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 system-storage-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/system-storage-cleanup)<a href="https://agentmods.dev/skills/vellum-ai/vellum-assistant/system-storage-cleanup"><img src="https://agentmods.dev/badge/skills/vellum-ai/vellum-assistant/system-storage-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/system-storage-cleanup"><img src="https://agentmods.dev/badge/skills/vellum-ai/vellum-assistant/system-storage-cleanup.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 | $0.00031 | $0.00990 |
| Opus 5 | $0.00015 | $0.00495 |
| Sonnet 5 | $0.00006 | $0.00198 |
| Haiku 4.5 | $0.00003 | $0.00099 |
Grade A, and why
system-storage-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 11d 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 — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are operating under a critical storage cleanup contract. Your only goal is to free enough storage for the assistant to resume normal work without damaging user data.
Cleanup Contract
Start by warning the user that storage is critically low and normal work is suspended until storage cleanup mode clears. Stay scoped to freeing storage until the disk-pressure lock clears or the guardian explicitly overrides it.
Prefer foreground inspection with available cleanup-safe tools before any mutation. Identify both the target volume that is actually full and the workspace path before proposing deletions. Do not work on unrelated tasks, refactors, installs, upgrades, or product changes while the storage lock is active.
Ask for explicit approval before deleting files, caches, logs, package caches, Docker artifacts, or any other data unless the user has already approved that exact action. Before asking, present each proposed deletion with:
- Exact path or artifact name.
- Estimated reclaimable size.
- Expected consequence, including whether it is regenerable or may remove user-visible history.
If the user approves a broad category, narrow it to exact paths or artifacts before deleting. If the user approves one exact path, do not treat that as approval for adjacent paths.
Never delete credentials, security material, workspace database files, config files, active profiler runs, migrations, skill source, app source, conversation records, memory graph nodes or segments, journal/, data/reflections/, PKB files, backups, or backup keys unless the user explicitly names that path and accepts the consequence.
Inspection Procedure
Use local/container-visible inspection first. Prefer df -h on the current workspace path and on VELLUM_WORKSPACE_DIR when that variable is available. In Docker/container mode, /workspace is the persistent volume and cleanup should normally focus there.
Use du one level at a time and sort by size to identify large directories before drilling deeper. Keep each pass readable and bounded to the volume or workspace that is actually full. Avoid whole-filesystem scans unless the target volume cannot be isolated.
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.
- 11d ago First seen · 75 lines · 31 tokens per session scan A d60ed6b3190a
system-storage-cleanup is a skill published in the GitHub repository vellum-ai/vellum-assistant (1,225 stars, last pushed yesterday), licensed MIT. It adds 31 tokens to every session and 990 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.