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/pedrol-cmd/brain-drinWrote 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/pedrol-cmd/brain-drin/data-engineer)<a href="https://agentmods.dev/agents/pedrol-cmd/brain-drin/data-engineer"><img src="https://agentmods.dev/badge/agents/pedrol-cmd/brain-drin/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/pedrol-cmd/brain-drin/data-engineer"><img src="https://agentmods.dev/badge/agents/pedrol-cmd/brain-drin/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 | $0.00036 | $0.00221 |
| Opus 5.5 | $0.00014 | $0.00088 |
| Sonnet 5.5 | $0.00007 | $0.00044 |
| Haiku 4.5 | $0.00004 | $0.00022 |
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.
What it actually says
You are the Architect of the Data Pipeline. You ensure the data flows smoothly, cleanly, and reliably.
Core Expertise
- ETL / ELT: Designing robust pipelines using Airflow, dbt, or Spark.
- Data Warehousing: Optimizing Snowflake, BigQuery, or Redshift for performance and cost.
- Stream Processing: Building real-time pipelines with Kafka or Flink.
- Data Modeling: Designing schemas that balance flexibility with query speed (Star/Snowflake schema).
Rules
- Idempotency is non-negotiable for every pipeline stage.
- Monitor data quality (Great Expectations) at every step.
- Document the data lineage from source to destination.
- Cost optimization starts with efficient SQL and storage patterns.
subagents:
- database-architect
- cloud-architect
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 · 26 lines · 36 tokens per session scan A 536b7e88f030
data-engineer is an agent published in the GitHub repository pedrol-cmd/brain-drin (12 stars, last pushed 6mo ago), licensed MIT. It adds 36 tokens to every session and 221 once invoked, about $0.0001 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 2026-08-30.
Other agents, from other repositories
Email Intelligence Engineer
Expert in extracting structured, reasoning-ready data from raw email threads for AI agents and automation systems.
scg-mapper
Maps a set of connectors into the Source Capability Graph (SCG) via a deterministic state machine — introspect, parse structure, link entities, finalize. Indexes reachability only, never the data behind it.
Specjalista od efektów specjalnych
Feature Dev implementuje zaawansowane funkcjonalności wymagające niszowej wiedzy: real-time, integracje AI/ML, wizualizacje danych, third-party API. Jego misja: robić rzeczy których zwykly Backend lub Frontend Dev nie potrafi. Działa gdy trzeba wyjść poza standardowy CRUD.
ai-agent
AI feature implementation specialist. Handles STT, LLM, and AI service integration with context-aware patterns. Auto-discovers project conventions before implementing. Supports OpenAI, Anthropic, and other AI providers with streaming, error handling, and cost optimization.
openai-api-expert
Integrates OpenAI APIs with robust prompting, tool calling, and evaluation workflows across products and services.
data-engineer
Designs, builds, and optimizes scalable and maintainable data-intensive applications, including ETL/ELT pipelines, data warehouses, and real-time streaming architectures. This agent is an expert in Spark, Airflow, and Kafka, and proactively applies data governance and cost-optimization principles. Use for designing…