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 inbox-cleanupgit 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/inbox-cleanup)<a href="https://agentmods.dev/skills/vellum-ai/vellum-assistant/inbox-cleanup"><img src="https://agentmods.dev/badge/skills/vellum-ai/vellum-assistant/inbox-cleanup/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/inbox-cleanup"><img src="https://agentmods.dev/badge/skills/vellum-ai/vellum-assistant/inbox-cleanup.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 229 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00049 | $0.03348 |
| Opus 5 | $0.00024 | $0.01674 |
| Sonnet 5 | $0.00010 | $0.00670 |
| Haiku 4.5 | $0.00005 | $0.00335 |
Grade A, and why
inbox-cleanup 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 12d 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 — 277 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Inbox Cleanup Skill
A playbook for large-scale email inbox cleanup. The core insight: sender-based scans are low-recall. Subject/body pattern queries catch 25x more archivable email. This skill is a multi-pass pipeline built around that insight.
Works with any connected email provider. Adapt query syntax to whatever the provider supports — the strategy (what to search for, how to decide what to archive) is universal.
Gmail is a required integration. It's declared via
includes: ["gmail"]in the frontmatter so it loads synchronously on activation, not lazily after the preferences form. Load/confirm the Gmail integration the moment this skill activates — before Phase 1 — so a missing or unauthorized connection surfaces up front rather than mid-cleanup.
Phase 1: Preference Capture
Do this before touching anything. Ask the user:
1. Aggressiveness level
- Conservative — newsletters with unsubscribe headers + obvious spam only
- Standard — above + cold outreach heuristics (subject patterns, unknown senders)
- Aggressive — above + anything from senders with no prior thread history
2. Age threshold Archive everything older than X days? Common choices: 30 / 60 / 90 days. Or no age filter.
First-run scope: On first invocation, scope to last 30 days or top 3 noise patterns, whichever surfaces faster. Show result, offer to expand. Prove the approach on a fast, visible slice before draining the whole backlog.
3. VIP senders to protect Ask: "Are there any senders that might look like cold outreach but you actually care about? Think: specific individuals at investors, advisors, your lawyer, accountant, recruiters you're actively working with."
Build an explicit keep list. Do not archive anything matching it, ever, regardless of aggressiveness.
4. Categories to confirm before archiving These need a sample + explicit approval before bulk action:
- Financial/billing alerts
- Legal/contracts
- Account suspension notices
- Government/regulatory
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.
- 12d ago First seen · 277 lines · 49 tokens per session scan A 2cf0937f1b41
inbox-cleanup is a skill published in the GitHub repository vellum-ai/vellum-assistant (1,225 stars, last pushed yesterday), licensed MIT. It adds 49 tokens to every session and 3,348 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
meeting-notes
Use when the user asks to capture, structure, or summarize meeting notes / call notes / 1:1 discussion / standup notes. Produces a standard template with attendees, agenda, decisions, action items (owner + deadline), and open questions.
skill-factory
A workflow that examines completed session work and turns reusable patterns into Claude Code skills.
ha-settings
Manage Hope Agent application settings through conversation. Use when the user wants to view or change any app configuration: theme, language, enhanced focus indicators, proxy, temperature, notifications, tool timeout, context compaction, automatic session titles, web search, GitHub issue reporting, memory, embedding…
meeting-scheduler
Schedule a small meeting end-to-end: resolve attendee emails, check the owner's calendar for the slot, dedup-check, then create + email the Google Calendar invite. The mechanical core only — cross-person availability negotiation stays interactive.
relay
Write a handoff/continuity note for the NEXT Sutando session. Captures what was just in flight, what to check first, what might go wrong, and implicit context the structured snapshot doesn't carry. Drained into session-state.md by src/session-handoff.sh.
x-twitter
Post to X via a signed-in browser session (live method — no API keys); API v2 path for search/read/engagement.