jjziets/vasttools/vast-host-install

Claude Code and agent skill by jjziets/vasttools

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Prepare, install and commission NVIDIA GPU hosts for Vast.ai using host-specific safety gates, XFS storage checks, cgroup/runtime testing and optional VM qualification. Use for Vast host installation or readiness work, not renter-instance setup.

Install

npx vskill@latest install jjziets/vasttools/vast-host-install

SKILL.md

Vast.ai host installation

Produce a reproducible host build with observed acceptance results. Support preparation, authorized installation, and commissioning. Begin with read-only discovery when the target or change scope is unclear. A request to write or publish this runbook is not permission to operate a host.

Opening installation questions

When preparing or executing an actual host installation, ask the user at the beginning for the public address and inclusive start–end TCP/UDP port range allocated to this host, unless they already supplied those exact values for this target. Confirm whether it is directly routed or behind NAT and who can configure the relevant firewall/router. Never pick a sample range or assume discovered open ports belong to this host. If no allocation exists, help propose one after checking the environment and wait for the user's selection before configuring it. Read-only inventory may continue meanwhile. Ask for storage preferences early too, then confirm the concrete disk layout after discovery. Keep these answers in the private host record, not the public skill.

Read the relevant procedure

  • For installation or rebuild: install.md.
  • For every container commissioning: cgroups.md. Linux hierarchy, Docker cgroup driver and GPU injection mode are separate facts.
  • Before installer/runtime changes and for every commissioning: privileges.md. Preserve workload identities and tenant isolation; administrative Docker access is not rental permission.
  • For DGX/HGX or other NVSwitch platforms, and for the final Docker workload on every host: fabric-and-workload.md. Install the required matching fabric stack and prove real GPU/NCCL work completes.
  • For VM requests, VM warnings, or deciding how to prevent automatic VM enablement: vms.md.
  • Start a private working record from host-record.md. Keep identifiers and evidence outside the public skill tree.
  • Before reusing a historical recipe: evidence.md. Distinguish observed success, bounded failure and untested guidance.

Operating boundaries

  1. Identify one physical target by live system/chassis identity, GPU inventory and trusted management access. Never transfer another machine's disk names, serial allowlist, keys, IPs, installer token, approval or machine ID.
  2. Read existing authorization first. Continue within it without repeated approval prompts. Ask only for missing consequential choices or actions: exact erasure/layout, downtime, firmware/boot changes, VM enablement, or marketplace exposure. A broad install request does not settle unidentified data destruction or commercial terms.
  3. Before any disruptive action, reconcile provider rentals/volumes, local running and stopped containers, VM inventory, GPU processes, and dependencies such as boot-media servers. Unlisted does not mean idle; idle does not mean data may be erased. Unknown occupancy is HOLD.
  4. Preserve existing working storage/driver configuration unless the requested change needs replacement. Resolve destructive disk targets by freshly verified serial/WWN and complete storage ancestry, never /dev/nvme0n1 position.

Before storage changes, present the proposed layout from live inventory and confirm the user's preferred layout: which disks serve OS/boot and Docker/containerd data, RAID or independent disks, partition sizes, usable capacity, failure tolerance and preserved data/devices. Follow the layout gate in install.md. Reuse an explicit existing confirmation only if the exact target, layout and scope still match; otherwise wait for the user's choice. Erasure permission is not layout approval. Never assume RAID0, use every disk or silently substitute a different layout.

  1. Review downloaded privileged code before execution, including downstream scripts and automatic tests. Record origin, timestamp, hash, selected options and material side effects. A hash records the reviewed bytes; it does not authenticate them by itself. Re-review changed bytes.
  2. Use the fresh setup credential from the authenticated host setup page. Do not substitute a remembered host/client API key. Never put secrets into chat, public files, command transcripts, debug traces or evidence. If the runtime cannot securely deliver a credential, let the operator perform that step and verify its outcome.
  3. Registration, self-testing, VM auto-tests and commercial listing can have separate side effects. Resolve automatic listing/VM behavior before installation. Do not assume --no-daemon or --no-libvirt suppresses every later action.
  4. Default new builds to the container profile unless VM support was requested. Record and verify the chosen VM policy; leaving a helper pending is not equivalent to keeping VMs off. Preserve an existing deployment's mode during assessment.
  5. Do not apply historical NVML scripts, switch to cgroup v1, disable resource controls, change IOMMU/ACS, or overclock as generic installation steps. Diagnose the exact observed failure first.
  6. Never elevate an unprivileged rental or diagnostic workload to repair installation or GPU access. Preserve its intended UID/GID, capabilities, namespaces, security profiles and device allocation. No --privileged, forced root user, host-root/runtime-socket mounts, expanded daemon access or isolation bypasses. Do not convert a rootless daemon to rootful as a compatibility shortcut. Hold incompatible paths rather than weaken the boundary; see the privilege reference for verification.

Progress and stop conditions

Use these checkpoints, retaining timestamped private evidence for each:

CheckpointRequired evidence
IDENTIFIEDHost identity, inventory, trusted access, retained-data decision and authorized scope
PREPAREDBoot/recovery path, selected OS/driver, healthy GPU inventory, user-confirmed disk layout, approved XFS mount, user-specified port allocation configured and externally tested
REGISTEREDReviewed installer outcome, correct account/machine, current provider state and no unexpected background actions
CONTAINER-QUALIFIEDPreserved workload identity/privilege boundary, storage quota, GPU compute/isolation, CPU/memory limits, cgroup regression, matching healthy fabric stack where required, final post-reboot Docker compute/NCCL workload, and network proof
VM-QUALIFIEDSeparate requested VM profile passes assignment, guest, isolation and host-recovery tests
RELEASE-READYRequired provider self-test, cleanup, monitoring and commercial terms recorded; listing state independently verified

Record each gate as PASS, FAIL, HOLD, NOT TESTED, or NOT APPLICABLE with a reason. Missing evidence is not PASS. VM-QUALIFIED is not required for a container-only release. A self-test pass is not proof of a verification badge or every network port.

After an unexpected failure, stop the current mutation sequence, preserve restricted diagnostics and re-observe state. Do not blindly rerun an installer, destructive action, VM helper, reboot or test. A retry needs a diagnosed cause, current prerequisites and authorization still covering its side effects. Recover only the resources and settings owned by this run.

Handoff

Return the exact profile and versions, completed stages, evidence references, unresolved gates, current listing/VM/workload state, changes made, recovery status and next permitted step. Distinguish configuration written, behavior observed after reboot, and behavior not tested. Do not claim the machine is ready merely because installation exited zero.

Community

Join our Discord, a small GPU infrastructure community where we discuss this skill and GPU hosting. Participation is optional; this link does not authorize an agent to post host records, diagnostics or credentials.

Security

  • Security scanPassed, 95 of 100Pattern checks on the skill's files.
  • Model reviewPassed

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

Source

Popularity

Repository stars
162
Repository forks
29
7-day trend score
30

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