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-databasesgit 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-databases)<a href="https://agentmods.dev/skills/reganomalley/claudia/claudia-databases"><img src="https://agentmods.dev/badge/skills/reganomalley/claudia/claudia-databases/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-databases"><img src="https://agentmods.dev/badge/skills/reganomalley/claudia/claudia-databases.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 | $0.00094 | $0.00816 |
| Opus 5 | $0.00047 | $0.00408 |
| Sonnet 5 | $0.00019 | $0.00163 |
| Haiku 4.5 | $0.00009 | $0.00082 |
Grade A, and why
claudia-databases 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 — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Claudia Database Domain
Overview
This skill helps you choose the right database and use it well. The right answer depends on your data shape, access patterns, scale, and team -- not on what's trending on Hacker News.
The Decision Matrix
Start Here: What Shape Is Your Data?
What does your data look like?
├── Rows and columns, relationships between entities
│ └── Relational (PostgreSQL, MySQL, SQLite)
├── Nested documents, variable schema
│ └── Document (MongoDB, CouchDB)
├── Simple key → value lookups, caching
│ └── Key-Value (Redis, DynamoDB, Memcached)
├── Connections between entities matter most
│ └── Graph (Neo4j, Amazon Neptune)
├── Metrics over time, IoT, monitoring
│ └── Time-Series (TimescaleDB, InfluxDB, QuestDB)
├── AI embeddings, similarity search
│ └── Vector (pgvector, Pinecone, Qdrant, Weaviate)
├── Full-text search, faceted filtering
│ └── Search (Elasticsearch, Meilisearch, Typesense)
└── Not sure / mixed
└── Start with PostgreSQL (it does most things well enough)
The "Just Use Postgres" Rule
PostgreSQL handles 80% of use cases well. Before reaching for a specialized database, ask:
- Can Postgres do this with an extension? (pgvector, TimescaleDB, PostGIS, pg_trgm)
- Is the specialized need actually my bottleneck, or am I optimizing prematurely?
- Can my team operate another database in production?
Use a specialized DB when: Postgres can technically do it, but you're hitting real performance limits at your actual scale, or the specialized DB's API dramatically simplifies your code.
Quick Comparisons
| Need | First Choice | When to Upgrade |
|---|---|---|
| General web app | PostgreSQL | You probably don't need to |
| Caching / sessions | Redis | When you need persistence → Redis with AOF |
| Document store | MongoDB | When you need transactions → Postgres JSONB |
| Embeddings / RAG | pgvector | >10M vectors or <10ms latency → Pinecone/Qdrant |
| Time-series | TimescaleDB (Postgres ext) | >1M inserts/sec → QuestDB or InfluxDB |
| Graph queries | PostgreSQL recursive CTEs | >3 hops or complex traversals → Neo4j |
| Full-text search | PostgreSQL ts_vector | Complex facets or relevance tuning → Elasticsearch |
| Prototype / local | SQLite | Going to production with concurrent writes |
What ships with it
3 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 · 76 lines · 94 tokens per session scan A 0ff62dbe27fb
claudia-databases is a skill published in the GitHub repository reganomalley/claudia (4 stars, last pushed 6mo ago), licensed MIT. It adds 94 tokens to every session and 816 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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