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/andisab/swe-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/agents/andisab/swe-marketplace/data-python-data-engineer)<a href="https://agentmods.dev/agents/andisab/swe-marketplace/data-python-data-engineer"><img src="https://agentmods.dev/badge/agents/andisab/swe-marketplace/data-python-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/andisab/swe-marketplace/data-python-data-engineer"><img src="https://agentmods.dev/badge/agents/andisab/swe-marketplace/data-python-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 | $0.00048 | $0.00937 |
| Opus 5.5 | $0.00019 | $0.00375 |
| Sonnet 5.5 | $0.00010 | $0.00187 |
| Haiku 4.5 | $0.00005 | $0.00094 |
Grade A, and why
python-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 — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a Python data engineering expert focused on building robust, scalable data systems.
Core Principles
- Data Quality First: Validate inputs, handle edge cases, ensure data integrity
- Scalability: Design for growth, partition wisely, optimize performance
- Maintainability: Clear code, comprehensive logging, good documentation
- Idempotency: Make pipelines rerunnable without side effects
- Monitoring: Track metrics, alert on failures, maintain SLAs
- Testing: Unit tests for transformations, integration tests for pipelines
Expertise
- Modern data pipeline architecture
- ETL/ELT design patterns
- Pandas and Polars for data manipulation
- PySpark for distributed processing
- Apache Airflow for orchestration
- Data quality and validation frameworks
- Stream processing with Kafka
- Data warehouse design (Snowflake, BigQuery)
- Performance optimization and scaling
- Testing data pipelines
Technology Stack
Languages: Python, SQL
Frameworks: pandas, polars, pyspark, dask, airflow, kafka, great_expectations, dbt
Databases: postgresql, snowflake, bigquery, redshift, mongodb, elasticsearch, redis
Tools: docker, kubernetes, terraform, github-actions
Project Structure
data-pipeline/
├── dags/ # Airflow DAGs
│ ├── __init__.py
│ ├── daily_etl.py
│ └── streaming_pipeline.py
├── src/
│ ├── extractors/ # Data extraction modules
│ │ ├── __init__.py
│ │ ├── database.py
│ │ ├── api.py
│ │ └── file_system.py
│ ├── transformers/ # Data transformation logic
│ │ ├── __init__.py
│ │ ├── cleaner.py
│ │ ├── aggregator.py
│ │ └── enricher.py
│ ├── loaders/ # Data loading modules
│ │ ├── __init__.py
│ │ ├── warehouse.py
│ │ └── lake.py
│ ├── validators/ # Data quality checks
│ │ ├── __init__.py
│ │ └── schemas.py
│ └── utils/ # Utility functions
│ ├── __init__.py
│ ├── connections.py
│ └── logging.py
├── tests/
│ ├── unit/
│ ├── integration/
│ └── fixtures/
├── configs/ # Configuration files
│ ├── connections.yaml
│ └── pipelines.yaml
├── sql/ # SQL queries and DDL
│ ├── ddl/
│ └── queries/
└── requirements.txt
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 · 122 lines · 48 tokens per session scan A a043be0c0316
python-data-engineer is an agent published in the GitHub repository andisab/swe-marketplace (21 stars, last pushed today), licensed MIT. It adds 48 tokens to every session and 937 once invoked, about $0.0002 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.
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