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 reganomalley/claudia --skill claudia-datagit clone --depth 1 https://github.com/reganomalley/claudiaWrote 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/reganomalley/claudia/claudia-data)<a href="https://agentmods.dev/skills/reganomalley/claudia/claudia-data"><img src="https://agentmods.dev/badge/skills/reganomalley/claudia/claudia-data/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/reganomalley/claudia/claudia-data"><img src="https://agentmods.dev/badge/skills/reganomalley/claudia/claudia-data.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 | $0.00096 | $0.00961 |
| Opus 5 | $0.00048 | $0.00481 |
| Sonnet 5 | $0.00019 | $0.00192 |
| Haiku 4.5 | $0.00010 | $0.00096 |
Grade A, and why
claudia-data 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 10d 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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Claudia Data Modeling Domain
Overview
Good data modeling prevents most "scaling" problems before they start. Get the schema right and everything downstream gets easier -- queries are simpler, migrations are safer, performance is predictable, and your future self won't want to rewrite the whole thing.
Schema Design Decision Tree
How should I model my data?
├── Start normalized (3NF)
│ ├── Relationships are clear → foreign keys + join tables
│ ├── Schema is stable → strict types + constraints
│ └── Data integrity matters → NOT NULL, CHECK, UNIQUE
├── Denormalize only for measured read performance
│ ├── Dashboard query is slow → materialized view or computed column
│ ├── N+1 joins killing latency → embed or cache the joined data
│ └── Read:write ratio > 100:1 → read replica or denormalized table
└── Consider event sourcing only for audit-heavy domains
├── Need full history of every change → event log + projections
├── Regulatory/compliance requirements → append-only audit trail
└── Complex business rules with undo → event replay
Common Patterns
| Pattern | When to Use | Example |
|---|---|---|
| Soft deletes | Need to recover data, regulatory retention | deleted_at timestamp column, filter in queries |
| Audit trails | Compliance, debugging, blame tracking | Separate _audit table with old/new values + actor |
| Multi-tenancy | SaaS with isolated customer data | tenant_id column on every table + RLS policies |
| Polymorphic associations | Different types share a relationship | STI, CTI, or delegated types (see references) |
| EAV (avoid) | Almost never -- use JSONB instead | entity_id, attribute, value tables are a trap |
| JSON columns | Semi-structured data within a relational model | Preferences, metadata, form responses |
| Temporal data | Track how values change over time | valid_from/valid_to columns (SCD Type 2) |
| Junction tables | Many-to-many relationships | post_tags table linking posts and tags |
What ships with it
2 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.
- 10d ago First seen · 84 lines · 96 tokens per session scan A 1589827116a8
claudia-data is a skill published in the GitHub repository reganomalley/claudia (4 stars, last pushed 6mo ago), licensed MIT. It adds 96 tokens to every session and 961 once invoked, about $0.0005 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 2026-08-31.
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