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 MonumentalSystems/Atlas-Agent-Teams --skill data-engineeringgit clone --depth 1 https://github.com/MonumentalSystems/Atlas-Agent-TeamsWrote 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/monumentalsystems/atlas-agent-teams/data-engineering)<a href="https://agentmods.dev/skills/monumentalsystems/atlas-agent-teams/data-engineering"><img src="https://agentmods.dev/badge/skills/monumentalsystems/atlas-agent-teams/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 | $0.00024 | $0.01116 |
| Opus 5 | $0.00012 | $0.00558 |
| Sonnet 5 | $0.00005 | $0.00223 |
| Haiku 4.5 | $0.00002 | $0.00112 |
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 7d 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 — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Engineering
Data Pipeline Patterns
Batch Processing
- Scheduled Jobs: Run data processing at fixed intervals (hourly, daily, weekly)
- Use Cases: Historical analysis, reporting, data warehousing
- Tools: Apache Spark, Hadoop, Airflow, dbt
- Design Considerations: Latency tolerance, resource efficiency, cost optimization
Streaming Processing
- Real-time Ingestion: Process data as it arrives with low latency
- Use Cases: Real-time analytics, monitoring, fraud detection
- Tools: Apache Kafka, Apache Flink, Apache Storm, Apache Beam
- Design Considerations: Event ordering, exactly-once semantics, backpressure
Lambda Architecture
- Batch Layer: Store immutable master dataset, compute batch views
- Speed Layer: Process real-time data for low-latency queries
- Serving Layer: Merge batch and real-time views for queries
- Use Cases: Systems requiring both batch and real-time capabilities
- Challenges: Complexity of maintaining two code paths
Kappa Architecture
- Unified Processing: Use a single stream processing framework
- Replay Capability: Reprocess data from the event log
- Use Cases: Simplified architecture when batch is just fast streaming
- Benefits: Reduced complexity, single codebase
ETL/ELT Best Practices
ETL (Extract, Transform, Load)
- Extract: Pull data from source systems with minimal impact
- Transform: Clean, validate, and transform data in a staging area
- Load: Load processed data into the target system
- Best Practices:
- Minimize source system impact
- Handle incremental updates efficiently
- Validate data before loading
- Document transformation logic
ELT (Extract, Load, Transform)
- Extract: Pull raw data from source systems
- Load: Load raw data into the target system (usually data warehouse)
- Transform: Transform data within the target system using SQL
- Best Practices:
- Leverage data warehouse compute power
- Maintain raw data for audit trails
- Use dbt for transformation orchestration
- Version control transformation logic
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.
- 7d ago First seen · 118 lines · 24 tokens per session scan A 2bffc9cde00f
data-engineering is a skill published in the GitHub repository MonumentalSystems/Atlas-Agent-Teams (21 stars, last pushed 26d ago), licensed MIT. It adds 24 tokens to every session and 1,116 once invoked, about $0.0001 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 2026-08-30.
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