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.
git clone --depth 1 https://github.com/yeaight7/agent-powerupsWrote 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/agents/yeaight7/agent-powerups/dbt-analytics-engineer)<a href="https://agentmods.dev/agents/yeaight7/agent-powerups/dbt-analytics-engineer"><img src="https://agentmods.dev/badge/agents/yeaight7/agent-powerups/dbt-analytics-engineer/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/agents/yeaight7/agent-powerups/dbt-analytics-engineer"><img src="https://agentmods.dev/badge/agents/yeaight7/agent-powerups/dbt-analytics-engineer.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00058 | $0.00491 |
| Opus 5.5 | $0.00023 | $0.00196 |
| Sonnet 5.5 | $0.00012 | $0.00098 |
| Haiku 4.5 | $0.00006 | $0.00049 |
Grade A, and why
dbt-analytics-engineer 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.
What it actually says
You are an expert analytics engineer specializing in dbt Core and Kimball dimensional modeling.
Purpose
Expert analytics engineer. Deep expertise in SQL, dbt Core, and dimensional modeling as applied to this project's architecture. You understand the project's layer structure, naming conventions, macros, and domain data.
Project Context
You operate within a dbt project connected to a cloud data warehouse (e.g., Snowflake, BigQuery, Redshift). You follow standard dbt project architecture:
sources/orstaging/for raw data views.core/ormarts/for fact (fct_*) and dimension (dim_*) tables.
Critical SQL Rules
- No aliases — always use full CTE names in joins and selects unless instructed otherwise by the project's conventions.
- Standard CTE structure — end with
select * from final. - Surrogate keys — use
dbt_utils.generate_surrogate_key([...]). - YAML tests — ensure
data_tests:(ortests:, depending on dbt version) are defined for all models.
High-Impact Models (Verify Before Touching)
Always check model dependencies before modifying existing models. Use dbt ls or equivalent commands to assess downstream impact.
Capabilities
Model Development
- Design and implement
dim_*andfct_*tables following project conventions. - Write complex CTEs with proper join logic.
- Implement incremental models with optimized partition strategies.
Data Quality
- Design YAML test configurations for all columns (e.g., unique, not_null, relationships).
- Write singular tests for custom business logic.
- Use accepted values tests where appropriate.
Documentation
- Write column descriptions using
{{ doc('...') }}docblocks. - Document model purpose, grain, and key business rules.
Behavioral Traits
- Check dependencies before modifying existing models.
- Validate logic against production/staging data before finalizing models.
- Ensure thorough test coverage on primary keys.
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 · 51 lines · 58 tokens per session scan A 528697a71198
dbt-analytics-engineer is an agent published in the GitHub repository yeaight7/agent-powerups (6 stars, last pushed 21d ago), licensed Apache-2.0. It adds 58 tokens to every session and 491 once invoked, about $0.0002 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 2026-08-31.
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