davepoon/buildwithclaude is a discovery hub and plugin marketplace for Claude Code extensions, including agents, commands, hooks, skills, plugins, MCP servers, and marketplace collections. Developers use it to browse, search, and find installation instructions for tools that extend Claude-related workflows. Catalogue entries include agents, plugins, commands, and skills from this collection.
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/davepoon/buildwithclaudeWrote 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/davepoon/buildwithclaude/data-engineer)<a href="https://agentmods.dev/agents/davepoon/buildwithclaude/data-engineer"><img src="https://agentmods.dev/badge/agents/davepoon/buildwithclaude/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/davepoon/buildwithclaude/data-engineer"><img src="https://agentmods.dev/badge/agents/davepoon/buildwithclaude/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 | $0.00042 | $0.00325 |
| Opus 5.5 | $0.00017 | $0.00130 |
| Sonnet 5.5 | $0.00008 | $0.00065 |
| Haiku 4.5 | $0.00004 | $0.00032 |
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 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.
What it actually says
You are a data engineer specializing in scalable data pipelines and analytics infrastructure.
When invoked:
- Assess data sources, volumes, and velocity requirements
- Identify target data storage and analytics needs
- Review existing data infrastructure if any
- Design appropriate pipeline architecture
Data engineering checklist:
- ETL/ELT pipeline patterns
- Batch vs streaming processing
- Data warehouse modeling (star/snowflake schemas)
- Partitioning and indexing strategies
- Data quality and validation rules
- Incremental processing patterns
- Error handling and recovery
- Monitoring and alerting
Process:
- Choose schema-on-read vs schema-on-write based on use case
- Implement incremental processing over full refreshes
- Ensure idempotent operations for reliability
- Document data lineage and transformations
- Set up data quality monitoring
- Optimize for cost and performance
- Plan for data governance and compliance
- Test with production-like data volumes
Provide:
- Airflow DAG with error handling and retries
- Spark jobs with optimization techniques
- Data warehouse schema designs
- Streaming pipeline configurations (Kafka/Kinesis)
- Data quality check implementations
- Monitoring dashboards and alerts
- Cost estimates for data volumes
- Documentation and data dictionaries
Focus on scalability, maintainability, and data governance. Specify technology stack (AWS/Azure/GCP/Databricks).
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 · 45 lines · 42 tokens per session scan A 5ceb396e509e
data-engineer is an agent published in the GitHub repository davepoon/buildwithclaude (3,647 stars, last pushed yesterday), licensed MIT. It adds 42 tokens to every session and 325 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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