Vellum Assistant is a personal AI assistant that remembers information about users, learns their preferences, and takes actions across connected apps. It is intended for people who want an assistant that can manage conversations, unfinished work, and proactive notifications over time. The catalogue skills, hooks, instruction, and setting configure or extend how the assistant works.
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 vellum-ai/vellum-assistant --skill llm-provider-setupgit clone --depth 1 https://github.com/vellum-ai/vellum-assistantWrote 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/vellum-ai/vellum-assistant/llm-provider-setup)<a href="https://agentmods.dev/skills/vellum-ai/vellum-assistant/llm-provider-setup"><img src="https://agentmods.dev/badge/skills/vellum-ai/vellum-assistant/llm-provider-setup/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/vellum-ai/vellum-assistant/llm-provider-setup"><img src="https://agentmods.dev/badge/skills/vellum-ai/vellum-assistant/llm-provider-setup.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 | $0.00056 | $0.01759 |
| Opus 5 | $0.00028 | $0.00879 |
| Sonnet 5 | $0.00011 | $0.00352 |
| Haiku 4.5 | $0.00006 | $0.00176 |
Grade A, and why
llm-provider-setup 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.
How it starts
The opening of the file, as written. The whole thing — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
This skill is the canonical procedure for adding a new LLM provider, model, or inference profile to a Vellum assistant. Follow the steps in order — each step's output feeds the next, and the final live-call verification is mandatory. Never skip ahead by writing raw config JSON.
Run the steps strictly sequentially — one command per step, and read its output before running the next. Never batch create → verify → activate into a single turn: creation can fail validation, verification can fail on a wrong model id or missing credential, and activation must not happen until verification passed. Each step's output is the gate for the next one.
Step 0 — Check what's already available (avoid collecting keys unnecessarily)
Managed (platform-credentialed) routing may already cover the user's need — no API key required:
assistant inference providers list # provider entries; `vellum` is the platform-managed route
assistant inference providers default # default provider + availability status
assistant inference profiles list # effective profiles: managed + user, with availability
Managed first. If the user is signed in to Vellum and the model they asked for is served by the managed route, build the profile on it — --provider vellum --model <model-id>, no --connection, no credential, nothing to prompt for. Skip Steps 1 and 2 entirely and go to Step 3; there is no key to collect. If the model turns out not to be managed-routable, profile creation says so explicitly (Step 4) — only then fall back to key collection.
Collect an API key only when there is genuinely no managed option: the user is not signed in to Vellum, the model is not served by the managed route, or the user explicitly wants to use their own key.
Step 1 — Reuse an existing key, or securely collect a new one
Before prompting the user for anything, check whether a suitable key is already stored:
assistant credentials list
If a credential for the target provider exists, reuse it — reference it by vault path in Step 2 and skip the prompt. Only collect a new key when none exists (or the user explicitly wants to replace it).
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 · 141 lines · 56 tokens per session scan A 2b48a880ca56
llm-provider-setup is a skill published in the GitHub repository vellum-ai/vellum-assistant (1,234 stars, last pushed today), licensed MIT. It adds 56 tokens to every session and 1,759 once invoked, about $0.0003 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 2026-09-03.
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Takes any security finding, error message, or jargon-heavy security advice and explains it in plain English. Use this when someone is confused by what /lictor-security-check found, or when they got a security warning from another tool and don't understand it.
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