蒲公英数据开发工程师Skill套件
v1.0.0蒲公英数据开发工程师Skill套件 - 专为数据开发工程师设计的完整AI Skill生态系统。 包含7个核心模块：需求分析、架构设计、数据建模、SQL开发、ETL Pipeline、数据质量、数据测试。 当用户需要端到端数据开发解决方案、数据仓库建设、ETL开发、SQL优化、数据质量管理时触发。 触发词：数据开发...
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
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OpenClaw
Benign
high confidencePurpose & Capability
Name/description correspond to the included modules (requirement analysis, architecture, modeling, SQL, ETL, DQ, testing). The repository contains module docs, examples and project-initialization scripts that are appropriate for a data-engineering assistant. There are no unrelated environment variables, binaries, or external service credentials declared.
Instruction Scope
Module frontmatter and SKILL.md authorize agent operations such as Read/Edit/Write/Bash for generating models, docs and scaffolding projects; this is expected for a code-and-docs generator. Note: those capabilities let the agent create files and run the included init-project.sh scripts in the workspace — review and run scripts in a safe/isolated directory before executing.
Install Mechanism
No install spec (instruction-only skill) and no downloads from external URLs. Provided shell scripts are local project scaffolding; there is no evidence of package installation or remote extract operations that would write arbitrary third-party binaries to disk.
Credentials
The skill does not request any environment variables or credentials. However, its documentation and scripts assume you'll supply dbt/profile or database connection information to actually run dbt or connect to databases; those credentials are not requested by the skill but will be required by downstream tools if you run them. Keep DB credentials and profiles.yml/.env files out of version control.
Persistence & Privilege
always:false and default autonomous invocation are normal. The skill writes files within user-specified project directories (init scripts) but does not claim or require persistent system-wide privileges or modify other skills' configs.
Assessment
This package appears coherent for scaffolding data-engineering projects and generating SQL/dbt artifacts. Before installing or running anything: (1) inspect the included init-project.sh scripts (they create files, copy docs, run sed) and run them only in an isolated/new project directory; (2) never paste production DB credentials into the skill UI — keep database profiles in secure storage and .env/profiles.yml out of git; (3) if you plan to run generated dbt or ETL code, do so in a test environment first; (4) if you need stronger assurance, review the omitted/truncated files for any network calls or unexpected shell use before invoking Bash tools.Like a lobster shell, security has layers — review code before you run it.
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License
MIT-0
Free to use, modify, and redistribute. No attribution required.