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 nWave-ai/nWave --skill nw-data-architecture-patternsgit clone --depth 1 https://github.com/nWave-ai/nWaveWrote 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/nwave-ai/nwave/nw-data-architecture-patterns)<a href="https://agentmods.dev/skills/nwave-ai/nwave/nw-data-architecture-patterns"><img src="https://agentmods.dev/badge/skills/nwave-ai/nwave/nw-data-architecture-patterns/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/nwave-ai/nwave/nw-data-architecture-patterns"><img src="https://agentmods.dev/badge/skills/nwave-ai/nwave/nw-data-architecture-patterns.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 | $0.00037 | $0.01569 |
| Opus 5.5 | $0.00015 | $0.00628 |
| Sonnet 5.5 | $0.00007 | $0.00314 |
| Haiku 4.5 | $0.00004 | $0.00157 |
Grade A, and why
nw-data-architecture-patterns 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 1mo 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 — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Architecture Patterns
Architecture Selection Decision Tree
Structured only -> Data Warehouse | Mixed + SQL analytics -> Data Lakehouse | Mixed + ML-primary -> Data Lake | Large org + autonomous domains -> Data Mesh
Data Warehouse
Schema: structured, schema-on-write | Data: tables, rows, columns | Governance: centralized | Query: SQL analytics, BI | Architecture: centralized single source of truth
Schema Patterns
Star Schema: Central fact table (measures) surrounded by denormalized dimension tables. Best for BI dashboards, standard reporting.
Snowflake Schema: Normalized dimensions (dimensions reference other dimensions). Reduces storage, increases JOIN complexity. Best when storage cost matters more than query speed.
Kimball vs Inmon
Kimball (Bottom-Up): Build data marts first, integrate later | Star schema, business-process driven | Faster initial delivery | Best for quick wins, department-level analytics
Inmon (Top-Down): Build enterprise DW first, derive data marts | Normalized 3NF enterprise model | Higher upfront effort | Best for large enterprises needing single source of truth
Technology: Snowflake | Amazon Redshift | Google BigQuery | Azure Synapse Analytics
Data Lake
Schema-on-read, flexible | All formats (structured, semi-structured, unstructured) | Raw data in native format | Query via Athena, Spark SQL, PySpark, Pandas | Risk: "data swamp" without governance
Organization
Zones: raw (landing, original format) -> curated (cleaned, validated) -> processed (transformed for use cases) -> archive (cold storage)
Anti-Patterns
- No metadata catalog -> undiscoverable data
- No access controls -> security/compliance risk
- No data quality checks -> garbage in/out
- No retention policy -> unbounded cost growth
Technology: S3 + Athena/Glue | Azure Data Lake Storage + Synapse | HDFS + Hive
Data Lakehouse
Combines warehouse reliability with lake flexibility | Schema enforcement on write with evolution support | ACID transactions on lake storage | Supports both BI/SQL and ML/data science workloads
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.
- 1mo ago First seen · 129 lines · 37 tokens per session scan A 27fa7113dc70
nw-data-architecture-patterns is a skill published in the GitHub repository nWave-ai/nWave (628 stars, last pushed 1mo ago), licensed MIT. It adds 37 tokens to every session and 1,569 once invoked, about $0.0001 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 2026-09-03.
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