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 Future-CX/AI-Architecture-Toolkit --skill data-architecture-designgit clone --depth 1 https://github.com/Future-CX/AI-Architecture-ToolkitWrote 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/future-cx/ai-architecture-toolkit/data-architecture-design)<a href="https://agentmods.dev/skills/future-cx/ai-architecture-toolkit/data-architecture-design"><img src="https://agentmods.dev/badge/skills/future-cx/ai-architecture-toolkit/data-architecture-design.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 5.1 | $0.00062 | $0.02868 |
| Opus 5 | $0.00031 | $0.01434 |
| Sonnet 5 | $0.00012 | $0.00574 |
| Haiku 4.5 | $0.00006 | $0.00287 |
Grade A, and why
data-architecture-design 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 3d 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 — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Architecture Design
Quick Start
Assume the Data Architect Agent role from agents/data-architect.md: clarify the canonical data object, ownership, source of truth, lifecycle, quality, privacy, retention, integration flows, and governance expectations.
Before creating or updating any data architecture design files, run a grill-me clarification session using ../grill-me/SKILL.md. Ask one question at a time until the target architecture link, data object meaning, ownership, lifecycle, integrations, quality expectations, privacy concerns, assumptions, and open questions are clear enough to avoid avoidable misunderstanding.
During that clarification session, use ../ubiquitous-language/SKILL.md whenever terms are vague, missing, overloaded, conflicting, or important enough to become shared domain language. Create or update <private-lab-root>/GLOSSARY.md inline as terms are clarified; do not batch glossary updates until the end.
Use templates/data-architecture-design-template.md as the output structure. Replace placeholders and drafting guidance with concrete content; mark unknown facts as TBD or open questions.
Store generated data architecture designs under the consuming repository's private lab root:
data-architectures/<data-object-slug>/<data-architecture-basename>.md
data-architectures/<data-object-slug>/diagrams/
Derive <data-architecture-basename> without repeating data:
- When
<data-object-slug>ends in-data, use<data-object-slug>-architecture-design. - Otherwise, use
<data-object-slug>-data-architecture-design. - Never generate a filename containing
-data-data-.
Examples: customer-data becomes customer-data-architecture-design.md, customer-user-data becomes customer-user-data-architecture-design.md, and audiences becomes audiences-data-architecture-design.md.
Do not write real-company data architecture details into this public toolkit repository.
Required Inputs
- Target architecture document to link to
- Canonical data object name
- Data object description and business purpose
- Main capability or business process using the data object
- Source of truth and data owner
- Data classification
- Producing systems, consuming systems, and integration touchpoints
- Data lifecycle states, retention, privacy, and classification expectations
- Known quality rules, reconciliation needs, lineage needs, and governance constraints
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.
- 3d ago Changed · -3 lines · -4 tokens per session bca9c6a33c87
- 7d ago First seen · 154 lines · 66 tokens per session scan A fb927e8d1e71
data-architecture-design is a skill published in the GitHub repository Future-CX/AI-Architecture-Toolkit (5 stars, last pushed 4d ago), licensed MIT. It adds 62 tokens to every session and 2,868 once invoked, about $0.0003 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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