Forest Plot Styler — 技能 — openclaw中文资讯站

安全扫描（ClawHub）

VirusTotal ：良性
OpenClaw ：良性

OpenClaw 评估

The skill's code, instructions, and dependencies are coherent with its stated purpose (creating/beautifying forest plots) but there are a few implementation/packaging issues and minor safety notes you should review before running it.

目的

Name/description align with what the files do: the SKILL.md and scripts/main.py read CSV/Excel input, calculate pooled effects, and draw forest plots with styling options. There are no unrelated credentials, binaries, or surprising capabilities requested.

说明范围

Runtime instructions instruct running the included Python script on user-supplied input files (CSV/XLSX) and writing output images — this matches the purpose. The SKILL.md includes a security checklist that recommends denying path traversal (../), but the script does not implement explicit path restriction or sandboxing; it will read any file path the user supplies. That is expected for a command-line tool but you should avoid running it in co…

安装机制

No install spec (instruction-only install) which keeps disk writes minimal. Dependencies are listed in requirements.txt but are unpinned and incomplete: SKILL.md mentions openpyxl for Excel support, yet requirements.txt does not include openpyxl. This is likely an oversight (packaging inconsistency) rather than malicious, but you should pin and audit dependencies before installing.

证书

The skill requests no environment variables, no credentials, and no config paths. Its filesystem access is limited to reading the input file you provide and writing the output image, which is proportionate to the stated purpose.

持久

The skill does not request persistent or privileged platform presence (always:false). It does not attempt to modify other skills or system-wide agent settings. Autonomous invocation is allowed by default (disable-model-invocation:false), but that is platform default and not in itself a red flag here.

综合结论

This skill appears to do what it claims (make styled forest plots) and contains only a single Python script plus example data. Before installing/running: 1) Review the script yourself or run it on non-sensitive sample data — it will read any file path you give it, so avoid passing paths to private system files. 2) Update and audit dependencies: requirements.txt is unpinned and omits openpyxl (needed for Excel input per SKILL.md). 3) Run in a s…

安装（复制给龙虾 AI）

将下方整段复制到龙虾中文库对话中，由龙虾按 SKILL.md 完成安装。

请把本段交给龙虾中文库（龙虾 AI）执行：为本机安装 OpenClaw 技能「Forest Plot Styler」。简介：Beautify meta-analysis forest plots with customizable odds ratio points and con…。
请 fetch 以下地址读取 SKILL.md 并按文档完成安装：https://raw.githubusercontent.com/openclaw/skills/refs/heads/main/skills/aipoch-ai/forest-plot-styler/SKILL.md
（来源：yingzhi8.cn 技能库）

SKILL.md

打开原始 SKILL.md（GitHub raw）

---
name: forest-plot-styler
description: Beautify meta-analysis forest plots with customizable odds ratio points
  and confidence intervals
version: 1.0.0
category: Visual
tags: []
author: AIPOCH
license: MIT
status: Draft
risk_level: Medium
skill_type: Tool/Script
owner: AIPOCH
reviewer: ''
last_updated: '2026-02-06'
---

# Forest Plot Styler

ID: 157

Beautifies Meta-analysis or subgroup analysis forest plots, customizes Odds Ratio point sizes and confidence interval line styles.

---

## Features

- Reads Meta-analysis data (CSV/Excel format)
- Draws high-quality forest plots
- Customizes Odds Ratio point sizes, colors, and shapes
- Customizes confidence interval line styles (color, thickness, endpoint style)
- Supports subgroup analysis display
- Automatically calculates and displays pooled effect values
- Outputs to PNG, PDF, or SVG format

---

## Usage

```bash
python scripts/main.py --input <data.csv> [options]
```

### Parameters

| Parameter | Type | Default | Required | Description |
|-----------|------|---------|----------|-------------|
| `--input`, `-i` | string | - | Yes | Input data file (CSV or Excel) |
| `--output`, `-o` | string | forest_plot.png | No | Output file path |
| `--format`, `-f` | string | png | No | Output format (png/pdf/svg) |
| `--point-size` | int | 8 | No | OR point size |
| `--point-color` | string | #2E86AB | No | OR point color |
| `--ci-color` | string | #2E86AB | No | Confidence interval line color |
| `--ci-linewidth` | int | 2 | No | Confidence interval line thickness |
| `--ci-capwidth` | int | 5 | No | Confidence interval endpoint width |
| `--summary-color` | string | #A23B72 | No | Pooled effect point color |
| `--summary-shape` | string | diamond | No | Pooled effect point shape |
| `--subgroup` | string | - | No | Subgroup analysis column name |
| `--title`, `-t` | string | Forest Plot | No | Chart title |
| `--xlabel`, `-x` | string | Odds Ratio (95% CI) | No | X-axis label |
| `--reference-line` | float | 1.0 | No | Reference line position |
| `--width`, `-W` | int | 12 | No | Image width (inches) |
| `--height`, `-H` | int | auto | No | Image height (inches) |
| `--dpi` | int | 300 | No | Image resolution |
| `--font-size` | int | 10 | No | Font size |
| `--style`, `-s` | string | default | No | Preset style (default/minimal/dark) |

