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 nimadorostkar/Claude-Skills-collection --skill data-modelinggit clone --depth 1 https://github.com/nimadorostkar/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/nimadorostkar/claude-skills-collection/data-modeling)<a href="https://agentmods.dev/skills/nimadorostkar/claude-skills-collection/data-modeling"><img src="https://agentmods.dev/badge/skills/nimadorostkar/claude-skills-collection/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/skills/nimadorostkar/claude-skills-collection/data-modeling"><img src="https://agentmods.dev/badge/skills/nimadorostkar/claude-skills-collection/data-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 | $0.00097 | $0.01206 |
| Opus 5.5 | $0.00039 | $0.00482 |
| Sonnet 5.5 | $0.00019 | $0.00241 |
| Haiku 4.5 | $0.00010 | $0.00121 |
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 19d 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 — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Modeling
Purpose
Design a schema that supports the queries you need and the changes you will want. Schema mistakes are the most expensive category of technical debt because the data outlives every application that touched it.
When to Use
- Designing a new schema or a significant new table.
- Adding multi-tenancy to an existing model.
- Modeling history, versioning, or audit requirements.
- Reviewing a schema before it becomes hard to change.
Capabilities
- Normalization, and deliberate denormalization.
- Key selection: natural, surrogate, UUID, ULID.
- Temporal modeling: valid time, transaction time, event history.
- Multi-tenancy strategies and their isolation guarantees.
- Constraint design: the invariants the database should enforce.
Inputs
- The domain entities and the relationships between them.
- The queries the schema must serve, and their frequency.
- Retention, audit, and compliance requirements.
Outputs
- A schema with enforced constraints and appropriate keys.
- A documented tenancy and soft-delete strategy.
- A migration path from the current schema, if there is one.
Workflow
- Normalize first — Third normal form as the default. Denormalize only where a measured query cost justifies it, and record the decision.
- Choose keys deliberately — A surrogate key (UUID/ULID) is stable and safe to expose. A natural key is meaningful and changes when the business changes its mind. Prefer surrogates for identity, and enforce natural uniqueness with a constraint.
- Enforce invariants in the database — Foreign keys, unique constraints, check constraints, not-null. Application-level validation does not survive a concurrent request, a background job, or a psql session.
- Model time explicitly — If history matters, do not overwrite. Append versions or events.
updated_attells you when, not what it was before. - Decide tenancy up front — Shared table with a tenant column, schema per tenant, or database per tenant. Migrating between these later is a project, not a task.
- Plan the migration — Every schema change on a large table needs a plan that does not lock it.
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.
- 19d ago Changed · +55 tokens per session d3409b5b7937
- 2mo ago First seen · 105 lines · 42 tokens per session scan A 3c2fa86e5edd
data-modeling is a skill published in the GitHub repository nimadorostkar/Claude-Skills-collection (33 stars, last pushed 5d ago), licensed MIT. It adds 97 tokens to every session and 1,206 once invoked, about $0.0004 per session on Opus 5.5. 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 2026-08-30.
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