Ingeniero de datos
v1.0.0Design and build scalable data pipelines, ETL/ELT systems, and data infrastructure. Use when designing data architectures, choosing between batch and streami...
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MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
high confidencePurpose & Capability
Name and description (designing pipelines, ETL/ELT, quality, Airflow/dbt/Spark/Kafka guidance) match the provided reference docs and three scripts (pipeline generator, quality validator, ETL optimizer). There are no unrelated binaries, credentials, or config paths declared that would be inconsistent with the stated purpose.
Instruction Scope
SKILL.md instructs the agent to load local reference files under {baseDir}/references and to run the packaged scripts under {baseDir}/scripts. The instructions do not ask the agent to read arbitrary system files, environment variables, or remote endpoints beyond what is normal for data-engineering artifacts. It does generate DAGs and commands that will later require environment-specific connections (Airflow conn IDs, Snowflake/Postgres connection names), which is expected for this purpose.
Install Mechanism
No install spec or external downloads are present; this is an instruction-only skill with bundled Python scripts and docs. Nothing is fetched from external URLs or installed at runtime by the skill itself.
Credentials
The skill declares no required environment variables or credentials (none in requires.env). However, generated artifacts (Airflow DAGs, SnowflakeOperator/PostgresOperator usage, Kafka examples) will expect platform-specific connection identifiers and secrets to be present in the target environment (Airflow connections, cloud credentials) — this is normal but users should not assume this skill will auto-supply or manage those credentials.
Persistence & Privilege
always is false and model invocation is allowed (default). The skill does not request permanent system presence or attempt to modify other skills or system-wide agent settings.
Assessment
This bundle appears coherent for designing and generating data pipelines, but review generated artifacts before running them in production. Specific points to consider: 1) Generated DAGs and tasks will reference connection IDs (e.g., postgres_conn_id, snowflake_conn_id) — you must configure those connections securely in your Airflow/secret manager rather than embedding secrets in generated code. 2) The generator can embed arbitrary bash commands from task parameters; inspect any generated BashOperator commands for accidental injection of secrets or destructive commands. 3) The validators and patterns include detectors for PII (emails, credit-card-like patterns) — avoid feeding sensitive production data into the skill without appropriate controls. 4) Test generated code in an isolated or staging environment first, and supply credentials via your normal secret-store mechanism. If you want to reduce risk, disable autonomous invocation for this skill or review its outputs manually before execution.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.