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Data Analyzer
作者
jpengcheng523-netizen
· GitHub ↗
· v1.0.0
· MIT-0
395
总下载
0
收藏
2
当前安装
1
版本数
在 OpenClaw 中安装
/install jpeng-data-analyzer
功能描述
Data analysis and visualization skill. Supports CSV, Excel, JSON data with statistical analysis, charts, and reports.
使用说明 (SKILL.md)
Data Analyzer
Analyze datasets and generate visualizations with statistical insights.
When to Use
- User wants to analyze a dataset
- Generate charts and visualizations
- Statistical analysis and summaries
- Data cleaning and transformation
Features
- Data formats: CSV, Excel, JSON, Parquet
- Statistics: Mean, median, std dev, correlations
- Visualizations: Bar, line, pie, scatter, heatmap
- Reports: Auto-generated analysis reports
Usage
Quick analysis
python3 scripts/analyze.py \
--input ./data.csv \
--output ./report/
Generate specific chart
python3 scripts/analyze.py \
--input ./data.csv \
--chart bar \
--x "category" \
--y "sales" \
--output ./chart.png
Statistical summary
python3 scripts/analyze.py \
--input ./data.csv \
--stats \
--columns "age,income,score"
Correlation analysis
python3 scripts/analyze.py \
--input ./data.csv \
--correlation \
--output ./correlation_matrix.png
Data transformation
python3 scripts/analyze.py \
--input ./data.csv \
--transform "normalize" \
--columns "price,quantity" \
--output ./normalized.csv
Output
{
"success": true,
"rows": 1000,
"columns": 10,
"stats": {
"mean": {"age": 35.2, "income": 55000},
"std": {"age": 12.3, "income": 15000}
},
"charts": ["./chart1.png", "./chart2.png"],
"report": "./report.html"
}
安全使用建议
This skill's README-like instructions assume a local script (scripts/analyze.py) and Python libraries, but the skill package contains no code or install steps. Before installing or enabling it: 1) Ask the publisher for the source code or a homepage and verify the analyze.py script and dependency list (pandas, matplotlib/seaborn, pyarrow/openpyxl, etc.). 2) If you intend to run the referenced script, inspect it for unexpected network calls, credential access, or data exfiltration. 3) If you want a drop-in skill, prefer one that includes code or a clear install spec from a trusted release (GitHub release, PyPI package, etc.). 4) If you try this locally, run it in an isolated environment (container or VM) with non-sensitive sample data first. 5) If the maintainer can't provide code or justification for missing artifacts, treat the skill as incomplete and avoid granting it access to sensitive data.
功能分析
Type: OpenClaw Skill
Name: jpeng-data-analyzer
Version: 1.0.0
The skill bundle contains metadata and documentation for a data analysis tool. The SKILL.md file describes standard data processing, visualization, and statistical analysis tasks using a script named scripts/analyze.py. No malicious instructions, prompt injections, or suspicious behaviors are present in the provided files.
能力评估
Purpose & Capability
Name/description claim data analysis and visualization (CSV/Excel/JSON/Parquet, charts, stats). Achieving that legitimately requires a Python script or binary plus libraries (pandas, matplotlib/seaborn, pyarrow/openpyxl, etc.), but the package contains no code, binaries, or install spec. The declared requirements (none) do not match the capabilities described.
Instruction Scope
SKILL.md instructs the agent or user to run 'python3 scripts/analyze.py' with various flags and to read/write local files (input data and output charts/reports). That is scoped to data analysis, but the referenced script path (scripts/analyze.py) is not present in the skill and no fallback instructions are provided. The instructions do not request credentials or external endpoints, which is good, but they presume local tooling that isn't included.
Install Mechanism
No install spec (instruction-only), which is lower technical risk from installation. However, because the instructions expect a local script and likely Python libraries, the lack of an install mechanism or list of dependencies is a usability and coherence problem: users or agents would need to supply the missing code/tooling themselves.
Credentials
The skill requests no environment variables, no credentials, and no config paths. This is proportionate to a local data-analysis helper that operates on user-provided files.
Persistence & Privilege
always is false and the skill does not request persistent privileges or modify other skills or system-wide settings. It does instruct writing output files to user-specified paths (expected for this purpose).
如何使用
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install jpeng-data-analyzer - 安装完成后,直接呼叫该 Skill 的名称或使用
/jpeng-data-analyzer触发 - 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
Initial release of jpeng-data-analyzer.
- Supports analysis and visualization of CSV, Excel, JSON, and Parquet data.
- Provides statistical summaries (mean, median, standard deviation, correlations).
- Generates a variety of charts: bar, line, pie, scatter, and heatmap.
- Offers auto-generated analysis reports.
- Includes data cleaning and transformation features.
元数据
常见问题
Data Analyzer 是什么?
Data analysis and visualization skill. Supports CSV, Excel, JSON data with statistical analysis, charts, and reports. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 395 次。
如何安装 Data Analyzer?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install jpeng-data-analyzer」即可一键安装,无需额外配置。
Data Analyzer 是免费的吗?
是的,Data Analyzer 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。
Data Analyzer 支持哪些平台?
Data Analyzer 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。
谁开发了 Data Analyzer?
由 jpengcheng523-netizen(@jpengcheng523-netizen)开发并维护,当前版本 v1.0.0。
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