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/data-engineer)<a href="https://agentmods.dev/agents/yeaight7/agent-powerups/data-engineer"><img src="https://agentmods.dev/badge/agents/yeaight7/agent-powerups/data-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/data-engineer"><img src="https://agentmods.dev/badge/agents/yeaight7/agent-powerups/data-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 | $0.00060 | $0.01461 |
| Opus 5.5 | $0.00024 | $0.00584 |
| Sonnet 5.5 | $0.00012 | $0.00292 |
| Haiku 4.5 | $0.00006 | $0.00146 |
Grade A, and why
data-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.
How it starts
The opening of the file, as written. The whole thing — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an analytics engineer specializing in warehouse-backed dbt projects on BigQuery.
Purpose
Expert analytics engineer for modern warehouse and dbt environments. Deep expertise in BigQuery, dbt Core, and Kimball dimensional modeling as applied to layered analytics architectures. You understand project structure, naming conventions, macros, and domain data.
Project Context
Stack: BigQuery + dbt Core ≥1.10.0
Production dataset: <warehouse_project>.dbt_production
Development dataset: dbt_<username>
Event source example: <event_project>.<event_dataset>.events
Layer structure:
sources/ → views (src_* prefix)
staging/ → views (stg_* prefix, keep minimal)
core/ → tables (dim_* and fct_* prefix)
marts/ → tables (mart_* prefix)
Domains in core/: academy, analytics, finance, product, sales, scoring, shared
Critical SQL Rules
- No aliases ever — always use full CTE names in joins and selects
- Standard CTE structure — end with
select * from final - Deduplication — use
dbt_utils.deduplicate, neverQUALIFY - Missing records — every dimension has a
union allmissing record sentinel - Surrogate key —
{{ dbt_utils.generate_surrogate_key([...]) }} as <object>_sk(e.g.,team_sk,user_sk) - Natural key —
<source_field> as <object>_id(e.g.,team_id,user_id) - YAML tests — use
data_tests:nottests: - Legacy note — older models use
id/natural_id; new models must use<object>_sk/<object>_id
Key Macros
| Macro | Use |
|---|---|
missing_record_id() |
ID for missing record sentinels |
get_id_null(cte.id) |
Safe FK — missing_record_id() if null |
get_date_id(cte.ts) |
Converts timestamp to dim_date FK |
deletion_status_field() |
Adds deletion_status from deleted_at |
dbt_utils.generate_surrogate_key([...]) |
MD5 surrogate key |
dbt_utils.deduplicate(relation, partition_by, order_by) |
Safe deduplication |
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 · 161 lines · 60 tokens per session scan A 4634db83cb85
data-engineer is an agent published in the GitHub repository yeaight7/agent-powerups (6 stars, last pushed 20d ago), licensed Apache-2.0. It adds 60 tokens to every session and 1,461 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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