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Data Model

作者 clawkk · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install data-model
功能描述
Deep data modeling workflow—grain, facts and dimensions, keys, slowly changing dimensions, normalization trade-offs, and analytics query patterns. Use when d...
使用说明 (SKILL.md)

Data Model

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.).

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.

Stage 2: Conformed Dimensions

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

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

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.

Stage 5: Keys & Integrity

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

Stage 6: Performance & Evolution

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

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.
安全使用建议
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.
功能分析
Type: OpenClaw Skill Name: data-model Version: 1.0.0 The skill bundle is a purely informational workflow for data modeling and analytics design. It contains no executable code, network requests, or suspicious instructions, focusing entirely on architectural guidance for star schemas and data warehousing in SKILL.md.
能力评估
✓ 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.
如何使用
  1. 确保已安装 OpenClaw（本地或 Docker 部署）
  2. 在对话框中输入安装命令：/install data-model
  3. 安装完成后，直接呼叫该 Skill 的名称或使用 /data-model 触发
  4. 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
v1.0.0
- Initial release of the "data-model" skill for analytics and warehouse design. - Introduces a six-stage workflow covering grain, conformed dimensions, facts & measures, SCD strategies, key management, and performance considerations. - Provides checklists and best practices for schema design, additive measures, dimension conformance, and SCD policy selection. - Offers guidance for handling common pitfalls (fan/chasm traps, late-arriving facts) and adapting to event-based pipelines. - Designed to support both star and snowflake schema reviews and implementations.
元数据
Slug data-model
版本 1.0.0
许可证 MIT-0
累计安装 1
当前安装数 1
历史版本数 1
常见问题

Data Model 是什么？

Deep data modeling workflow—grain, facts and dimensions, keys, slowly changing dimensions, normalization trade-offs, and analytics query patterns. Use when d... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 236 次。

如何安装 Data Model？

在 OpenClaw 或 Claude Code 对话框中运行命令「/install data-model」即可一键安装，无需额外配置。

Data Model 是免费的吗？

是的，Data Model 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。

Data Model 支持哪些平台？

Data Model 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 Data Model？

由 clawkk（@clawkk）开发并维护，当前版本 v1.0.0。

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