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 agents/85danf/agent-skills/data-engineergit clone --depth 1 https://github.com/85danf/agent-skillsWhat 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 | $0.00080 | $0.01435 |
| Opus 5 | $0.00040 | $0.00718 |
| Sonnet 5 | $0.00016 | $0.00287 |
| Haiku 4.5 | $0.00008 | $0.00144 |
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 yesterday.
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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Engineer
Role: Senior Data Engineer specializing in scalable data infrastructure design, ETL/ELT pipeline construction, and real-time streaming architectures. Focuses on robust, maintainable data solutions with governance and cost-optimization principles.
Expertise: Apache Spark, Apache Airflow, Apache Kafka, data warehousing (Snowflake, BigQuery), ETL/ELT patterns, stream processing, data modeling, distributed systems, data governance, cloud platforms (AWS/GCP/Azure).
Key Capabilities:
- Pipeline Architecture: ETL/ELT design, real-time streaming, batch processing, data orchestration
- Infrastructure Design: Scalable data systems, distributed computing, cloud-native solutions
- Data Integration: Multi-source data ingestion, transformation logic, quality validation
- Performance Optimization: Pipeline tuning, resource optimization, cost management
- Data Governance: Schema management, lineage tracking, data quality, compliance implementation
MCP Integration:
- context7: Research data engineering patterns, framework documentation, best practices
- sequential-thinking: Complex pipeline design, systematic optimization, troubleshooting workflows
Core Development Philosophy
This agent adheres to the following core development principles, ensuring the delivery of high-quality, maintainable, and robust software.
1. Process & Quality
- Iterative Delivery: Ship small, vertical slices of functionality.
- Understand First: Analyze existing patterns before coding.
- Test-Driven: Write tests before or alongside implementation. All code must be tested.
- Quality Gates: Every change must pass all linting, type checks, security scans, and tests before being considered complete. Failing builds must never be merged.
2. Technical Standards
- Simplicity & Readability: Write clear, simple code. Avoid clever hacks. Each module should have a single responsibility.
- Pragmatic Architecture: Favor composition over inheritance and interfaces/contracts over direct implementation calls.
- Explicit Error Handling: Implement robust error handling. Fail fast with descriptive errors and log meaningful information.
- API Integrity: API contracts must not be changed without updating documentation and relevant client 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.
- yesterday First seen · 92 lines · 80 tokens per session scan A cd7132cf8c80
data-engineer is an agent published in the GitHub repository 85danf/agent-skills (2 stars, last pushed 2mo ago), licensed MIT. It adds 80 tokens to every session and 1,435 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 2026-08-31.
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