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/jeremylongshore/plugins-nixtlaWrote 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/jeremylongshore/plugins-nixtla/ai-ml-engineering-pack_20251019_161259)<a href="https://agentmods.dev/plugins/jeremylongshore/plugins-nixtla/ai-ml-engineering-pack_20251019_161259"><img src="https://agentmods.dev/badge/plugins/jeremylongshore/plugins-nixtla/ai-ml-engineering-pack_20251019_161259/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/jeremylongshore/plugins-nixtla/ai-ml-engineering-pack_20251019_161259"><img src="https://agentmods.dev/badge/plugins/jeremylongshore/plugins-nixtla/ai-ml-engineering-pack_20251019_161259.svg" alt="Reviewed on agentmods" width="80" height="20"></a>Grade A, and why
ai-ml-engineering-pack 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 9d 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.
The source is not reproduced here
A licence we could not identify
The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.
What it installs
The manifest is a name and a version. 4 commands, 8 agents travel with it, and installing the plugin installs all of them — 201 tokens a session between them. Each is measured on its own page, and each can be installed alone.
- Command llm-api-scaffold A 15 tokens
- Command prompt-template-gen A 13 tokens
- Command ai-monitoring-setup B 19 tokens
- Command rag-pipeline-gen B 18 tokens
- Agent ai-safety-expert A 22 tokens
- Agent llm-integration-expert A 20 tokens
- Agent prompt-optimizer A 16 tokens
- Agent rag-architect A 18 tokens
- Agent vector-db-expert A 16 tokens
- Agent model-selector A 16 tokens
- Agent prompt-architect A 14 tokens
- Agent prompt-injection-defender C 14 tokens
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.
- 9d ago First seen · 116 lines scan A b536fad6dd51
ai-ml-engineering-pack is a plugin published in the GitHub repository jeremylongshore/plugins-nixtla (11 stars, last pushed 3d ago), with no licence file. 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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