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Install in OpenClaw
/install universal-data-analyst
Description
基于数据本体论自动识别数据类型,生成分析方案及脚本,输出数据质量报告和多格式智能分析报告,支持多种数据格式。
Usage Guidance
Before installing or running this skill, consider the following:
- Do not enable fully autonomous execution unless you trust the environment and have strict sandboxing (e.g., no outbound network, limited filesystem rights, isolated VM/container).
- The skill generates Python scripts from LLM output and can execute them. Always review generated scripts manually (use the step-by-step mode) before executing, especially if the data is sensitive or the runtime has network/database access.
- The package doesn’t declare required LLM API keys or DB creds, but its examples expect you to use external LLMs or DB connection strings — treat those credentials as sensitive. Provide them only in controlled ways and prefer ephemeral/separated accounts.
- If you need to run this in production, run it in a sandbox with egress blocked or restricted, and use least-privilege credentials for any database connections.
- Note the claimed "no hardcoding / always LLM-driven" promise is not strictly true: the code includes heuristics and defaults. Expect hybrid LLM+heuristic behavior.
- If possible, run the code audit (search for subprocess, os.system, exec, open(..., 'w'), network libraries) and add explicit guardrails (denylist network calls, require signed/approved scripts) before enabling autonomous use.
Capability Analysis
Type: OpenClaw Skill
Name: universal-data-analyst
Version: 1.0.3
The skill bundle implements a comprehensive data analysis workflow that includes high-risk capabilities, specifically the execution of LLM-generated Python scripts using `subprocess.run` in `orchestrator.py` and the ability to connect to arbitrary SQL databases via `layers/data_loader.py`. While these features are aligned with the stated purpose of automated data analysis, they represent a significant security risk for Remote Code Execution (RCE) if the LLM is manipulated via prompt injection or if malicious inputs are provided. No evidence of intentional malice, such as hardcoded exfiltration endpoints or unauthorized file access, was found in the code logic.
Capability Tags
Capability Assessment
Purpose & Capability
The SKILL.md emphasizes that every decision is done 'by the LLM' and 'no keyword hardcoding', but the code contains clear heuristics and hard-coded lists (e.g., _detect_join_keys candidate_names, default ontology values). The skill both generates LLM prompts and also implements local heuristics/processing — that's not itself malicious but contradicts the 'no hardcoding' claim and should be understood by users.
Instruction Scope
The runtime instructions and code generate LLM prompts, request the user (or autonomous flow) to obtain LLM responses, then generate full Python analysis scripts and execute them. Executing code created by an LLM is inherently risky: a generated script could contain network calls, file-system operations, or other exfiltration logic. The SKILL.md and orchestrator allow 'autonomous' modes and skipping manual review; the code also includes subprocess and execution plumbing (truncated but referenced). The skill also supports SQL connection strings — this implies access to databases but the skill does not declare or limit how credentials are used. There are explicit instructions/examples showing how to call an external LLM (Anthropic) but no enforced or declared guardrails or required manual review step.
Install Mechanism
No install spec; the package is delivered as code files and relies on standard Python dependencies. That lowers supply-chain risk compared to fetching arbitrary archives or binaries. Dependencies listed are normal data-analysis libs (pandas, numpy, matplotlib, etc.).
Credentials
The skill declares no required environment variables or primary credential, but the README and SKILL.md show examples of calling external LLM APIs (Anthropic) and mention SQL connection strings. In practice, using this skill will typically require API keys or DB credentials supplied by the user — the package does not request or document required env vars in its manifest, which is a mismatch the user should be aware of.
Persistence & Privilege
always:false (good), but the platform default allows autonomous invocation and the SKILL.md/code reference autonomous modes and automated end-to-end flows. Combined with the ability to auto-generate and execute LLM-produced scripts and to accept database connection strings/files, this increases blast radius: if invoked autonomously and paired with model access, the flow could run arbitrary code without human review. The skill does not include explicit sandboxing or network-restriction guidance.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install universal-data-analyst - After installation, invoke the skill by name or use
/universal-data-analyst - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.3
No changes detected in this version.
- Version 1.0.2 has no file changes compared to the previous release.
v1.0.2
No changes detected in this version.
- Version 1.0.2 has no file changes compared to the previous release.
v1.0.1
**v1.0.1 小版本更新:引入流程健康监控和编码容错支持**
- 增加“流程健康监控”功能:分析流程每步新增状态追踪与依赖检查,失败时输出清晰错误提示和修复建议
- 增强数据加载编码容错机制:CSV/TSV 文件支持自动编码检测和多编码尝试,提升中文文件兼容性
- 遇到数据加载失败或严重异常时,自动中断流程并给出详细指导
- 新增文件:流程健康监控说明文档与 Python 实现脚本
v1.0.0
Universal Data Analyst v1.0.0
- Initial release featuring a universal, ontology-driven data analysis system.
- Supports a wide range of data types (economic and non-economic) and file formats (CSV, Excel, Parquet, JSON, SQL).
- Automated 7-step analysis workflow: from data loading and ontology identification to report generation.
- No rule-based keyword matching; all decisions made through model reasoning.
- Outputs include data quality reports, executable analysis scripts, HTML/Markdown reports, and visualizations.
Metadata
Frequently Asked Questions
What is universal-data-analyst?
基于数据本体论自动识别数据类型,生成分析方案及脚本,输出数据质量报告和多格式智能分析报告,支持多种数据格式。 It is an AI Agent Skill for Claude Code / OpenClaw, with 345 downloads so far.
How do I install universal-data-analyst?
Run "/install universal-data-analyst" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is universal-data-analyst free?
Yes, universal-data-analyst is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does universal-data-analyst support?
universal-data-analyst is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created universal-data-analyst?
It is built and maintained by yamaz (@yamaz49); the current version is v1.0.3.
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