---

## Input Data Format

CSV/Excel files must contain the following columns:

| Column Name | Description | Type |
|------|------|------|
| `study` | Study name | Text |
| `or` | Odds Ratio value | Numeric |
| `ci_lower` | Confidence interval lower bound | Numeric |
| `ci_upper` | Confidence interval upper bound | Numeric |
| `weight` | Weight (optional, for point size) | Numeric |
| `subgroup` | Subgroup label (optional) | Text |

### Sample Data

```csv
study,or,ci_lower,ci_upper,weight,subgroup
Study A,0.85,0.65,1.12,15.2,Drug A
Study B,0.72,0.55,0.94,18.5,Drug A
Study C,1.15,0.88,1.50,12.3,Drug B
Study D,0.95,0.75,1.20,14.8,Drug B
```

---

## Examples

### Basic Usage

```bash
python scripts/main.py -i meta_data.csv
```

### Custom Style

```bash
python scripts/main.py -i meta_data.csv 
    --point-color="#E63946" 
    --ci-color="#457B9D" 
    --point-size=10 
    --ci-linewidth=3 
    -t "Meta-Analysis of Treatment Effects"
```

### Subgroup Analysis

```bash
python scripts/main.py -i meta_data.csv 
    --subgroup subgroup_column 
    --summary-color="#F4A261" 
    -o subgroup_forest.png
```

### Output PDF Vector Graphic

```bash
python scripts/main.py -i meta_data.csv 
    -f pdf 
    -o forest_plot.pdf
```

---

## Preset Styles

### default
- Blue color scheme
- Standard font size
- White background

### minimal
- Clean lines
- Grayscale color scheme
- No grid lines

### dark
- Dark background
- Bright data points
- Suitable for dark theme presentations

---

## Dependencies

- Python >= 3.8
- matplotlib >= 3.5.0
- pandas >= 1.3.0
- numpy >= 1.20.0
- openpyxl >= 3.0.0 (for reading Excel)

---

## Output Example

Generated forest plot contains:
- Left side: Study name list
- Middle: OR values and confidence intervals
- Right side: Weight percentage (if available)
- Bottom: Pooled effect value (diamond marker)
- Reference line (OR=1)

---

## Notes

1. Ensure input file encoding is UTF-8
2. OR values are automatically converted when log scale is suggested
3. Studies with confidence intervals crossing 1 are not statistically significant
4. Weight values are used to adjust point size, reflecting study contribution

## Risk Assessment

| Risk Indicator | Assessment | Level |
|----------------|------------|-------|
| Code Execution | Python/R scripts executed locally | Medium |
| Network Access | No external API calls | Low |
| File System Access | Read input files, write output files | Medium |
| Instruction Tampering | Standard prompt guidelines | Low |
| Data Exposure | Output files saved to workspace | Low |

## Security Checklist

- [ ] No hardcoded credentials or API keys
- [ ] No unauthorized file system access (../)
- [ ] Output does not expose sensitive information
- [ ] Prompt injection protections in place
- [ ] Input file paths validated (no ../ traversal)
- [ ] Output directory restricted to workspace
- [ ] Script execution in sandboxed environment
- [ ] Error messages sanitized (no stack traces exposed)
- [ ] Dependencies audited
## Prerequisites

```bash
# Python dependencies
pip install -r requirements.txt
```

## Evaluation Criteria

### Success Metrics
- [ ] Successfully executes main functionality
- [ ] Output meets quality standards
- [ ] Handles edge cases gracefully
- [ ] Performance is acceptable

### Test Cases
1. **Basic Functionality**: Standard input → Expected output
2. **Edge Case**: Invalid input → Graceful error handling
3. **Performance**: Large dataset → Acceptable processing time

## Lifecycle Status

- **Current Stage**: Draft
- **Next Review Date**: 2026-03-06
- **Known Issues**: None
- **Planned Improvements**: 
  - Performance optimization
  - Additional feature support