dimensional-modeling

dimensional-modeling is a skill for Claude Code, Codex from khalilbenaz/claude-skills-collection. It costs 122 tokens per session (2,549 once invoked), scanned A, original, MIT.

A guide to dimensional modelling, a way to structure a data warehouse for reporting. It explains fact tables for measurable events, dimension tables for descriptive context, grain, star schemas, and changing historical values.

In plain words
What is it for?
Use it to model processes such as sales or orders, define what one row represents, choose facts and dimensions, and design warehouse reporting structures.
Why use it?
It helps keep analytical data consistent and makes reports easier to query without mixing different levels of detail.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to model processes such as sales or orders, define what one row represents, choose facts and dimensions, and design warehouse reporting structures.

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Install with agentmods
npx agentmods add skills/khalilbenaz/claude-skills-collection/dimensional-modeling
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add khalilbenaz/claude-skills-collection --skill dimensional-modeling
Clone the repo
git clone --depth 1 https://github.com/khalilbenaz/claude-skills-collection

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for dimensional-modeling

README.md
[![agentmods](https://agentmods.dev/badge/skills/khalilbenaz/claude-skills-collection/dimensional-modeling/github.svg)](https://agentmods.dev/skills/khalilbenaz/claude-skills-collection/dimensional-modeling)
Your own site
<a href="https://agentmods.dev/skills/khalilbenaz/claude-skills-collection/dimensional-modeling"><img src="https://agentmods.dev/badge/skills/khalilbenaz/claude-skills-collection/dimensional-modeling/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for dimensional-modeling

Your own site · 80×15
<a href="https://agentmods.dev/skills/khalilbenaz/claude-skills-collection/dimensional-modeling"><img src="https://agentmods.dev/badge/skills/khalilbenaz/claude-skills-collection/dimensional-modeling.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 122 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,549 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00122 $0.02549
Opus 5 $0.00061 $0.01274
Sonnet 5 $0.00024 $0.00510
Haiku 4.5 $0.00012 $0.00255

Measured 9d ago against content hash 41ac909b3e8a, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

dimensional-modeling scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

data-skills/dimensional-modeling/SKILL.md · 233 lines

How it starts

The opening of the file, as written. The whole thing — 233 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Modélisation Dimensionnelle

Workflow — 4 décisions dans l'ordre

1. Identifier le processus métier

Choisir UN processus à la fois (ventes, commandes, facturation, trafic web). Ne pas mélanger deux processus dans une même table de faits à ce stade.

2. Définir le grain

La décision la plus critique. "1 ligne = ?" doit s'énoncer en une phrase.

Grain Exemple
Grain fin (transactionnel) 1 ligne par ligne de commande
Grain moyen 1 ligne par commande
Grain agrégé 1 ligne par client par mois

Règle : toujours choisir le grain le plus fin techniquement supportable. Les agrégats peuvent toujours être calculés à la requête ; l'inverse est impossible.

3. Identifier les dimensions

Questions guides : Qui ? Quoi ? Où ? Quand ? Comment ? Chaque dimension répond à l'une de ces questions pour décrire le fait.

4. Identifier les mesures (faits)

Ne retenir que les mesures numériques cohérentes avec le grain défini. Classer chaque mesure : additive / semi-additive / non-additive.

Additivité Définition Exemple
Additive Somme valide sur toutes dimensions Quantité vendue, chiffre d'affaires
Semi-additive Somme valide sur certaines dimensions seulement Solde de compte (pas sur le temps)
Non-additive Pas de somme utile Taux, ratios, prix unitaire

Schéma en étoile — Structure SQL

-- Table de faits
CREATE TABLE fact_sales (
    sale_key        BIGINT IDENTITY PRIMARY KEY,
    date_key        INT NOT NULL REFERENCES dim_date(date_key),
    product_key     INT NOT NULL REFERENCES dim_product(product_key),
    customer_key    INT NOT NULL REFERENCES dim_customer(customer_key),
    store_key       INT NOT NULL REFERENCES dim_store(store_key),

    -- Mesures additives
    quantity        INT            NOT NULL,
    unit_price      DECIMAL(10,2)  NOT NULL,
    discount_amount DECIMAL(10,2)  NOT NULL DEFAULT 0,
    net_amount      DECIMAL(10,2)  NOT NULL,
    tax_amount      DECIMAL(10,2)  NOT NULL,
    total_amount    DECIMAL(10,2)  NOT NULL,

    -- Clés dégénérées (identifiants source sans dimension propre)
    invoice_number  VARCHAR(50),
    line_number     INT
);

-- Dimension Date (pré-remplie, jamais via ETL en temps réel)
CREATE TABLE dim_date (
    date_key      INT         PRIMARY KEY,  -- Format YYYYMMDD
    full_date     DATE        NOT NULL,
    day_of_week   INT         NOT NULL,     -- 1=Lundi ... 7=Dimanche
    day_name      VARCHAR(10) NOT NULL,
    day_of_month  INT         NOT NULL,
    week_of_year  INT         NOT NULL,
    month_number  INT         NOT NULL,
    month_name    VARCHAR(10) NOT NULL,
    quarter       INT         NOT NULL,
    year          INT         NOT NULL,
    is_weekend    BIT         NOT NULL,
    is_holiday    BIT         NOT NULL,
    fiscal_year   INT,
    fiscal_quarter INT
);

Read the full file on GitHub · 233 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 9d ago First seen · 233 lines · 122 tokens per session scan A 41ac909b3e8a

Subscribe to this mod's changes

dimensional-modeling is a skill published in the GitHub repository khalilbenaz/claude-skills-collection (22 stars, last pushed 19d ago), licensed MIT. It adds 122 tokens to every session and 2,549 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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