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 agentmods add skills/ancoleman/ai-design-components/architecting-datanpx skills add ancoleman/ai-design-components --skill architecting-datagit clone --depth 1 https://github.com/ancoleman/ai-design-componentsWrote 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/ancoleman/ai-design-components/architecting-data)<a href="https://agentmods.dev/skills/ancoleman/ai-design-components/architecting-data"><img src="https://agentmods.dev/badge/skills/ancoleman/ai-design-components/architecting-data.svg" alt="Measured on agentmods" 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 | $0.00081 | $0.03452 |
| Opus 5 | $0.00041 | $0.01726 |
| Sonnet 5 | $0.00016 | $0.00690 |
| Haiku 4.5 | $0.00008 | $0.00345 |
Grade A, and why
architecting-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 5d 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 — 399 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Architecture
Purpose
Guide architects and platform engineers through strategic data architecture decisions for modern cloud-native data platforms.
When to Use This Skill
Invoke this skill when:
- Designing a new data platform or modernizing legacy systems
- Choosing between data lake, data warehouse, or data lakehouse
- Deciding on data modeling approaches (dimensional, normalized, data vault, wide tables)
- Evaluating centralized vs data mesh architecture
- Selecting open table formats (Apache Iceberg, Delta Lake, Apache Hudi)
- Designing medallion architecture (bronze, silver, gold layers)
- Implementing data governance and cataloging
Core Concepts
1. Storage Paradigms
Three primary patterns for analytical data storage:
Data Lake: Centralized repository for raw data at scale
- Schema-on-read, cost-optimized ($0.02-0.03/GB/month)
- Use when: Diverse data sources, exploratory analytics, ML/AI training data
Data Warehouse: Structured repository optimized for BI
- Schema-on-write, ACID transactions, fast queries
- Use when: Known BI requirements, strong governance needed
Data Lakehouse: Hybrid combining lake flexibility with warehouse reliability
- Open table formats (Iceberg, Delta Lake), ACID on object storage
- Use when: Mixed BI + ML workloads, cost optimization (60-80% cheaper than warehouse)
Decision Framework:
- BI/Reporting only + Known queries → Data Warehouse
- ML/AI primary + Raw data needed → Data Lake or Lakehouse
- Mixed BI + ML + Cost optimization → Data Lakehouse (recommended)
- Exploratory/Unknown use cases → Data Lake
For detailed comparison, see references/storage-paradigms.md.
2. Data Modeling Approaches
Four primary modeling patterns:
Dimensional (Kimball): Star/snowflake schemas for BI
- Use when: Known query patterns, BI dashboards, trend analysis
Normalized (3NF): Eliminate redundancy for transactional systems
- Use when: OLTP systems, frequent updates, strong consistency
What ships with it
13 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.
- examples/dbt-project/README.md 925 B
- examples/dbt-project/stg_customers.sql 380 B
- outputs.yaml 7.5 KB
- references/data-mesh-guide.md 3.5 KB
- references/decision-frameworks.md 14 KB
- references/governance-patterns.md 3.7 KB
- references/medallion-pattern.md 3.2 KB
- references/modeling-approaches.md 13 KB
- references/modern-data-stack.md 5.8 KB
- references/scenarios.md 7.1 KB
- references/storage-paradigms.md 14 KB
- references/table-formats.md 1.5 KB
- references/tool-recommendations.md 11 KB
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.
- 5d ago First seen · 399 lines · 81 tokens per session scan A ae7daf6ee248
architecting-data is a skill published in the GitHub repository ancoleman/ai-design-components (517 stars, last pushed 8mo ago), licensed MIT. It adds 81 tokens to every session and 3,452 once invoked, about $0.0004 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 2026-08-30.
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