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.
Best use case
meta-analysis-forest-plotter is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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.
Teams using meta-analysis-forest-plotter should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/meta-analysis-forest-plotter/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How meta-analysis-forest-plotter Compares
| Feature / Agent | meta-analysis-forest-plotter | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this 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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
Best AI Skills for ChatGPT
Find the best AI skills to adapt into ChatGPT workflows for research, writing, summarization, planning, and repeatable assistant tasks.
AI Agent for Product Research
Browse AI agent skills for product research, competitive analysis, customer discovery, and structured product decision support.
AI Agent for SaaS Idea Validation
Use AI agent skills for SaaS idea validation, market research, customer discovery, competitor analysis, and documenting startup hypotheses.
SKILL.md Source
# 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 |
| I² 25-50% | Moderate heterogeneity |
| I² > 50% | High heterogeneity |
| Q p-value < 0.05 | Significant heterogeneity |
### 3. Subgroup Analysis
```python
fig = plotter.subgroup_plot(
data,
subgroup_col="treatment_type",
subgroups=["Surgery", "Radiation", "Combined"]
)
```
### 4. Custom Styling
```python
fig = plotter.plot(
data,
style="publication",
journal="lancet", # or "nejm", "jama", "nature"
color_scheme="monochrome",
show_weights=True
)
```
## CLI Usage
```bash
# From CSV data
python scripts/forest_plotter.py \
--input meta_analysis_data.csv \
--effect-col OR \
--output forest_plot.pdf
# With custom styling
python scripts/forest_plotter.py \
--input data.csv \
--style lancet \
--width 8 --height 10
```
## Output Formats
- **PDF**: Publication quality, vector graphics
- **PNG**: Web/presentation, 300 DPI
- **SVG**: Editable in Illustrator/Inkscape
- **TIFF**: Journal submission format
## References
- `references/forest-plot-styles.md` - Journal-specific formatting
- `examples/sample-plots/` - Example outputs
---
**Skill ID**: 207 | **Version**: 1.0 | **License**: MITRelated Skills
Margin Analysis & Profit Optimization
Analyze gross, operating, and net margins by product line, customer segment, and channel. Identify margin erosion patterns and build pricing power.
Investment Analysis & Portfolio Management Engine
Complete investment analysis, portfolio construction, risk management, and trade execution methodology. Works across stocks, crypto, ETFs, bonds, and alternatives. Zero dependencies — pure agent skill.
FP&A Command Center — Financial Planning & Analysis Engine
You are a senior FP&A professional. You build financial models, run variance analysis, produce board-ready reports, and turn raw numbers into strategic decisions. You work with whatever data the user provides — spreadsheets, CSV, pasted numbers, or verbal estimates.
data-analysis-partner
智能数据分析 Skill，输入 CSV/Excel 文件和分析需求，输出带交互式 ECharts 图表的 HTML 自包含分析报告
onchain-contract-token-analysis
Analyze smart contracts, token mechanics, permissions, fee flows, upgradeability, market risks, and likely attack surfaces for onchain projects. Use when reviewing ERC-20s, launchpads, vaults, staking systems, LP fee routing, ownership controls, proxy setups, or suspicious token behavior.
resume-analysis
简历分析 skill。用于诊断整份简历的完整性、清晰度、岗位相关性、成果表达和结构质量。当用户说“分析简历”“看看我的简历”“简历诊断”时使用。
metacognitive-self-mod
Analyze and improve the improvement process. Use for detecting quality regressions and refining meta-optimization
akshare-stock-analysis
专业股票分析技能整合 akshare 数据 + 技术指标 + 板块轮动 + 持仓诊断。通过 AKShare CLI 调用A股/基金/期货实时行情数据，计算MACD/RSI/MA等技术指标，分析板块轮动趋势，诊断持仓风险与收益，输出结构化JSON结果。适用场景：股票行情查询、技术指标分析、持仓组合诊断、板块热点追踪、财经数据统计。触发关键词：分析股票、今日行情、持仓诊断、推荐股票、今日财经、股票代码、涨跌幅、北向资金。无需API密钥，直接调用 akshare 获取A股/基金/期货实时数据。
statistical-analysis-advisor
Recommends appropriate statistical methods (T-test vs ANOVA, etc.) based.
meta-ad-spy
Competitive intelligence skill for spying on competitor ads using Meta's Ad Library. Use this skill whenever the user wants to: research competitor Facebook/Instagram ads, analyze ad strategies, extract ad creatives or copy, find how long ads have been running, scout ad spend patterns, monitor industry advertising trends, or build any kind of competitor ad intelligence report. Triggers on phrases like "check competitor ads", "what ads is [brand] running", "spy on ads", "Facebook ad library", "Meta ad library", "scrape ads", "monitor ads", "ad intelligence", "ad research", or any request to analyze advertising strategies on Meta platforms. Always use this skill even if the user just mentions they want to understand what a competitor is doing on Facebook or Instagram.
contradiction-analysis
触发：当问题复杂、存在多个冲突因素、优先级不清，或你不知道应该先解决什么时调用；常见信号包括 trade-off、瓶颈、根因不明、主次不清、多个问题互相牵制。 English: Trigger when a problem contains competing forces, unclear priorities, or no obvious entry point. Use this skill to identify contradictions, isolate the principal contradiction, classify its nature, and choose the right response.
survey-analysis
AI-powered survey response analysis. Analyzes open-ended survey responses, clusters themes, detects sentiment, and generates actionable insights. Uses BERTopic + GPT-4o-mini.