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 skills/hoangatg/ai-agent-toolkit/data-engineeringnpx skills add hoangatg/ai-agent-toolkit --skill data-engineeringgit clone --depth 1 https://github.com/hoangatg/ai-agent-toolkitWrote 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/skills/hoangatg/ai-agent-toolkit/data-engineering)<a href="https://agentmods.dev/skills/hoangatg/ai-agent-toolkit/data-engineering"><img src="https://agentmods.dev/badge/skills/hoangatg/ai-agent-toolkit/data-engineering.svg" alt="Measured on agentmods" 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 | $0.00037 | $0.00793 |
| Opus 5 | $0.00018 | $0.00396 |
| Sonnet 5 | $0.00007 | $0.00159 |
| Haiku 4.5 | $0.00004 | $0.00079 |
Grade A, and why
data-engineering 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 — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Engineering
Move data reliably from where it is to where it needs to be.
1. Pipeline Architecture
ETL vs ELT
| Approach | Transform Where | Best For |
|---|---|---|
| ETL | Before loading | Legacy DW, data cleansing |
| ELT | After loading | Modern cloud DW (BigQuery, Snowflake) |
Pipeline Patterns
| Pattern | Use Case |
|---|---|
| Batch | Daily/hourly aggregations |
| Streaming | Real-time analytics, alerts |
| Micro-batch | Near-real-time (Spark Streaming) |
| Lambda | Batch + streaming combined |
| Kappa | Streaming-only (simplicity) |
2. Technology Selection
| Layer | Options |
|---|---|
| Ingestion | Kafka, Debezium, Fivetran, Airbyte |
| Processing | Spark, Flink, dbt, Pandas |
| Storage | S3, GCS, Delta Lake, Iceberg |
| Warehouse | BigQuery, Snowflake, Redshift, ClickHouse |
| Orchestration | Airflow, Dagster, Prefect |
| Visualization | Metabase, Looker, Superset |
3. dbt Patterns
| Concept | Purpose |
|---|---|
| Sources | Raw data documentation |
| Staging | Clean, rename, type-cast |
| Intermediate | Business logic transforms |
| Marts | Final business-facing tables |
| Tests | Data quality assertions |
dbt Best Practices
| Principle | Application |
|---|---|
| DRY models | Use CTEs and refs |
| Incremental | Process only new data |
| Documentation | Describe every model and column |
| Testing | unique, not_null, relationships |
| Version control | Git for all SQL transforms |
4. Data Quality
| Dimension | Check |
|---|---|
| Completeness | No missing required fields |
| Uniqueness | No unexpected duplicates |
| Freshness | Data is recent enough |
| Accuracy | Values within expected ranges |
| Consistency | Same data across systems |
Tool Options
| Tool | Purpose |
|---|---|
| Great Expectations | Python-based validation |
| dbt tests | SQL-based assertions |
| Soda | Data monitoring |
| Monte Carlo | Data observability |
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 · 116 lines · 37 tokens per session scan A 8ab659498108
data-engineering is a skill published in the GitHub repository hoangatg/ai-agent-toolkit (1 stars, last pushed 5mo ago), licensed MIT. It adds 37 tokens to every session and 793 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 2026-09-03.
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