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.
npx skills add khalilbenaz/claude-skills-collection --skill dimensional-modelinggit clone --depth 1 https://github.com/khalilbenaz/claude-skills-collectionWrote 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.
[](https://agentmods.dev/skills/khalilbenaz/claude-skills-collection/dimensional-modeling)<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.
<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>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once 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 |
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.
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
);
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.
- 9d ago First seen · 233 lines · 122 tokens per session scan A 41ac909b3e8a
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.
Other skills, from other repositories
database-patterns
DB schema design and query tuning: normalization, indexing, N+1, transactions, EXPLAIN. Triggers: schema, index, slow query, N+1, PostgreSQL, MySQL, EXPLAIN, deadlock, query plan.
migration-patterns
Zero-downtime DB migrations: expand-contract, double-write, backfill, blue-green. Triggers: migration, schema change, backfill, ALTER TABLE, online DDL.
migrate
Run/create DB migrations (Alembic, Prisma, Laravel, Django, Flyway, Drizzle); checks backup. Triggers: apply migration, rollback, generate migration.
mongo-migration
MongoDB schema migration safety reviewer and migration script generator. ALWAYS use when writing, reviewing, or planning MongoDB schema changes — field additions/removals, index builds, schema validator changes, document type migrations, shard key modifications, or any bulk update touching production collections.…
mysql-migration
MySQL schema migration safety reviewer and DDL generator. ALWAYS use when writing, reviewing, or planning MySQL schema changes — ALTER TABLE, CREATE/DROP INDEX, column type changes, charset conversions, data backfills, or any DDL touching production tables. Covers online DDL algorithm selection (INSTANT/INPLACE/COPY)…
oracle-migration
Oracle Database schema migration safety reviewer and DDL generator. ALWAYS use when writing, reviewing, or planning Oracle schema changes — ALTER TABLE, CREATE/DROP INDEX, column type changes, constraint additions, partition DDL, or any DDL touching production tables. Covers DDL auto-commit implications…