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 agentsope/SkillAlchemy --skill agentsop-llm-engine-selectiongit clone --depth 1 https://github.com/agentsope/SkillAlchemyWrote 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/agentsope/skillalchemy/agentsop-llm-engine-selection)<a href="https://agentmods.dev/skills/agentsope/skillalchemy/agentsop-llm-engine-selection"><img src="https://agentmods.dev/badge/skills/agentsope/skillalchemy/agentsop-llm-engine-selection/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/agentsope/skillalchemy/agentsop-llm-engine-selection"><img src="https://agentmods.dev/badge/skills/agentsope/skillalchemy/agentsop-llm-engine-selection.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00086 | $0.06819 |
| Opus 5.5 | $0.00034 | $0.02728 |
| Sonnet 5.5 | $0.00017 | $0.01364 |
| Haiku 4.5 | $0.00009 | $0.00682 |
Grade A, and why
agentsop-llm-engine-selection 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.
How it starts
The opening of the file, as written. The whole thing — 377 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LLM Engine Selection SOP
State-of-the-art warning. This skill is dated May 2026. The inference-engine landscape moves in 3–6 month cycles (TGI exited maintenance into deprecation in late 2025; SGLang's RadixAttention regressed vLLM's lead in 2024; TensorRT-LLM dropped its proprietary-engine requirement in 2025; Ollama added concurrent-request support mid-2025). Re-verify before betting a quarter of eng budget on any choice below.
1. 何时激活 (When to activate)
Activate this skill any time a coder-agent must:
- Pick a serving stack for a new project (production hosting / batch / edge / dev laptop / multi-tenant SaaS / structured-output service).
- Defend an existing stack against a "let's switch to X" pressure.
- Migrate: justify or block a swap (e.g. TGI → vLLM, Ollama → vLLM, vLLM → TensorRT-LLM).
- Mix: design a multi-tier deployment (e.g. premium tier on TensorRT-LLM, free tier on vLLM-AWQ, dev on Ollama).
- Audit a recommendation that smells like benchmark-cherry-picking ("X is 5× faster").
Do not activate for:
- Tuning a single chosen engine — defer to the dedicated skill (
vllm,sglang,tensorrt-llm,llama-cpp). - Training/fine-tuning runtime selection — different problem class (
accelerate,deepspeed,axolotl). - Hosted-API procurement (OpenAI / Anthropic / Bedrock) — engine choice doesn't apply.
2. 核心心智模型 (Core Mental Model)
Engine choice is a function of (hardware × workload × constraint), not "which is fastest".
There is no global ranking. Every "X beats Y by N%" headline holds only inside an unstated (hardware, batch size, ISL/OSL, model, quantization, concurrency) tuple. Change any axis and the ranking flips.
2.1 The four-axis decision space
- Hardware axis — NVIDIA H100/A100 (NVLink) ≠ NVIDIA L40S/RTX (PCIe-only) ≠ AMD MI300 ≠ Apple Silicon ≠ CPU-only. The interconnect topology matters as much as raw FLOPS [spheron.network 2026]. PCIe-only tensor parallelism collapses; NVLink rescues it.
- Workload axis — production multi-user (throughput) vs latency-bound single-stream vs offline batch vs edge single-user vs structured-output service vs multi-LoRA SaaS. Each has a different winner.
- Constraint axis — license (Apache vs proprietary), vendor lock-in tolerance, engineering budget (1 day vs 2 weeks setup), commercial-use clauses, on-prem vs cloud, P50 vs P99 SLA.
- Maturity axis — the engine's coverage of YOUR model family. A 2025-launched MoE may run on vLLM day-1 but need a 3-month wait for TensorRT-LLM, and may never get a stable GGUF.
What ships with it
4 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.
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 · 377 lines · 86 tokens per session scan A 4b7866571be9
agentsop-llm-engine-selection is a skill published in the GitHub repository agentsope/SkillAlchemy (429 stars, last pushed 7d ago), licensed MIT. It adds 86 tokens to every session and 6,819 once invoked, about $0.0003 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.
Other skills, from other repositories
spark-environment-setup
Set up a working ML training/inference environment on NVIDIA DGX Spark (GB10, aarch64, CUDA 13). Use when installing PyTorch/Unsloth/TRL/vLLM on DGX Spark, hitting libcudart or wheel-ABI errors on aarch64, or choosing between NGC containers and bare pip installs.
spark-training-gotchas
Preflight and diagnose the ten known failure modes for ML training on NVIDIA DGX Spark. Use when a training run on DGX Spark fails to start, OOMs below the 128GB limit, slows down mid-run, or before any multi-hour training job on GB10.
llama-cpp
Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.
amc-run-rtsp-calibration
Calibrate a new dataset from live RTSP camera streams via the AutoMagicCalib REST API. Use when the user provides RTSP URLs or asks to calibrate live cameras; VIOS records clips, AMC ingests them, then runs calibration.
deepstream-import-vision-model
Use this skill to bring a supported object-detection vision model from HuggingFace or NVIDIA NGC into an NVIDIA DeepStream pipeline with end-to-end automation: ONNX download, SafeTensors export, TRT engine build, custom nvinfer bbox parser, multi-stream benchmark, and PDF report. Object detection models only.
jetson-inference-mem-tune
Pick the serving stack and per-runtime memory flags (vLLM, SGLang, llama.cpp, TensorRT Edge-LLM) for an LLM/VLM workload on any NVIDIA Jetson.