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版本数
在 OpenClaw 中安装
/install universal-data-analyst-en
功能描述
Performs automated, LLM-driven data analysis including loading, validation, method selection, script generation, execution, and comprehensive reporting for d...
安全使用建议
This skill is coherent with its stated purpose, but it generates Python analysis scripts via LLM prompts and can execute those scripts locally. Before installing or running: 1) Do NOT run this on sensitive or production systems without reviewing generated scripts first. 2) Inspect any generated analysis_script.py for network, subprocess, or filesystem operations (look for imports like requests, socket, subprocess, os.system, eval/exec, urllib, ftplib, paramiko). 3) Prefer running the orchestration and script execution inside an isolated environment (ephemeral VM, container, or sandbox) with limited network and file access. 4) If you will call external LLMs, keep API keys separate and only use trusted endpoints; the skill does not manage credentials. 5) Consider using the human-in-the-loop mode (generate prompts and scripts but manually review/execute) rather than fully autonomous execution. If you want me to, I can: (a) scan the full repository for occurrences of subprocess/requests/os.system/eval/exec/network endpoints, or (b) point to specific lines/functions to review before running.
功能分析
Type: OpenClaw Skill
Name: universal-data-analyst-en
Version: 1.0.3
The skill bundle implements a 'Universal Data Analyst' that uses LLMs to generate and execute Python code. The core risk lies in `orchestrator.py`, which uses `subprocess.run` to execute scripts generated by an LLM based on user-provided data and 'intent' strings, creating a significant Remote Code Execution (RCE) surface via prompt injection. Additionally, `layers/data_loader.py` supports loading data from SQL databases via arbitrary connection strings and executing SQL queries. While these capabilities are aligned with the stated purpose of the tool and no explicit evidence of malicious intent (such as hardcoded exfiltration URLs or credential theft) was found, the high-risk execution model and broad data access permissions warrant a suspicious classification.
能力标签
能力评估
Purpose & Capability
Name/description match the included modules (data loader, validator, orchestrator, LLM prompt generator, report generator). The code produces prompts for LLMs and coordinates a multi-step analysis pipeline, which is coherent with the stated purpose. Minor mismatch: README/SKILL.md includes example calls to an LLM client (Anthropic/Claude) but the shipped LLM module returns prompts rather than performing network calls and the skill declares no required env vars/credentials for LLM access.
Instruction Scope
The orchestrator generates full Python analysis scripts (via LLM prompts) and then executes them (the orchestrator imports subprocess and contains step execution logic). Executing code generated by an LLM on the user's machine is expected for this tool's purpose but is a high-risk action: generated scripts can contain arbitrary file I/O, shell/OS calls, or network operations and thus may exfiltrate data or modify the system. The SKILL.md and code instruct saving prompt files and calling an LLM externally — but the skill also supports an autonomous flow that can generate and run code. There are no enforced sandboxing or restrictions in the provided code.
Install Mechanism
There is no install spec (instruction-only skill with packaged Python modules). Nothing is downloaded at install time, so no arbitrary remote code is pulled during installation. The runtime will write output and prompt files to local output directories.
Credentials
The skill declares no required environment variables or credentials. However, documentation/examples reference calling external LLM APIs (Anthropic/Claude) which would require API keys if you choose to integrate — these keys are not managed by the skill. The shipped code itself does not appear to read unrelated system credentials or config paths.
Persistence & Privilege
always:false and no special persistence or modifications to other skills/configs. The skill creates session/output directories within the working directory; it does not request or claim system-wide privileges. Autonomous invocation is allowed by platform default, which combined with script execution increases blast radius but is not itself an unusual setting.
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 universal-data-analyst-en - 安装完成后,直接呼叫该 Skill 的名称或使用
/universal-data-analyst-en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
v1.0.3
v1.0.2 of Universal Data Analyst
- No functional changes or user-visible updates.
- Internal version increment only; documentation and features remain unchanged.
v1.0.2
v1.0.2 of Universal Data Analyst
- No functional changes or user-visible updates.
- Internal version increment only; documentation and features remain unchanged.
v1.0.1
**Flow Health Monitoring and Fault Tolerance Added**
- Introduced Flow Health Monitoring: Tracks analysis step status, checks dependencies, and provides detailed error messages with fix suggestions.
- Enhanced file loading reliability: Automatic detection and fallback for multiple encodings (utf-8, gbk, etc.) and CSV engine fallback when errors are encountered.
- Output now includes a comprehensive flow health report with execution status and issue summary.
- Added documentation: FLOW_HEALTH_MONITOR_GUIDE.md and UPDATES.md describe new monitoring and fault tolerance features.
v1.0.0
Universal Data Analyst 2.0.0
- Introduces open-ended data analysis using data ontology and LLM-based reasoning—no hardcoded keyword rules.
- Automatically adapts analysis to economic and non-economic data types.
- Adds mandatory data quality validation with integrated results in reports.
- Supports CSV, Excel, Parquet, JSON, and SQL data formats with autonomous and LLM-powered modes.
- Generates HTML and Markdown reports with detailed diagnostics, analysis, and charts.
- Enables both command-line and Python API usage for flexible integration.
元数据
常见问题
universal-data-analyst-en 是什么?
Performs automated, LLM-driven data analysis including loading, validation, method selection, script generation, execution, and comprehensive reporting for d... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 152 次。
如何安装 universal-data-analyst-en?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install universal-data-analyst-en」即可一键安装,无需额外配置。
universal-data-analyst-en 是免费的吗?
是的,universal-data-analyst-en 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。
universal-data-analyst-en 支持哪些平台?
universal-data-analyst-en 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 universal-data-analyst-en?
由 yamaz(@yamaz49)开发并维护,当前版本 v1.0.3。
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