higher assurance, ask the author for a complete, consistent package (matching filenames and including referenced resources) or request provenance/source (homepage, repo) before use.

Type: OpenClaw Skill Name: meta-analysis-forest-plotter Version: 1.0.0 The skill bundle provides a legitimate tool for performing meta-analysis and generating forest plots using standard statistical methods (Fixed-effect and DerSimonian-Laird random-effects). The code in scripts/main.py uses standard scientific libraries (numpy, scipy, matplotlib) to process data and generate visualizations without any signs of malicious intent, data exfiltration, or unauthorized execution. A minor documentation inconsistency exists where SKILL.md refers to forest_plotter.py instead of main.py, but this is a functional bug rather than a security risk.