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 j4flmao/agent-skills --skill dimensional-modelinggit clone --depth 1 https://github.com/j4flmao/agent-skillsWrote 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/j4flmao/agent-skills/dimensional-modeling)<a href="https://agentmods.dev/skills/j4flmao/agent-skills/dimensional-modeling"><img src="https://agentmods.dev/badge/skills/j4flmao/agent-skills/dimensional-modeling.svg" alt="Measured on agentmods" 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 | $0.05433 |
| Opus 5 | $0.00061 | $0.02717 |
| Sonnet 5 | $0.00024 | $0.01087 |
| Haiku 4.5 | $0.00012 | $0.00543 |
Grade A, and why
data-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 4d 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 — 532 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dimensional Modeling
Purpose
Design robust dimensional data models for analytical workloads following Kimball methodology, star schema best practices, slowly changing dimension strategies, fact table design patterns, and Data Vault 2.0 architecture.
Agent Protocol
Trigger
Exact user phrases: "dimensional modeling", "Kimball", "star schema", "snowflake schema", "bus matrix", "conformed dimension", "slowly changing dimension", "SCD", "fact table", "dimension table", "data vault", "hub link satellite", "grain declaration", "surrogate key", "degenerate dimension".
Input Context
- Business processes to model (sales, inventory, orders, payments)
- Source systems and data granularity
- Reporting and analytics requirements
- Query patterns (aggregations, drill-down, slice-and-dice)
- Data volume and growth rate
- Historical tracking requirements (how far back, what changes to track)
- BI tool requirements (Tableau, Power BI, Looker)
Output Artifact
Dimensional model with bus matrix, star schemas, SCD strategy, fact table designs, and DDL statements.
Response Format
-- Dimension and fact table DDL
-- SCD implementation
# Bus matrix
# Grain declaration
# Design decisions and trade-offs
No preamble. No postamble. No explanations. No filler/hedging/transitions.
Completion Criteria
- Business process selected and grain declared
- Bus matrix created showing dimensions by process
- Conformed dimensions identified and standardized
- SCD type selected per dimension attribute
- Fact table type (transaction, periodic snapshot, accumulating) selected
- Additive, semi-additive, and non-additive measures classified
- Surrogate key strategy defined
- Data Vault model designed if applicable
Max Response Length
300 lines of code and configuration.
Workflow
Step 1: Select Business Process
Identify core business processes that generate measurable events: sales transactions, order fulfillment, inventory movements, customer interactions, financial postings, service requests. Each process becomes a fact table candidate. Prioritize by business impact, data availability, and reporting requirements. Start with 3-5 core processes, expand in later phases.
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 4d ago First seen · 532 lines · 122 tokens per session scan A 5e8d9b6b3ba0
data-dimensional-modeling is a skill published in the GitHub repository j4flmao/agent-skills (22 stars, last pushed yesterday), licensed MIT. It adds 122 tokens to every session and 5,433 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
data-engineer
Builds data infrastructure — ETL/ELT pipelines, data warehousing, stream processing, data quality, orchestration (Airflow/Dagster), and analytics engineering (dbt). Use when the user asks to build data pipelines, set up ETL/ELT workflows, design a data warehouse, configure stream processing, or implement analytics…
build-with-tinybase
Scaffold, extend, and verify reactive local-first JavaScript or TypeScript applications with TinyBase. Use when choosing TinyBase for in-memory tabular or key-value state, generating an app with create-tinybase, adding schemas or UI bindings, configuring browser or database persistence, configuring MergeableStore…
database
Query and manage SQLite, PostgreSQL, and MySQL databases from the command line. Use when the user asks to run SQL queries, inspect database schemas, create or alter tables, import or export data, manage indexes, analyze query performance with EXPLAIN, back up or restore databases, or perform CRUD operations via…
qdrant
Manage Qdrant vector database via REST API. Use when the user asks to create or delete collections, upsert or search vectors, inspect points, filter by payload fields, manage snapshots, check cluster status, or debug semantic search issues. Covers collection CRUD, point upsert/search/scroll/count, payload indexes…
analytical-databases-expert
Design and query columnar analytical stores: DuckDB, ClickHouse and cloud warehouses, including file formats, partitioning, sort keys and cost control. Use when the user mentions DuckDB, ClickHouse, Parquet, columnar storage, OLAP, a data warehouse or lakehouse, analytical queries over large tables, or when the task…
mysql-expert
MySQL and MariaDB administration and development: InnoDB internals, indexing, query tuning, replication, and online schema change. Use when the user mentions MySQL, MariaDB, InnoDB, my.cnf, slow queries, EXPLAIN, binlog or replication lag, gtid, Percona or pt-online-schema-change, or when the task involves designing a…