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 chat-complex-documentsgit 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/chat-complex-documents)<a href="https://agentmods.dev/skills/vellum-ai/vellum-assistant/chat-complex-documents"><img src="https://agentmods.dev/badge/skills/vellum-ai/vellum-assistant/chat-complex-documents/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/chat-complex-documents"><img src="https://agentmods.dev/badge/skills/vellum-ai/vellum-assistant/chat-complex-documents.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 | $0.00090 | $0.01311 |
| Opus 5 | $0.00045 | $0.00656 |
| Sonnet 5 | $0.00018 | $0.00262 |
| Haiku 4.5 | $0.00009 | $0.00131 |
Grade A, and why
chat-complex-documents 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 — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
What this does (in plain terms)
You have documents — contracts, reports, slide decks, scanned PDFs, spreadsheets, emails, images — and you want the assistant to reliably get the content out of them and make it usable: searchable, answerable, summarizable, or ready to feed a knowledge base or another tool.
Unstructured Transform does exactly that. Hand it files and it turns them into clean, structured, AI-ready data across 60+ formats with one pipeline — no custom parsing or OCR to build and maintain. See the Transform overview.
Under the hood it:
- Partitions each document into structured elements (titles, paragraphs, tables, lists), adjusting extraction per page for accuracy and cost.
- Enriches the result with metadata, table and image descriptions, and entity recognition.
The result is structured JSON that's ready for search, Q&A, summarization, and agents.
Why people use it
- Make documents searchable and answerable. Transform extracts clean, structured text and tables from your files so the assistant can search them and answer questions grounded in the source.
- Handle messy, varied files. One integration covers 60+ formats (scanned PDFs, tables, images, Office files) instead of a per-format parser.
When to use
USE THIS SKILL WHEN:
- The user wants to extract or structure content from documents (PDF, Office, images, emails, scanned files).
- The user wants documents made searchable or ready for RAG / Q&A / a knowledge base.
- The user asks to connect Unstructured or Unstructured Transform.
- A Transform tool returns an auth error → re-run the
authstep below.
Prerequisites
- An Unstructured account to sign in with during the OAuth step (a free tier is available). See pricing.
- Nothing to install locally — Transform is a hosted MCP server.
Setup
Step 1 — Detect your environment
Determine which command tool to use for every command in this skill:
What ships with it
1 file 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.
- 9d ago First seen · 113 lines · 90 tokens per session scan A fe3b6125cf2a
chat-complex-documents is a skill published in the GitHub repository vellum-ai/vellum-assistant (1,204 stars, last pushed yesterday), licensed MIT. It adds 90 tokens to every session and 1,311 once invoked, about $0.0005 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.
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