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Agent skill

Data Modeling

by cbrock84 in cbrock84/headcount

Designs the warehouse and semantic layer — source-to-mart structure, dimensional modeling, grain, slowly changing dimensions, and the metric layer analytics reads through.

MITAuto-check passedDatabases

Install Data Modeling

skills CLI
$ npx skills add cbrock84/headcount --skill data-modeling -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install cbrock84/headcount data-modeling --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/cbrock84/headcount.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/data-analytics/skills/data-modeling .claude/skills/data-modeling && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
data-modeling
GitHub stars
2k
Token cost
~1k tokens
SKILL.md length
550 words
Files
2 (incl. references)
Skills in repo
175
Repo updated
First seen
Licence
MIT

At a glance

Designs the warehouse and semantic layer — source-to-mart structure, dimensional modeling, grain, slowly changing dimensions, and the metric layer analytics reads through.

  • Works in 3 steps: Raw — source data, append-only,… → Staging — cleaned and conformed:… → Marts — business-facing models shaped…
  • Tasks that involve Database schema design
  • SKILL.md covers Layers, and why the middle one…, Grain is the decision…, Dimensional structure and The semantic layer, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Data Modeling is an agent skill from cbrock84/headcount. Designs the warehouse and semantic layer — source-to-mart structure, dimensional modeling, grain, slowly changing dimensions, and the metric layer analytics reads through. Use this to design or restructure a warehouse, model a new source, decide on grain or table structure, build a semantic or metric layer, or diagnose why queries are slow, wrong, or impossible to write.

Its SKILL.md is about 1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/sources.md`).

It sits in Databases, covering Database schema design and Data warehousing. The repository describes itself as: An agent organization structured as a company — 15+ departments, 125+ skills, each independently installable, citing the standards and regulators that settle the question. Runs… The licence is MIT.

When your agent uses it

  • Tasks that involve Database schema design
  • Tasks that involve Data warehousing

Example prompts

  • “Use the data-modeling skill to design the warehouse and semantic layer — source-to-mart structure, dimensional modeling, grain, slowly changing…”
  • “/data-modeling”

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

  1. Raw — source data, append-only, otherwise unmodified. Do not apply business logic on
  2. Staging — cleaned and conformed: consistent types, standardized names, deduplicated, no
  3. Marts — business-facing models shaped for how questions are asked.

What it can do on your machine

Read from SKILL.md and the folder at commit 98d1c17. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Data Modeling loads about 1k tokens when it runs, and up to ~1.3k if it reads all its reference files. Until then it costs about 97 tokens; SKILL.md has 550 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~97
When it runs · the whole SKILL.md, loaded when a task matches
~1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~1.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from cbrock84/headcount at commit 98d1c17, republished under its MIT licence (© cbrock84). 550 words, ~1,002 tokens.

Download SKILL.mdSave it as .claude/skills/data-modeling/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
data-modeling
description
Designs the warehouse and semantic layer — source-to-mart structure, dimensional modeling, grain, slowly changing dimensions, and the metric layer analytics reads through. Use this to design or restructure a warehouse, model a new source, decide on grain or table structure, build a semantic or metric layer, or diagnose why queries are slow, wrong, or impossible to write.

Data modeling

Layers, and why the middle one matters

Three layers, each with one job:

  1. Raw — source data, append-only, otherwise unmodified. Do not apply business logic on ingest: you cannot recover what you discarded, and the logic will need to change retroactively.

    Privacy and security transformations are the exception, and belong at ingest. Credentials and secrets should never land in the warehouse at all. Personal data that is not needed should be dropped rather than stored and governed later, and identifiers you must keep but rarely need in the clear should be tokenized or encrypted on arrival. Retention and deletion apply from ingest, not from the marts.

    The distinction: strip what you must not hold, keep everything you are entitled to hold, and leave interpretation for later.

  2. Staging — cleaned and conformed: consistent types, standardized names, deduplicated, no business logic yet.

  3. Marts — business-facing models shaped for how questions are asked.

The discipline that pays is keeping business logic out of layers 1 and 2. Logic embedded in ingestion cannot be changed retroactively, and it will need to change.

Grain is the decision everything follows from

State the grain of every table in one sentence: one row per what. "One row per order line per day" is a grain. "Order data" is not.

Most modeling errors are grain errors, and they surface as fan-out — a join multiplying rows so every downstream sum is inflated. If a number is mysteriously too high, check the grain before checking the logic.

Dimensional structure

Facts for events and measurements; dimensions for the things being described. Keep facts narrow and long, dimensions wide and short.

