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-cost-optimizergit 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-cost-optimizer)<a href="https://agentmods.dev/skills/vellum-ai/vellum-assistant/llm-cost-optimizer"><img src="https://agentmods.dev/badge/skills/vellum-ai/vellum-assistant/llm-cost-optimizer/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-cost-optimizer"><img src="https://agentmods.dev/badge/skills/vellum-ai/vellum-assistant/llm-cost-optimizer.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 | $0.00060 | $0.03469 |
| Opus 5 | $0.00030 | $0.01734 |
| Sonnet 5 | $0.00012 | $0.00694 |
| Haiku 4.5 | $0.00006 | $0.00347 |
Grade A, and why
llm-cost-optimizer 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 — 243 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
This skill walks through analyzing and reducing LLM spend on a Vellum assistant. There are three layers:
- Provider connections — named auth configs (e.g.
anthropic-managed,my-personal-key) - Model profiles — named presets (provider + model + effort + thinking + contextWindow). Four managed defaults, with UI labels. Note that the keys do not track the labels: read the key, not the name, when pinning a call site.
balanced→ Balanced (the general agent-loop profile)quality-optimized→ Quality (the expensive escalation profile)cost-optimized→ Budget (the cheap utility/background profile, and the one to pin for spend reduction)latency-optimized→ Fast (the low time-to-first-token profile, used by live voice; faster but not cheaper than Budget)
- Call-site profile pins (
llm.callSites.<id>.profile) — optional per-task overrides of the shipped defaults.
The concrete model behind each managed profile depends on the install: platform-managed installs and BYOK installs resolve different providers/models, and the catalog changes over time. Never assume which model a profile maps to — read assistant config get llm.profiles and the usage breakdown by model to see what actually ran.
How model selection works — read this before diagnosing
Every LLM call resolves exactly one winning profile through a strict first-usable-wins chain. Profiles never merge with each other:
- Per-conversation / per-run override — the user's
/modelpick, an openassistant inference session, or a schedule's pinned profile llm.activeProfile— applies tomainAgent(the chat loop) only; it IS the user's chat-model selection and outranks anyllm.callSites.mainAgentpinllm.callSites.<site>.profile— explicit per-site pin- The call site's shipped default intent, resolved through
llm.defaultProvider balancedintent (final anchor)
A rung only wins if its profile exists, is enabled, and carries its own provider + model; otherwise resolution silently falls to the next rung.
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 · 243 lines · 60 tokens per session scan A e57dc0405c50
llm-cost-optimizer is a skill published in the GitHub repository vellum-ai/vellum-assistant (1,234 stars, last pushed today), licensed MIT. It adds 60 tokens to every session and 3,469 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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Applies the fixes recommended by /lictor-security-check, with the user's explicit permission for each change. Walks through findings one at a time, shows the proposed change, gets approval, applies, runs tests if available, and moves on. Some fixes (rotating leaked credentials) require the user to act outside Claude …
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