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Data Analyzer
by
jpengcheng523-netizen
· GitHub ↗
· v1.0.0
· MIT-0
395
Downloads
0
Stars
2
Active Installs
1
Versions
Install in OpenClaw
/install jpeng-data-analyzer
Description
Data analysis and visualization skill. Supports CSV, Excel, JSON data with statistical analysis, charts, and reports.
README (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"
}
Usage Guidance
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.
Capability Analysis
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.
Capability Assessment
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).
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install jpeng-data-analyzer - After installation, invoke the skill by name or use
/jpeng-data-analyzer - Provide required inputs per the skill's parameter spec and get structured output
Version History
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.
Metadata
Frequently Asked Questions
What is Data Analyzer?
Data analysis and visualization skill. Supports CSV, Excel, JSON data with statistical analysis, charts, and reports. It is an AI Agent Skill for Claude Code / OpenClaw, with 395 downloads so far.
How do I install Data Analyzer?
Run "/install jpeng-data-analyzer" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Data Analyzer free?
Yes, Data Analyzer is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Data Analyzer support?
Data Analyzer is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Data Analyzer?
It is built and maintained by jpengcheng523-netizen (@jpengcheng523-netizen); the current version is v1.0.0.
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