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/irahardianto/rugged-gemini/data-engineeringnpx skills add irahardianto/rugged-gemini --skill data-engineeringgit clone --depth 1 https://github.com/irahardianto/rugged-geminiWrote 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/irahardianto/rugged-gemini/data-engineering)<a href="https://agentmods.dev/skills/irahardianto/rugged-gemini/data-engineering"><img src="https://agentmods.dev/badge/skills/irahardianto/rugged-gemini/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 | $0.00031 | $0.00654 |
| Opus 5 | $0.00015 | $0.00327 |
| Sonnet 5 | $0.00006 | $0.00131 |
| Haiku 4.5 | $0.00003 | $0.00065 |
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 5d 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Engineering Principles
Guidelines for building reliable, scalable data pipelines and platforms.
When to Invoke
- Designing data pipelines (ETL/ELT)
- Evaluating batch vs stream processing
- Data quality and governance requirements
- Data warehouse/lake architecture decisions
Pipeline Architecture
Design Principles
- Idempotent pipelines — re-running produces same result. Use upserts, not inserts.
- Schema evolution — handle new fields without breaking consumers.
- Exactly-once processing — deduplication at ingestion, idempotency keys.
- Incremental processing — process only new/changed data, not full reloads.
Patterns
| Pattern | When to Use |
|---|---|
| Batch ETL | Scheduled, high volume, latency-tolerant |
| Streaming | Real-time, event-driven, low latency |
| Lambda | Both batch and stream (complexity trade-off) |
| Kappa | Stream-only, reprocessing via replay |
| Medallion | Bronze (raw) → Silver (cleaned) → Gold (curated) |
Data Quality
Checks (Non-Negotiable)
- Completeness — no unexpected nulls in required fields
- Uniqueness — no duplicate records on primary keys
- Referential integrity — foreign keys resolve
- Freshness — data arrives within SLA window
- Volume — row counts within expected range (±threshold)
Framework
Source → Validate (schema, nulls, types) → Transform → Validate (business rules) → Load → Verify (counts, checksums)
Orchestration
| Tool | Strength |
|---|---|
| Apache Airflow | Most mature, Python-native, DAG-based |
| Dagster | Type-safe, asset-oriented, modern |
| Prefect | Pythonic, flow-based, cloud-native |
Best Practices
- DAGs should be idempotent and retriable
- Separate orchestration from computation
- Use backfill capabilities for historical reprocessing
- Alert on SLA breaches, not just failures
Data Modeling
| Model | When |
|---|---|
| Star schema | Analytics, BI dashboards, simple queries |
| Data Vault | Enterprise, auditability, multiple sources |
| Dimensional | Aggregated reporting, OLAP |
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.
- 5d ago First seen · 81 lines · 31 tokens per session scan A a953ce0d5bd6
data-engineering is a skill published in the GitHub repository irahardianto/rugged-gemini (5 stars, last pushed 3mo ago), licensed MIT. It adds 31 tokens to every session and 654 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 2026-08-31.
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