Data Model

Deep data modeling workflow—grain, facts and dimensions, keys, slowly changing dimensions, normalization trade-offs, and analytics query patterns. Use when d...

Security Scan

This skill is an instruction-only data-modeling workflow that is internally consistent with its description and does not request credentials, install software, or perform I/O—low-risk in its current form.

Purpose & Capability

The SKILL.md content matches the skill name/description: it provides a six-stage data modeling workflow (grain, facts/dimensions, SCDs, keys, performance). There are no unrelated requirements (no unexpected env vars, binaries, or config paths).

Instruction Scope

Instructions are limited to modeling guidance and a checklist. They do not instruct the agent to read files, access environment variables, call external endpoints, or aggregate/transmit data outside the workflow. The only actionable prompt is to 'confirm tooling' (dbt, BigQuery, etc.), which is descriptive rather than an instruction to obtain credentials.

Install Mechanism

No install specification or code files are present (instruction-only). Nothing will be downloaded or written to disk as part of installing this skill.

Credentials

The skill declares no required environment variables, credentials, or config paths. There is nothing disproportionate or unexplained requested for the stated purpose.

Persistence & Privilege

The skill is not marked 'always' and uses platform defaults for invocation. It does not request persistent system changes or access to other skills' configurations.

Assessment

This skill appears coherent and low-risk because it is instruction-only and asks for no credentials or installs. Things to consider before using: (1) the skill source/homepage is unknown—if you need stronger provenance, confirm the publisher or prefer a vetted alternative; (2) when following its guidance, avoid sending real production data or credentials to the agent (provide small, sanitized schema examples instead); (3) if you later let the agent act on your cloud/data warehouse (e.g., run queries in BigQuery or modify dbt projects), that will require granting credentials—only do that through trusted, least-privilege mechanisms. Overall, the skill itself does not contain surprising or disproportionate requests.

Analytics models succeed when grain is explicit, keys are stable, and slowly changing dimensions are chosen deliberately—not “star schema by default.”

When to Offer This Workflow

Trigger conditions:

• Designing a warehouse, lakehouse, or BI layer
• Confusion on one row per what; duplicate counts in reports
• Refactoring dimensional models for performance or clarity

Initial offer:

Use six stages: (1) business questions & grain, (2) conformed dimensions, (3) facts & measures, (4) dimensions & SCD types, (5) keys & integrity, (6) performance & evolution). Confirm tooling (dbt, dimensional DW, BigQuery, etc.).

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Stage 1: Business Questions & Grain

Goal: Grain = the atomic row: e.g., “one line item per order per day” not “sort of per order.”

Practices

• List questions the model must answer; derive grain from smallest needed detail

Exit condition: One sentence grain per fact table.

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Stage 2: Conformed Dimensions

Goal: Same customer/product definitions across facts—shared dimension tables or SCD policy aligned.

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Stage 3: Facts & Measures

Goal: Additive vs semi-additive vs non-additive measures documented (balances, distinct counts).

Practices

• Degenerate dimensions vs junk dimensions—avoid wide fact sprawl without reason

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Stage 4: Dimensions & SCD Types

Goal: SCD1 overwrite vs SCD2 history with valid_from/valid_to vs SCD3 limited history—match compliance and reporting needs.

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Stage 5: Keys & Integrity

Goal: Surrogate keys in facts; natural keys preserved as attributes; referential integrity strategy in the warehouse layer.

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Stage 6: Performance & Evolution

Goal: Partition and cluster keys for large facts; late-arriving facts policy; version dims when schema evolves.

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Final Review Checklist

• Grain explicit per fact table
• Conformed dimensions planned
• Measure additivity documented
• SCD strategy per critical dimension
• Keys and late-arriving data handled

Tips for Effective Guidance

• Fan traps and chasm traps in BI—flag when joining across facts incorrectly.
• Snapshot fact tables for point-in-time balances vs transaction facts.

Handling Deviations

• Event-only pipelines: still model curated dimensions for analysis, not only raw JSON.