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.
Install with Tessl CLI
npx tessl i github:aipoch/medical-research-skills --skill meta-analysis-forest-plotter95
Does it follow best practices?
Validation for skill structure
Discovery
100%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is an excellent skill description that clearly defines a specialized niche (meta-analysis forest plots) with explicit trigger guidance and specific capabilities. It uses appropriate third-person voice, includes domain-specific terminology that researchers would naturally use, and clearly distinguishes itself from general visualization tools.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'creating forest plots', 'visualizing effect sizes across studies', 'generating publication-ready meta-analysis figures', plus mentions specific outputs like 'confidence intervals, heterogeneity metrics, and subgroup analyses'. | 3 / 3 |
Completeness | Explicitly answers both what ('Produces high-quality forest plots with confidence intervals, heterogeneity metrics, and subgroup analyses') and when ('Use when creating forest plots for meta-analyses, visualizing effect sizes across studies, or generating publication-ready meta-analysis figures'). | 3 / 3 |
Trigger Term Quality | Includes natural keywords users would say: 'forest plots', 'meta-analyses', 'effect sizes', 'studies', 'confidence intervals', 'heterogeneity', 'subgroup analyses'. These are domain-appropriate terms researchers would naturally use. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive niche focused specifically on forest plots and meta-analysis visualization. Unlikely to conflict with general charting or data visualization skills due to the specialized statistical/research domain terminology. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
87%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a strong, well-crafted skill that efficiently teaches forest plot generation with concrete, executable examples. The content respects token budget while providing comprehensive coverage of capabilities. Minor weakness is the lack of explicit validation steps or error handling guidance for data preparation.
Suggestions
Add a brief data validation step before plotting (e.g., checking for missing values, ensuring CI bounds are valid)
Include a simple troubleshooting section or common error patterns when input data is malformed
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is lean and efficient, providing only necessary information. No explanations of what forest plots or meta-analyses are—assumes Claude knows. Every section delivers actionable content without padding. | 3 / 3 |
Actionability | Provides fully executable Python code examples with specific parameters, CLI commands with flags, and concrete data column requirements. Code is copy-paste ready with realistic parameter names. | 3 / 3 |
Workflow Clarity | While individual operations are clear, there's no explicit workflow for the full process (data preparation → validation → plot generation → export). Missing validation steps for input data format or error handling guidance. | 2 / 3 |
Progressive Disclosure | Well-structured with clear sections progressing from Quick Start to advanced features. References to external files (forest-plot-styles.md, sample-plots/) are one level deep and clearly signaled. | 3 / 3 |
Total | 11 / 12 Passed |
Validation
100%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 11 / 11 Passed
Validation for skill structure
No warnings or errors.
- Reviewed
Table of Contents
Is this your skill?
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.