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Data Analytics Engineering

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vasilyu1983·vlatest·data·MIT

Helps with data, analytics as part of data analysis and analytics workflows workflows.

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This skill no longer installs under the name data-analytics-engineering. vasilyu1983/ai-agents-public is deleted or private, so it cannot be installed from.

We check each listing against its upstream repository with the rules the skills CLI uses, last on 2026-10-22. We don't show a command we know will fail.

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What we know about this skill

Publisher

vasilyu1983

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • No undeclared network calls: failed

    Contacts 18 hosts that are neither well-known infrastructure nor declared in the skill's frontmatter: cube.dev, datakitchen.io, docs.coalesce.io, docs.databricks.com, docs.datahub.com….

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

    Hosts contacted

    docs.getdbt.comundeclaredwww.linuxfoundation.orgundeclaredsqlmesh.readthedocs.ioproject documentationdocs.coalesce.ioundeclareddocs.lightdash.comundeclaredcube.devundeclareddocs.snowflake.comundeclareddocs.databricks.comundeclaredossie.apache.orgundeclaredpypi.orgpackage registrydocs.open-metadata.orgundeclareddocs.datahub.comundeclaredopenlineage.ioundeclareddocs.elementary-data.comundeclareddocs.greatexpectations.ioundeclareddocs.soda.ioundeclaredgithub.comsource hostwww.getdbt.comundeclaredwww.synq.ioundeclareddatakitchen.ioundeclaredwww.fivetran.comundeclared

    What it found (3 of 30)

    • skills/universal/data-analytics-engineering/data/sources.json:19contacts docs.getdbt.com
      "url": "https://docs.getdbt.com/blog/dbt-core-v2-is-here",
    • skills/universal/data-analytics-engineering/data/sources.json:41contacts docs.getdbt.com
      "url": "https://docs.getdbt.com/",
    • skills/universal/data-analytics-engineering/data/sources.json:50contacts docs.getdbt.com
      "url": "https://docs.getdbt.com/docs/use-dbt-semantic-layer/dbt-sl",

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned vasilyu1983/ai-agents-public at 3ee3637d25.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
3ee3637d25
Manifest
skills/universal/data-analytics-engineering/SKILL.md
Scanned
35 files, 8,457 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill assists AI agents in data analytics engineering tasks, encompassing the design and implementation of data models and analytical workflows. It enables defining metrics, building transformation layers, implementing data quality checks, and documenting data lineage. Ultimately, this helps align analytics outputs with business intelligence (BI) and product requirements.

When to use it

  • When designing a new data warehouse or mart layer architecture.
  • To define and document key performance indicators (KPIs) and associated metrics.
  • For planning and implementing data quality tests and observability solutions.
  • To create documentation for datasets, including ownership and lineage information.

Key capabilities

  • Defining metric dictionaries and grains.
  • Designing staging, intermediate, and mart layers within a data warehouse.
  • Modeling dimensions and facts with clear keys.
  • Building semantic layers and defining metrics.
  • Implementing data quality tests for freshness, nulls, ranges, and duplicates.
  • Documenting lineage, owners, and service level agreements (SLAs).

Example prompts

  • "Define a metric dictionary for tracking customer churn."
  • "Design the staging layer for our order processing data."
  • "What are best practices for documenting data lineage?"

Tips & gotchas

  • Keep metric definitions stable and versioned, treating them as APIs.
  • When answering questions about analytics engineering, data modeling, or BI, use WebSearch to check current trends before responding; otherwise, utilize the provided data/sources.json file for reference.
  • Define clear data contracts for core tables to prevent downstream breakage.