Conform dimensions across facts — one customer dimension, used everywhere. Separate customer tables per domain is how the same customer gets counted differently in two reports.

Handle history deliberately. Overwriting a dimension attribute rewrites the past: last year's revenue silently re-attributes to this year's segment. Decide per attribute whether history matters, and where it does, keep versions with valid-from and valid-to.

Show full SKILL.md (219 more words)Show less

The semantic layer

Define metrics once, above the marts, and have every consumer read through it. Without it, the same metric is reimplemented in each dashboard and they drift — not because anyone is careless, but because a filter differs.

The semantic layer is where the metric dictionary becomes executable rather than documentary.

Performance

Model for the query pattern you actually have. Pre-aggregate what is queried constantly; leave the long tail to compute on demand.

Partition and cluster on what people filter by — usually time, then a tenant or entity key. Most slow warehouse queries are full scans of a table that could have been partitioned by date.

Denormalize deliberately, and write down why. Undocumented denormalization is indistinguishable from a modeling error six months later.

Sources

references/sources.md in this skill lists the outside authorities that settle the questions here — what each one is authoritative for, and what you may do with it. Check them before answering on anything they cover, and cite what you used. Most are free to read and not free to reproduce; the use note on each is binding.

Never

  • Build a mart directly on raw. The coupling means every source change breaks the business layer.
  • Mix grains in one table.
  • Let a dashboard contain business logic the warehouse does not. That logic is invisible and unversioned.

© cbrock84, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file (references) in plugins/data-analytics/skills/data-modeling of cbrock84/headcount.

  • SKILL.md
  • references/sources.md

Open the folder on GitHubat commit 98d1c17

Compare with similar skills

Data Modeling next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Data Modeling compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Data Modeling this skillcbrock84/headcount2k—~1kAutomated safety check: PassMIT
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SQL Prodavila7/claude-code-templates32k9 repos~1.9kAutomated safety check: PassMIT
Schema Design Advisorchmonitor/chmonitor299—~2.2kAutomated safety check: PassGPL-3.0
Schema Design InterviewerPrepLabsAI/InterviewMentor112—~8.2kAutomated safety check: PassMIT
Clickhouse Core Workflow Ajeremylongshore/tons-of-skills-marketplace2.8k—~1.6kAutomated safety check: PassMIT

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Categories

Questions about Data Modeling

What does Data Modeling do?

Designs the warehouse and semantic layer — source-to-mart structure, dimensional modeling, grain, slowly changing dimensions, and the metric layer analytics reads through. Data Modeling is an agent skill from cbrock84/headcount. Designs the warehouse and semantic layer — source-to-mart structure, dimensional modeling, grain, slowly changing dimensions, and the metric layer analytics reads through.

When should I use Data Modeling?

Data Modeling fits situations like: tasks that involve Database schema design; tasks that involve Data warehousing.

How do I install Data Modeling in Claude Code?

Run `npx skills add cbrock84/headcount --skill data-modeling -a claude-code`. Or copy the skill folder (plugins/data-analytics/skills/data-modeling in cbrock84/headcount) into .claude/skills/data-modeling in your project. Claude Code loads it when a task matches its description.

How do I install Data Modeling in Codex?

Run `npx skills add cbrock84/headcount --skill data-modeling -a codex`. Or copy the skill folder (plugins/data-analytics/skills/data-modeling in cbrock84/headcount) into .agents/skills/data-modeling in your project. Codex loads it when a task matches its description.

Can I use Data Modeling in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add cbrock84/headcount --skill data-modeling -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/data-modeling, .gemini/skills/data-modeling, .github/skills/data-modeling and .opencode/skills/data-modeling in your project.

What does Data Modeling need to run?

SKILL.md names no scripts, command-line tools or credentials: Data Modeling is instructions for the agent only.

Does Data Modeling access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Data Modeling safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Data Modeling use?

Data Modeling is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Data Modeling use?

About 1k tokens (SKILL.md is roughly 4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 313 tokens, read only when the agent opens those files.

What are the alternatives to Data Modeling?

Skills that share tags, products or a category with Data Modeling: Ddia Principles (luoling8192/ai-coding-principles, 173 stars), SQL Pro (davila7/claude-code-templates, 32k stars), Schema Design Advisor (chmonitor/chmonitor, 299 stars) and Schema Design Interviewer (PrepLabsAI/InterviewMentor, 112 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Data Modeling?

cbrock84 (a GitHub user) maintains it in cbrock84/headcount, which has 2,007 GitHub stars. The repository holds 175 skills in this directory. The repository was last updated on September 17, 2026.

Source: cbrock84/headcount on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.