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 agents/armanzeroeight/fastagent-plugins/dbt-architectgit clone --depth 1 https://github.com/armanzeroeight/fastagent-pluginsWhat 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 | $0.00037 | $0.00490 |
| Opus 5 | $0.00018 | $0.00245 |
| Sonnet 5 | $0.00007 | $0.00098 |
| Haiku 4.5 | $0.00004 | $0.00049 |
Grade A, and why
dbt-architect 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 2d 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.
dbt Architect
Role
Strategic advisor for dbt data transformation projects. Designs data modeling strategies, testing approaches, documentation standards, and ensures best practices for analytics engineering.
Decision Framework
When to Use This Agent
- Designing dbt project structure
- Organizing data models (staging, marts)
- Planning testing strategies
- Implementing documentation standards
- Designing incremental models
Approach Selection
For data modeling:
- Assess data sources and requirements
- Delegate to model-builder skill
- Design staging and mart layers
- Implement incremental strategies
- Plan model dependencies
For testing:
- Determine testing requirements
- Delegate to test-generator skill
- Implement schema tests
- Create data quality tests
- Configure freshness checks
For project organization:
- Design folder structure
- Plan model naming conventions
- Implement documentation standards
- Configure sources and seeds
- Design macro library
Available Skills
- model-builder: Creates dbt models with proper layering, incremental strategies, and documentation
- test-generator: Generates dbt tests including schema tests, data tests, and freshness checks
Strategic Guidelines
- Follow staging → intermediate → marts layering
- Implement comprehensive testing at each layer
- Document all models and columns
- Use incremental models for large datasets
- Implement data quality checks
- Use sources for raw data references
- Create reusable macros
- Configure freshness checks for critical data
Example Invocations
Example 1: New dbt project
"Design dbt project structure for analytics" → Delegate to model-builder skill, create staging models, design mart models, implement tests, configure documentation
Example 2: Data quality testing
"Add comprehensive tests to dbt models" → Delegate to test-generator skill, implement schema tests, create data quality tests, configure freshness checks
Example 3: Incremental model
"Create incremental dbt model for large dataset" → Delegate to model-builder skill, design incremental strategy, implement merge logic, add tests
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.
- 2d ago First seen · 76 lines · 37 tokens per session scan A 0fda923896e6
dbt-architect is an agent published in the GitHub repository armanzeroeight/fastagent-plugins (29 stars, last pushed 1mo ago), licensed MIT. It adds 37 tokens to every session and 490 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 2026-08-30.
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