nw-database-technology-selection

nw-database-technology-selection is a skill for Claude Code, Codex from nWave-ai/nWave. It costs 38 tokens per session (1,210 once invoked), scanned A, original, MIT.

A framework for choosing a database by comparing how data is queried, how consistent it must be, how large it may become, and how complex its relationships and reporting needs are. It covers relational and non-relational databases, transaction models, and workload types.

In plain words
What is it for?
Use it to compare database technologies, choose between SQL and NoSQL, distinguish transactional workloads from analytics, and evaluate options such as PostgreSQL or caching systems.
Why use it?
It replaces database choice by habit with a comparison based on the application's actual needs. The guide also highlights trade-offs involving scale, latency, compliance, query features, and operational limits.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/nwave-ai/nwave/nw-database-technology-selection
Any agent
npx skills add nWave-ai/nWave --skill nw-database-technology-selection
Clone the repo
git clone --depth 1 https://github.com/nWave-ai/nWave

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for nw-database-technology-selection

README.md
[![agentmods](https://agentmods.dev/badge/skills/nwave-ai/nwave/nw-database-technology-selection.svg)](https://agentmods.dev/skills/nwave-ai/nwave/nw-database-technology-selection)
Your own site
<a href="https://agentmods.dev/skills/nwave-ai/nwave/nw-database-technology-selection"><img src="https://agentmods.dev/badge/skills/nwave-ai/nwave/nw-database-technology-selection.svg" alt="Measured on agentmods" height="20"></a>
Per session 38 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,210 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00038 $0.01210
Opus 5 $0.00019 $0.00605
Sonnet 5 $0.00008 $0.00242
Haiku 4.5 $0.00004 $0.00121

Measured yesterday against content hash 9c05a1dc0f39, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

nw-database-technology-selection 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 yesterday.

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.

nWave/skills/nw-database-technology-selection/SKILL.md · 75 lines

How it starts

The opening of the file, as written. The whole thing — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Database Technology Selection

Selection Decision Framework

Start with these questions:

  1. Primary access patterns? (point lookups, range queries, graph traversals, full-text search)
  2. Consistency guarantees? (strong ACID vs eventual consistency)
  3. Expected scale? (data volume, concurrent users, read/write ratio)
  4. Query complexity? (key-value, complex joins, aggregations, graph traversals)
  5. Latency targets? (sub-ms caching, ms OLTP, second-range analytics)
  6. Compliance requirements? (GDPR, CCPA, HIPAA, data residency)

RDBMS Selection Guide

PostgreSQL

Strengths: Full ACID, advanced cost-based optimizer, rich indexes (B-tree, Hash, GiST, GIN, BRIN), JSONB | Best for: complex queries, mixed OLTP/analytics, geospatial (PostGIS), JSON+relational hybrid | Scaling: read replicas, partitioning, PgBouncer, Citus for horizontal | Watch: write-heavy needs tuning, vertical scaling limits

Oracle

Strengths: RAC clustering, Data Guard, Flashback, mature optimizer, partitioning | Best for: enterprise OLTP, mission-critical with vendor support, large-scale DW | Scaling: RAC horizontal, partitioning, Active Data Guard read replicas | Watch: licensing cost, vendor lock-in

SQL Server

Strengths: BI integration (SSRS/SSAS/SSIS), Always On AG, TDE built-in, columnstore indexes | Best for: Microsoft ecosystem, BI-heavy, hybrid OLTP/analytics | Scaling: Always On AG for HA, read-scale replicas, partitioning | Watch: Windows-centric, licensing model

MySQL

Strengths: Simplicity, wide adoption, InnoDB ACID, good read performance, easy replication | Best for: web apps, read-heavy, simple transactional systems | Scaling: primary-replica, Group Replication, MySQL Router | Watch: less sophisticated optimizer than PostgreSQL, limited window functions in older versions

NoSQL Selection Guide

Document Stores (MongoDB, Couchbase)

JSON-like documents, flexible schemas | Best for: CMS, catalogs, user profiles, rapid prototyping | Query: MongoDB aggregation pipeline, Couchbase N1QL | Indexing: compound (ESR rule: Equality-Sort-Range), text, geospatial | Trade-offs: flexible schema vs consistency enforcement, $lookup joins expensive

Read the full file on GitHub · 75 lines

Changes

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.

  1. yesterday First seen · 75 lines · 38 tokens per session scan A 9c05a1dc0f39

Subscribe to this mod's changes

nw-database-technology-selection is a skill published in the GitHub repository nWave-ai/nWave (604 stars, last pushed 5d ago), licensed MIT. It adds 38 tokens to every session and 1,210 once invoked, about $0.0002 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.

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