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.
npx skills add RHEcosystemAppEng/agentic-plugins --skill model-deploygit clone --depth 1 https://github.com/RHEcosystemAppEng/agentic-pluginsWrote 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/skills/rhecosystemappeng/agentic-plugins/model-deploy)<a href="https://agentmods.dev/skills/rhecosystemappeng/agentic-plugins/model-deploy"><img src="https://agentmods.dev/badge/skills/rhecosystemappeng/agentic-plugins/model-deploy/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/skills/rhecosystemappeng/agentic-plugins/model-deploy"><img src="https://agentmods.dev/badge/skills/rhecosystemappeng/agentic-plugins/model-deploy.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 | $0.00135 | $0.06043 |
| Opus 5.5 | $0.00054 | $0.02417 |
| Sonnet 5.5 | $0.00027 | $0.01209 |
| Haiku 4.5 | $0.00014 | $0.00604 |
Grade B, and why
model-deploy scanned grade B with 2 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.
Sends data to an external URLmediumData exfiltration
A POST to an outside endpoint may be telemetry or may be exfiltration; either way the mod talks to somewhere, and you should know where.
- **vLLM (OpenAI-compatible)**: `curl -X POST [endpoint]/v1/completions -H "Content-Type: application/json" -d '{"model":"[model-name]","prompt":"Hello","max_tokens":100}'` Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- **vLLM (OpenAI-compatible)**: `curl -X POST [endpoint]/v1/completions -H "Content-Type: application/json" -d '{"model":"[model-name]","prompt":"Hello","max_tokens":100}'` 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 ships with it
8 files beside SKILL.md 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.
- references/common-issues.md 33 B
- references/examples/model-deploy.md 4.0 KB
- references/known-model-profiles.md 4.5 KB
- references/live-doc-lookup.md 35 B
- references/model-deploy-preflight-checklist.md 3.6 KB
- references/openshift-fallback-templates.md 48 B
- references/skill-conventions.md 37 B
- references/supported-runtimes.md 61 B
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 Changed 1ec42da1702d
- 1mo ago First seen · 463 lines · 135 tokens per session scan B e8bf98b1c0aa
model-deploy is a skill published in the GitHub repository RHEcosystemAppEng/agentic-plugins (53 stars, last pushed 12d ago), with no licence file. It adds 135 tokens to every session and 6,043 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it B with 2 findings (sends data to an external url, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
tensorrt-llm
High-throughput LLM inference on NVIDIA GPUs.
agent-platform-tuning
Agent Platform Model Tuning. Use when you need to fine-tune open models or Gemini models using Agent Platform infrastructure. Don't use for model training outside Agent Platform, model deployment to endpoints (use agent-platform-deploy), or managing serving endpoints (use agent-platform-endpoint-management).
google-cloud-solution-guided-gke-ai-migration
Guides the migration of existing AI workloads (Cloud Run, Gemini API, Gemini Enterprise Agent Platform) to self-hosted GKE inference using gcloud and kubectl. Use when the user has an existing AI inference workload (on Cloud Run, the Gemini API, Gemini Enterprise Agent Platform, or a custom VM) and wants to move it to…
apigee-agent-gateway-governance
Stands up an Apigee API proxy that a Google Cloud Agent Gateway calls as an ext-proc (Service Extension) callout on an agent's LLM traffic, to meter token usage per model, extract caller identity, and apply Model Armor prompt screening or a semantic cache. Use when wiring Apigee in as an AI gateway behind an Agent…
modal
Modal is a serverless cloud platform for running Python on demand, including on-demand GPUs. Use when deploying or serving AI/ML models, running GPU-accelerated workloads (training, fine-tuning, inference), serving web endpoints, scheduling batch jobs, or scaling Python code to cloud containers with the Modal SDK.
gke-inference
Deploys and optimizes AI/ML inference workloads on GKE, using GPUs, TPUs, and model servers. Use when deploying GKE inference servers, configuring GKE GPU resources for inference, or deploying LLMs on GKE. Don't use for migrating existing AI workloads to GKE (use google-cloud-solution-guided-gke-ai-migration), GKE RAG…