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 agentmods add skills/nvidia/nvcf/runnpx skills add NVIDIA/nvcf --skill rungit clone --depth 1 https://github.com/NVIDIA/nvcfWhat 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 | $0.00034 | $0.00255 |
| Opus 5 | $0.00017 | $0.00128 |
| Sonnet 5 | $0.00007 | $0.00051 |
| Haiku 4.5 | $0.00003 | $0.00026 |
Grade A, and why
run 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 2d 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
Running nvcf-ui
There is no local backend — the app either runs against a real cluster or uses MSW mocks.
Mocked mode (default for local dev)
VITE_MOCK=true task ui:dev
Starts Vite at http://localhost:5173 with MSW intercepting all API requests using the seeded in-memory store. No cluster required. Use this for UI development.
To layer a scenario on top of the baseline store:
VITE_MOCK=true VITE_SCENARIO=functions:empty-list task ui:dev
Multiple scenarios are comma-separated: VITE_SCENARIO=functions:empty-list,tasks:launching-task.
Against a real cluster
task ui:dev
No VITE_MOCK — requests proxy to the configured upstream. Requires cluster access. Check ui/vite.config.ts for the proxy target.
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.
- 2d ago First seen · 38 lines · 34 tokens per session scan A 96080dba4ab6
run is a skill published in the GitHub repository NVIDIA/nvcf (202 stars, last pushed 2d ago), licensed Apache-2.0. It adds 34 tokens to every session and 255 once invoked, about $0.0002 per session on Opus 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
vmware-privateai
Use this skill whenever the user needs the GPU / AI-infrastructure layer of VMware Private AI Foundation with NVIDIA (PAIF-N) on vSphere 9.x / VCF 9.1: inventory GPU hosts and physical GPU devices, see which VMs consume a vGPU and the profile each holds, read real-time GPU utilization, list the vGPU and DirectPath…
kubernetes-mesh-provisioner
Kubernetes Mesh Provisioner atomic skill. Stands up an RKE2 cluster (server + agents) with Cilium CNI and the NVIDIA GPU device plugin, the Kubernetes parallel of swarm-mesh-provisioner. Idempotent — re-runnable.
accelerated-computing-cudf
Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV/Parquet I/O, nullable semantics, and multi-GPU DataFrame workloads.
llm-torch-profiler-analysis
Unified LLM torch-profiler triage skill for sglang, vllm, TensorRT-LLM, and TokenSpeed. Use it to inspect an existing trace.json(.gz) or profile directory, or to drive live profiling against a running server when supported and return one three-table report with kernel, overlap-opportunity, and fuse-pattern tables.
cookbook-review-pr
Review a pull request against the SGLang Cookbook (docs/, Mintlify) contribution checklist — the config-driven format (per-model config + benchmarks JSX consumed by the shared deployment.jsx / playground.jsx engines). Run with /cookbook-review-pr .
speculative-naming
Naming conventions for SGLang speculative decoding identifiers. Use when adding, renaming, or reviewing identifiers in speculative decoding code — anything under python/sglang/srt/speculative/, related attention backends, scheduler accumulators, IPC fields, observability metrics, or CLI flags.