Diagnose NVIDIA driver installation failures on DeepOps-managed nodes — nvidia-smi errors, "No devices were found", DKMS build failures, or GPU pods crash-looping. Use before reinstalling anything.
npx skills add https://github.com/NVIDIA/deepops --skill diagnose-driver-install
Work through these in order; most "driver failures" are one of the first
three and need no reinstall.
On DeepOps Slurm nodes, GPUs are hidden from ordinary SSH sessions by
design. Bare nvidia-smi over SSH reporting No devices were found on an
otherwise healthy node is expected.
srun --gpus=1 nvidia-smi # the authoritative test on Slurm nodes
If the srun job sees the GPU, the driver is fine. Stop here.
lspci | grep -i nvidia
No output → not a driver problem. The GPU is absent, unseated, or bound by
VFIO passthrough or platform firmware; escalate to hardware support before
touching software.
nvidia-smi: No devices were found immediately after a clean install is the
classic symptom of the wrong module flavor for the GPU generation:
supported — DeepOps default nvidia_driver_ubuntu_use_open_kernel_modules: true
is correct.
set nvidia_driver_ubuntu_use_open_kernel_modules: false in
config/group_vars/all.yml and rerun the driver play.
Check what is loaded: modinfo nvidia | grep -i license (open modules say
MIT/GPL, proprietary says NVIDIA).
dkms status # driver module state per kernel
dmesg | grep -iE 'nvidia|nvrm' | tail -20
lsmod | grep nvidia
(linux-headers-$(uname -r)) or a kernel updated after the driver
install. Install headers or reboot into the matching kernel, then rerun
the driver play.
nvidia-smi fails → check dmesg for RmInitAdapter orfallen-off-the-bus errors; these are hardware/firmware territory.
After correcting configuration, converge with the playbook rather than
manual package surgery, then validate:
ansible-playbook -l <host> playbooks/nvidia-software/nvidia-driver.yml
python3 scripts/validation/validate_slurm.py --json # or validate_k8s.py
Reruns are idempotent; a healthy converged rerun reports changed=0.
GPU Operator crash-looping driver pods: `kubectl logs -n <gpu-operator
namespace> <driver-pod>` usually names the same root causes — missing
headers, wrong module flavor, or a node reboot needed. Fix via the operator
values or node state, not by installing drivers by hand on the host (the
operator owns the driver on Kubernetes nodes unless DeepOps was configured
for host drivers).
Integration with protocols.io API for managing scientific protocols. This skill should be used when working with protocols.io to search, create, update, or publish protocols; manage protocol steps and materials; handle discussions and comments; organize workspaces; upload and manage files; or integrate protocols.io functionality into workflows. Applicable for protocol discovery, collaborative protocol development, experiment tracking, lab protocol management, and scientific documentation.
Analyzes job descriptions and generates tailored resumes that highlight relevant experience, skills, and achievements to maximize interview chances
Generate Excalidraw diagrams from natural language descriptions. Use when asked to "create a diagram", "make a flowchart", "visualize a process", "draw a system architecture", "create a mind map", or "generate an Excalidraw file". Supports flowcharts, relationship diagrams, mind maps, and system architecture diagrams. Outputs .excalidraw JSON files that can be opened directly in Excalidraw.
Build and distribute Expo development clients locally or via TestFlight
Use when you have a written implementation plan to execute in a separate session with review checkpoints
Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
Benchling R&D platform integration. Access registry (DNA, proteins), inventory, ELN entries, workflows via API, build Benchling Apps, query Data Warehouse, for lab data management automation.
Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use bioservices.
Take nvidia/diagnose-driver-install from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.