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 skills add Kilo-Org/kilo-marketplace --skill senior-data-engineergit clone --depth 1 https://github.com/Kilo-Org/kilo-marketplaceWrote 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/kilo-org/kilo-marketplace/senior-data-engineer)<a href="https://agentmods.dev/skills/kilo-org/kilo-marketplace/senior-data-engineer"><img src="https://agentmods.dev/badge/skills/kilo-org/kilo-marketplace/senior-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/skills/kilo-org/kilo-marketplace/senior-data-engineer"><img src="https://agentmods.dev/badge/skills/kilo-org/kilo-marketplace/senior-data-engineer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00085 | $0.01433 |
| Opus 5 | $0.00043 | $0.00717 |
| Sonnet 5 | $0.00017 | $0.00287 |
| Haiku 4.5 | $0.00009 | $0.00143 |
Grade A, and why
senior-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 9d 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 — 204 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Senior Data Engineer
Production-grade data engineering skill for building scalable, reliable data systems.
Table of Contents
- Trigger Phrases
- Quick Start
- Workflows
- Architecture Decision Framework
- Tech Stack
- Reference Documentation
- Troubleshooting
Trigger Phrases
Activate this skill when you see:
Pipeline Design:
- "Design a data pipeline for..."
- "Build an ETL/ELT process..."
- "How should I ingest data from..."
- "Set up data extraction from..."
Architecture:
- "Should I use batch or streaming?"
- "Lambda vs Kappa architecture"
- "How to handle late-arriving data"
- "Design a data lakehouse"
Data Modeling:
- "Create a dimensional model..."
- "Star schema vs snowflake"
- "Implement slowly changing dimensions"
- "Design a data vault"
Data Quality:
- "Add data validation to..."
- "Set up data quality checks"
- "Monitor data freshness"
- "Implement data contracts"
Performance:
- "Optimize this Spark job"
- "Query is running slow"
- "Reduce pipeline execution time"
- "Tune Airflow DAG"
Quick Start
Core Tools
# Generate pipeline orchestration config
python scripts/pipeline_orchestrator.py generate \
--type airflow \
--source postgres \
--destination snowflake \
--schedule "0 5 * * *"
# Validate data quality
python scripts/data_quality_validator.py validate \
--input data/sales.parquet \
--schema schemas/sales.json \
--checks freshness,completeness,uniqueness
# Optimize ETL performance
python scripts/etl_performance_optimizer.py analyze \
--query queries/daily_aggregation.sql \
--engine spark \
--recommend
Workflows
→ See references/workflows.md for details
Architecture Decision Framework
Use this framework to choose the right approach for your data pipeline.
Batch vs Streaming
| Criteria | Batch | Streaming |
|---|---|---|
| Latency requirement | Hours to days | Seconds to minutes |
| Data volume | Large historical datasets | Continuous event streams |
| Processing complexity | Complex transformations, ML | Simple aggregations, filtering |
| Cost sensitivity | More cost-effective | Higher infrastructure cost |
| Error handling | Easier to reprocess | Requires careful design |
What ships with it
9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- LICENSE 1.0 KB
- references/data_modeling_patterns.md 26 KB
- references/data_pipeline_architecture.md 34 KB
- references/dataops_best_practices.md 42 KB
- references/troubleshooting.md 3.2 KB
- references/workflows.md 15 KB
- scripts/data_quality_validator.py 59 KB runs code
- scripts/etl_performance_optimizer.py 65 KB runs code
- scripts/pipeline_orchestrator.py 28 KB runs code
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.
- 9d ago First seen · 204 lines · 85 tokens per session scan A 296c35454919
senior-data-engineer is a skill published in the GitHub repository Kilo-Org/kilo-marketplace (175 stars, last pushed 22d ago), licensed Apache-2.0. It adds 85 tokens to every session and 1,433 once invoked, about $0.0004 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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