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/jpoutrin/product-forgeWrote 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/jpoutrin/product-forge/devops-data)<a href="https://agentmods.dev/plugins/jpoutrin/product-forge/devops-data"><img src="https://agentmods.dev/badge/plugins/jpoutrin/product-forge/devops-data/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/jpoutrin/product-forge/devops-data"><img src="https://agentmods.dev/badge/plugins/jpoutrin/product-forge/devops-data.svg" alt="Reviewed on agentmods" width="80" height="20"></a>Grade A, and why
devops-data 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": "devops-data",
"version": "1.0.0",
"description": "DevOps and Data Engineering with AWS, GCP, Ansible, dbt, and SQLMesh expertise",
"author": {
"name": "jpoutrin",
"email": "[email protected]"
},
"agents": [
"./agents/cto-architect.md",
"./agents/data-engineering-expert.md",
"./agents/devops-expert.md",
"./agents/mcp-expert.md"
]
}
What it installs
The manifest is a name and a version. 17 skills, 4 agents travel with it, and installing the plugin installs all of them — 568 tokens a session between them. Each is measured on its own page, and each can be installed alone.
- Skill mcp-architecture A 39 tokens
- Skill dependency-alignment A 34 tokens
- Skill rfc-specification A 40 tokens
- Skill technical-specification A 42 tokens
- Skill portman A 41 tokens
- Skill rfc-status A 13 tokens
- Skill list-rfcs A 0 tokens
- Skill tech-spec-status A 13 tokens
- Skill create-tech-spec A 12 tokens
- Skill list-tech-specs A 0 tokens
- Skill ansible A 30 tokens
- Skill create-rfc A 15 tokens
- Skill aws-cloud A 40 tokens
- Skill dbt A 35 tokens
- Skill gcp-cloud A 35 tokens
- Skill sqlmesh A 35 tokens
- Skill direnv B 49 tokens
- Agent cto-architect A 21 tokens
- Agent mcp-expert A 29 tokens
- Agent data-engineering-expert B 24 tokens
- Agent devops-expert C 21 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.
- 1mo ago First seen · 16 lines scan A 968a32494788
devops-data is a plugin published in the GitHub repository jpoutrin/product-forge (16 stars, last pushed 7mo 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 2026-08-30.
Other plugins, from other repositories
ml-training
Machine-learning training and benchmarking on rented cloud GPUs: prebaked RunPod images that boot in seconds instead of minutes, pod lifecycle scripts, GPU selection and pricing ladders, and the provisioning pitfall catalog.
local-ai
From nothing to a working AI setup you own. Scans the machine for models that fit, installs and wires up the right app with consent at every download, shares it over a private URL, and rents a cloud GPU when a model is too big. Needs shell access.
databricks
Databricks skills for the CLI, Apps, Lakebase, Model Serving, Lakeflow Jobs, Spark Declarative Pipelines, Declarative Automation Bundles (DABs), and classic-to-serverless migration.
orchestra
Skills for working with Orchestra data pipelines — diagnose and fix failed runs, triage with a merge gate, author pipeline YAML, set up dbt Slim CI, configure dbt state-aware orchestration (source freshness and buildafter), run Snowflake data-quality tests, review a workspace against best practices, and merge duplicat.
flyte
Work with Flyte 2. Deploy Flyte on AWS or kind, author and run workflows, apps, agents, data, and ML workloads with the SDK, and operate your cluster through bundled MCP servers.
adf-master
Complete Azure Data Factory expertise for pipeline JSON development, validation, and optimization (2025-2026). This plugin should be used for ADF pipeline creation, activity configuration, linked services, datasets, triggers, expressions, CI/CD, Microsoft Fabric integration, Databricks orchestration, ML pipeline…