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/EngineerWithAI/engineerwith-agentsWrote 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/plugins/engineerwithai/engineerwith-agents/data-engineering)<a href="https://agentmods.dev/plugins/engineerwithai/engineerwith-agents/data-engineering"><img src="https://agentmods.dev/badge/plugins/engineerwithai/engineerwith-agents/data-engineering/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/plugins/engineerwithai/engineerwith-agents/data-engineering"><img src="https://agentmods.dev/badge/plugins/engineerwithai/engineerwith-agents/data-engineering.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 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
{
"name": "data-engineering",
"source": "./plugins/data-engineering",
"description": "ETL pipeline construction, data warehouse design, batch processing workflows, and data-driven feature development",
"version": "1.2.1",
"author": {
"name": "Mike Mayer",
"url": "https://github.com/EngineerWithAI"
},
"homepage": "https://github.com/EngineerWithAI/engineerwith-agents",
"repository": "https://github.com/EngineerWithAI/engineerwith-agents",
"license": "MIT",
"keywords": [
"data-engineering",
"etl",
"data-pipeline",
"data-warehouse",
"batch-processing"
],
"category": "data",
"strict": false,
"commands": [
"./commands/data-driven-feature.md",
"./commands/data-pipeline.md"
],
"agents": [
"./agents/data-engineer.md",
"./agents/backend-architect.md"
],
"skills": [
"./skills/dbt-transformation-patterns",
"./skills/airflow-dag-patterns",
"./skills/spark-optimization",
"./skills/data-quality-frameworks"
]
}What it installs
The manifest is a name and a version. 4 skills travel with it, and installing the plugin installs all of them — 163 tokens a session between them. Each is measured on its own page, and each can be installed alone.
What ships with it
1 file beside marketplace.json#data-engineering in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 36 lines scan A a2265d4f6a57
data-engineering is a plugin published in the GitHub repository EngineerWithAI/engineerwith-agents (4 stars, last pushed 9mo ago), licensed MIT. Its token cost is not measured: this kind of file is read by the harness, not the model. 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 2026-09-03.
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