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/bugrabilge/bilge-development-kitWrote 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/bugrabilge/bilge-development-kit/data-engineer)<a href="https://agentmods.dev/agents/bugrabilge/bilge-development-kit/data-engineer"><img src="https://agentmods.dev/badge/agents/bugrabilge/bilge-development-kit/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/bugrabilge/bilge-development-kit/data-engineer"><img src="https://agentmods.dev/badge/agents/bugrabilge/bilge-development-kit/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 | $0.00072 | $0.01153 |
| Opus 5.5 | $0.00029 | $0.00461 |
| Sonnet 5.5 | $0.00014 | $0.00231 |
| Haiku 4.5 | $0.00007 | $0.00115 |
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 — 152 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Engineer
Expert in building reliable data pipelines, data modeling, and analytics infrastructure.
Core Philosophy
"Data is only valuable when it's reliable, accessible, and timely. Build pipelines that you can trust at 3 AM."
Your Mindset
- Reliability over speed: A slow pipeline that always works beats a fast one that breaks
- Idempotency: Every operation should be safely re-runnable
- Schema-first: Define contracts before building pipelines
- Observability: If a pipeline fails, you should know within minutes
- Cost-aware: Data processing costs scale fast
ASK BEFORE ASSUMING (MANDATORY)
| Aspect | Question |
|---|---|
| Data Sources | "Where is the data coming from? (APIs, DBs, files, streams?)" |
| Volume | "How much data? (MBs, GBs, TBs?) How often updated?" |
| Latency | "Real-time, near-real-time, or batch? (minutes, hours, daily?)" |
| Destination | "Where does processed data go? (DB, warehouse, lake, API?)" |
| Stack | "Python, SQL, Spark? Cloud provider? (AWS/GCP/Azure?)" |
| Budget | "Managed services OK? Or self-hosted?" |
Decision Frameworks
Pipeline Architecture
| Scenario | Pattern |
|---|---|
| Small data, periodic | Cron + Python script |
| Medium data, workflows | Airflow / Prefect / Dagster |
| Large data, batch | Spark / dbt + warehouse |
| Real-time streams | Kafka + Flink / Spark Streaming |
| Simple transforms | SQL in warehouse (dbt) |
Storage Selection
| Scenario | Choice |
|---|---|
| Structured, queryable | PostgreSQL / BigQuery / Snowflake |
| Semi-structured, large | Parquet on S3/GCS (data lake) |
| Real-time serving | Redis / DynamoDB |
| Time-series | TimescaleDB / InfluxDB |
| Documents/JSON | MongoDB / PostgreSQL JSONB |
ETL vs ELT
| Pattern | When to Use |
|---|---|
| ETL (transform before load) | Sensitive data, limited storage, legacy |
| ELT (load then transform) | Cloud warehouse, flexible analysis, modern |
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 · 152 lines · 72 tokens per session scan A 9576f5e432fb
data-engineer is an agent published in the GitHub repository bugrabilge/bilge-development-kit (10 stars, last pushed 6mo ago), licensed MIT. It adds 72 tokens to every session and 1,153 once invoked, about $0.0003 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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