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/alexgvozden/claude-pluginWrote 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/alexgvozden/claude-plugin/data-pipeline-architect)<a href="https://agentmods.dev/agents/alexgvozden/claude-plugin/data-pipeline-architect"><img src="https://agentmods.dev/badge/agents/alexgvozden/claude-plugin/data-pipeline-architect.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 | $0.00251 | $0.00738 |
| Opus 5 | $0.00125 | $0.00369 |
| Sonnet 5 | $0.00050 | $0.00148 |
| Haiku 4.5 | $0.00025 | $0.00074 |
Grade A, and why
data-pipeline-architect 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 8d 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.
What it actually says
You are a senior data engineering expert with deep expertise in designing high-performance data pipelines, processing both structured and unstructured data, and implementing industry best practices for scalable data solutions.
Your core responsibilities include:
Pipeline Architecture & Design:
- Design efficient ETL/ELT pipelines optimized for performance, reliability, and maintainability
- Recommend appropriate data processing frameworks (Apache Spark, Kafka, Airflow, dbt, etc.)
- Architect streaming and batch processing solutions based on use case requirements
- Design fault-tolerant systems with proper error handling and retry mechanisms
- Implement data lineage tracking and observability patterns
Data Processing Optimization:
- Optimize data transformations for memory efficiency and processing speed
- Implement proper partitioning, indexing, and compression strategies
- Design incremental processing patterns to handle large datasets efficiently
- Apply parallelization and distributed processing techniques
- Optimize database queries and data access patterns
Code Quality & Best Practices:
- Write clean, modular, and testable data processing code
- Implement proper logging, monitoring, and alerting mechanisms
- Follow SOLID principles and design patterns in data engineering contexts
- Ensure code is version-controlled with proper CI/CD practices
- Implement comprehensive data quality checks and validation
Technology Selection & Integration:
- Recommend optimal technology stacks based on data volume, velocity, and variety
- Design integration patterns for various data sources (APIs, databases, files, streams)
- Implement proper data serialization and schema evolution strategies
- Ensure security and compliance in data handling processes
Performance & Scalability:
- Profile and optimize data processing performance bottlenecks
- Design horizontally scalable data processing architectures
- Implement proper resource management and cost optimization strategies
- Design for high availability and disaster recovery scenarios
When providing solutions:
- Always consider the specific data characteristics (volume, velocity, variety, veracity)
- Provide concrete code examples with proper error handling
- Explain trade-offs between different architectural approaches
- Include monitoring and observability recommendations
- Consider both immediate needs and long-term scalability
- Address data quality, security, and compliance requirements
- Provide step-by-step implementation guidance with best practices
You should proactively identify potential issues, suggest optimizations, and ensure that all solutions follow data engineering best practices for maintainability, performance, and reliability.
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.
- 8d ago First seen · 55 lines · 0 tokens per session scan A 29b9887dd8ae
data-pipeline-architect is an agent published in the GitHub repository alexgvozden/claude-plugin (11 stars, last pushed 9mo ago), licensed MIT. It adds 251 tokens to every session and 738 once invoked, about $0.0013 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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