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.
git clone --depth 1 https://github.com/christopherlouet/claude-baseWrote 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/commands/christopherlouet/claude-base/data-modeling)<a href="https://agentmods.dev/commands/christopherlouet/claude-base/data-modeling"><img src="https://agentmods.dev/badge/commands/christopherlouet/claude-base/data-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/commands/christopherlouet/claude-base/data-modeling"><img src="https://agentmods.dev/badge/commands/christopherlouet/claude-base/data-modeling.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00000 | $0.00436 |
| Opus 5.5 | $0.00000 | $0.00174 |
| Sonnet 5.5 | $0.00000 | $0.00087 |
| Haiku 4.5 | $0.00000 | $0.00044 |
Grade A, and why
data-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 13d 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.
What it actually says
Agent DATA-MODELING
Design and implement data models (schemas, ERD, data warehouse).
Request context
$ARGUMENTS
Objective
Design a data model suited to analytical or transactional use cases, with naming conventions, documentation and quality tests.
Workflow
- Understand the needs: analytical use cases, business entities, questions to answer, volume
- Choose the type of modeling (OLTP 3NF, Star Schema, Snowflake, Data Vault, Wide Table)
- Define the entities with relations and cardinality
- Apply naming conventions (fact_, dim_, _id, at, is, _amount)
- Implement with dbt if applicable (models, tests, YAML documentation)
- Manage Slowly Changing Dimensions (SCD Type 2) if needed
- Document with ERD (dbdiagram.io or draw.io) and data dictionary
- Add quality tests (unique, not_null, accepted_values)
Expected output
Data model with entities (type, description, estimated volume), ERD, data dictionary and example queries.
Related agents
| Agent | When to use it |
|---|---|
/data:data-pipeline |
Feed the model |
/growth:growth-analytics |
Analyze KPIs on the modeled data |
/ops:ops-database |
Optimize performance |
IMPORTANT: The model must answer business questions, not the other way around.
YOU MUST document every table and column.
NEVER create a model without understanding the use cases.
Think hard about the model's scalability and maintainability.
See also
For the dbt implementation of a model, dbt Labs' own skills — dbt-labs/dbt-agent-skills skills/dbt/skills/* (Apache-2.0, pin 168a2b0b) — cover models, tests, documentation and mesh versioning. The modelling itself (3NF, star schema, Data Vault, SCD, ERD, data dictionary) stays here: no vendor covers it tool-agnostically.
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.
- 13d ago Changed · +4 lines 126aae842772
- 1mo ago First seen · 46 lines · 0 tokens per session scan A 166dd40a9078
data-modeling is a command published in the GitHub repository christopherlouet/claude-base (5 stars, last pushed 4d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 436 tokens. 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 2026-08-31.
Other commands, from other repositories
migrate
Generate and manage database migrations.
context-stats
Display context window usage and token statistics.
update-counts
Explicitly refresh public toolkit and committed marketplace source snapshots and numeric documentation.
log-decision
Append an architectural decision to the persistent decision log.
cancel-ralph
Cancel active Ralph Wiggum loop.
update-docs
Scan recently modified files and update the matching documentation.